[go: up one dir, main page]

WO2021057848A1 - Procédé d'entraînement de réseau, procédé de traitement d'image, réseau, dispositif terminal et support - Google Patents

Procédé d'entraînement de réseau, procédé de traitement d'image, réseau, dispositif terminal et support Download PDF

Info

Publication number
WO2021057848A1
WO2021057848A1 PCT/CN2020/117470 CN2020117470W WO2021057848A1 WO 2021057848 A1 WO2021057848 A1 WO 2021057848A1 CN 2020117470 W CN2020117470 W CN 2020117470W WO 2021057848 A1 WO2021057848 A1 WO 2021057848A1
Authority
WO
WIPO (PCT)
Prior art keywords
sample
image
mask
edge
neural network
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2020/117470
Other languages
English (en)
Chinese (zh)
Inventor
刘钰安
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong Oppo Mobile Telecommunications Corp Ltd
Original Assignee
Guangdong Oppo Mobile Telecommunications Corp Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong Oppo Mobile Telecommunications Corp Ltd filed Critical Guangdong Oppo Mobile Telecommunications Corp Ltd
Publication of WO2021057848A1 publication Critical patent/WO2021057848A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/12—Edge-based segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30196—Human being; Person
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • This application relates to the field of image processing technology, and in particular to an image segmentation network training method, image processing method, image segmentation network, terminal equipment, and computer-readable storage medium.
  • the current commonly used method is: use the trained image segmentation network to output a mask that represents the area where the target object (that is, the foreground, such as a portrait) is located, and then use the mask to segment the target object Come out, and then change the image background.
  • the mask output by the current image segmentation network cannot accurately represent the contour edge of the target object, so that the target object cannot be accurately segmented, and the effect of replacing the image background is poor. Therefore, how to enable the mask output by the image segmentation network to more accurately represent the contour edge of the target object is a technical problem that needs to be solved urgently.
  • the purpose of the embodiments of this application is to provide an image segmentation network training method, image processing method, image segmentation network, terminal equipment, and computer-readable storage medium, which can make the output of the trained image segmentation network to a certain extent
  • the mask can more accurately represent the contour edge of the target object.
  • an image segmentation network training method which includes steps S101-S105:
  • S101 Obtain each sample image containing a target object, a sample mask corresponding to each sample image, and sample edge information corresponding to each sample mask, where each sample mask is used to indicate the target in the corresponding sample image The image area where the object is located, each sample edge information is used to indicate the contour edge of the image area where the target object is indicated by the corresponding sample mask;
  • S102 For each sample image, input the sample image to an image segmentation network, and obtain a generation mask output by the image segmentation network for indicating the area where the target object in the sample image is located;
  • S103 For each generated mask, input the generated mask to the trained edge neural network to obtain generated edge information output by the edge neural network, and the generated edge information is used to indicate the target object indicated by the generated mask The contour edge of the area;
  • S104 Determine the loss function of the image segmentation network, where the loss function is used to measure the difference between the sample mask corresponding to each sample image and the generated mask, and the loss function is also used to measure each sample image Corresponding to the gap between the generated edge information and the sample edge information;
  • S105 Adjust various parameters of the image segmentation network, and then return to execute S102 until the loss function of the image segmentation network is less than a first preset threshold, thereby obtaining a trained image segmentation network.
  • an image processing method including:
  • the trained image segmentation network Obtains the image to be processed, and input the image to be processed into the trained image segmentation network to obtain the mask corresponding to the image to be processed, wherein the trained image segmentation network adopts a trained edge neural network After training, the trained edge neural network is used to output the contour edge of the area where the target object is indicated by the mask according to the input mask;
  • the target object contained in the image to be processed is segmented.
  • an image segmentation network is provided, and the image segmentation network is obtained by training using the training method described in the first aspect.
  • a terminal device including a memory, a processor, and a computer program that is stored in the memory and can run on the processor.
  • the processor executes the computer program, it implements the first aspect or the second aspect. The steps of the method described in the aspect.
  • a computer-readable storage medium stores a computer program, and when the above-mentioned computer program is executed by a processor, the steps of the method described in the first aspect or the second aspect are implemented.
  • a computer program product includes a computer program, and when the computer program is executed by one or more processors, the steps of the method described in the first aspect or the second aspect are implemented.
  • the trained edge neural network when training the image segmentation network, the trained edge neural network will be used to train the image segmentation network.
  • the trained edge neural network 001 is input to the edge neural network 001 according to the generated mask 002 indicated by the image area (pure) White area), output generated edge information 003, the edge information is used to indicate the location of the contour edge of the image area, the generated edge information 003 in Figure 1 is presented in the form of an image.
  • the training method provided by this application includes the following steps: First, for each sample, the sample image is input to the image segmentation network to obtain the generated mask output by the image segmentation network, and the generated mask is input to the training After the edge neural network, the generated edge information output by the edge neural network is obtained; secondly, the loss function of the image segmentation network is determined, and the loss function is positively correlated with the mask gap corresponding to each sample image (a sample image corresponds to The mask gap is the gap between the sample mask corresponding to the sample image and the generated mask), and the loss function and the edge gap corresponding to each sample image are also positively correlated (the edge gap corresponding to a sample image is the The difference between the sample edge information corresponding to the sample image and the generated edge information), and finally, adjust the various parameters of the image segmentation network until the loss function is less than the first preset threshold.
  • the above training method ensures that the generated mask output by the image segmentation network is close to the sample mask, it will further ensure that the contour edge of the target object represented in the generated mask output by the image segmentation network is more consistent with the actual contour edge. For approximation, therefore, the mask image output by the image segmentation network provided by this application can more accurately represent the contour edge of the target object.
  • Fig. 1 is a schematic diagram of the working principle of a trained edge neural network provided by the present application
  • FIG. 2 is a schematic diagram of a training method of an image segmentation network provided by Embodiment 1 of the present application;
  • FIG. 3 is a schematic diagram of a sample image, sample mask, and sample edge information provided in Embodiment 1 of the present application;
  • Embodiment 4 is a schematic structural diagram of an image segmentation network provided by Embodiment 1 of the present application.
  • FIG. 5 is a schematic diagram of the connection relationship between the image segmentation network provided in the first embodiment of the present application and the trained edge neural network;
  • FIG. 6 is a schematic diagram of the structure of the edge neural network provided in the first embodiment of the present application.
  • FIG. 7 is a schematic diagram of another image segmentation network training method provided in Embodiment 2 of the present application.
  • FIG. 8(a) is a schematic diagram of the training process of the edge segmentation network provided in the second embodiment of the present application.
  • Fig. 8(b) is a schematic diagram of the training process of the image segmentation network provided in the second embodiment of the present application.
  • FIG. 9 is a schematic diagram of the work flow of the image processing method provided in the third embodiment of the present application.
  • FIG. 10 is a schematic structural diagram of an image segmentation network training device provided in the fourth embodiment of the present application.
  • FIG. 11 is a schematic structural diagram of an image processing apparatus according to Embodiment 5 of the present application.
  • FIG. 12 is a schematic structural diagram of a terminal device according to Embodiment 6 of the present application.
  • the method provided in the embodiments of the present application may be applicable to terminal devices.
  • the terminal devices include, but are not limited to: smart phones, tablet computers, notebooks, desktop computers, cloud servers, and the like.
  • the term “if” can be interpreted as “when” or “once” or “in response to determination” or “in response to detection” depending on the context .
  • the phrase “if determined” or “if detected [described condition or event]” can be interpreted as meaning “once determined” or “in response to determination” or “once detected [described condition or event]” depending on the context ]” or “in response to detection of [condition or event described]”.
  • the training method includes:
  • step S101 each sample image containing the target object, the sample mask corresponding to each sample image, and the sample edge information corresponding to each sample mask are obtained, where each sample mask is used to indicate the corresponding sample In the image area where the target object is located, each sample edge information is used to indicate the contour edge of the image area where the target object is indicated by the corresponding sample mask.
  • a part of the sample images can be obtained from the data set first, and then the number of sample images used for training the image segmentation network can be expanded in the following ways: mirror inversion, scale scaling and/or scaling of the sample images obtained in advance Gamma changes, etc., so as to increase the number of sample images, so as to obtain each sample image described in step S101.
  • the sample mask described in this application is a binary image.
  • the method of obtaining the sample edge information corresponding to a certain sample mask in step S101 may be: performing an expansion operation on the sample mask to obtain a mask image after the expansion operation, and combining the mask image after the expansion operation with the By subtracting the sample mask, the edge information of the sample corresponding to the sample mask can be obtained.
  • the edge information of the sample obtained in this way is the same as the sample mask, which is a binary image.
  • an image 201 is a sample image containing a target object (ie, a portrait)
  • an image 202 may be a sample mask corresponding to the sample image 201
  • an image 203 may be sample edge information corresponding to the sample mask 201.
  • the above sample edge information is not necessarily a binary image, but can also be other information expression forms, as long as it can reflect the "contour edge of the image area where the target object is indicated by the sample mask". .
  • the above-mentioned target object may be any subject, such as a portrait, a dog, a cat, etc., and this application does not limit the category of the target object.
  • the image content contained in each sample image should be as different as possible.
  • the image content contained in sample image 1 can be a frontal portrait of Xiao Ming.
  • the image content contained in the sample image 2 may be a half-profile portrait of Xiaohong.
  • step S102 for each sample image, the sample image is input to the image segmentation network, and a generation mask output by the image segmentation network for indicating the area of the target object in the sample image is obtained.
  • an image segmentation network needs to be established in advance, and the image segmentation network is used to output a mask corresponding to the image (that is, to generate a mask) according to the input image.
  • the image segmentation network may be CNN (Convolutional Neural Networks, Convolutional Neural Network), or FPN (Feature Pyramid Networks, Feature Pyramid Network), and this application does not limit the specific network structure of the image segmentation network.
  • the image segmentation network using the FPN structure can be specifically referred to in Figure 4.
  • step S102 is started to train the image segmentation network.
  • each sample image needs to be input to the image segmentation network to obtain each generation mask output by the image segmentation network, where each generation mask corresponds to a sample image.
  • the "generating mask" described in this step is the same as the sample mask described in step S101, and may be a binary image.
  • step S103 for each generated mask, input the generated mask to the trained edge neural network to obtain the generated edge information output by the edge neural network, and the generated edge information is used to indicate what the generated mask indicates The contour edge of the area where the target object is located.
  • a trained edge neural network Before performing this step S103, a trained edge neural network needs to be obtained.
  • the trained edge neural network is used to generate a mask based on the input and output to generate edge information.
  • the generated edge information is used to indicate the input generated mask The contour edge of the area where the indicated target object is located.
  • the edge neural network after training may be as shown in FIG. 1.
  • FIG. 1 After inputting the generated mask shown in 002 into the trained edge neural network shown in 001, the trained edge neural network shown in 001 will output the generated edge information shown in 003.
  • each of the generated masks described in step S102 is input to the trained edge neural network, and each generated edge information output by the trained edge neural network is obtained, where each Each generation edge information corresponds to a generation mask, which is used to represent the contour edge of the image area where the target object indicated by the generation mask is located.
  • the connection between the image segmentation network and the trained edge neural network is shown in FIG. 5.
  • step S104 the loss function of the above-mentioned image segmentation network is determined.
  • the loss function is used to measure the difference between the sample mask and the generated mask corresponding to each sample image, and the loss function is also used to measure each sample image. Respectively correspond to the gap between the generated edge information and the sample edge information.
  • each sample image corresponds to a sample mask, sample edge information, mask generation, and edge information generation.
  • it is necessary to calculate the gap between the sample mask corresponding to the sample image and the generated mask for the convenience of subsequent description, it is defined that for a certain sample image, The gap between the sample mask corresponding to the sample image and the generated mask is the mask gap corresponding to the sample image), it is also necessary to calculate the gap between the sample edge information corresponding to the sample image and the generated edge information (for the convenience of subsequent description, the definition of For a sample image, the difference between the sample edge information corresponding to the sample image and the generated edge information is the edge difference corresponding to the sample image).
  • step S104 the loss function of the aforementioned image segmentation network needs to be calculated.
  • the loss function and each sample image are used to measure the difference between the sample mask and the generated mask corresponding to each sample image, and the loss function is also used To measure the gap between the generated edge information and the sample edge information corresponding to each sample image, that is, the loss function is positively correlated with the mask gap corresponding to each sample image, and the loss function is related to each sample image.
  • the corresponding marginal gaps are also positively correlated.
  • the calculation process of the aforementioned loss function may be:
  • Step A For each sample image, calculate the image difference between the generated mask corresponding to the sample image and the sample mask corresponding to the sample image (that is, it can be regarded as m1 i is the pixel value of the i-th pixel of the generated mask, m2 i is the pixel value of the i-th pixel of the sample mask, and M is the total number of pixels of the generated mask).
  • Step B If the above sample edge information and generated edge information are both images, then for each sample image, calculate the image difference between the sample edge information corresponding to the sample image and the generated edge information corresponding to the sample image (calculation of the image difference Refer to step A).
  • Step C The image differences obtained in the above step A and the image differences obtained in the image B can be averaged (if the number of sample images is N, the image differences obtained in step A and the image differences obtained in step B can be calculated And then divide by 2N) to get the loss function.
  • the calculation method of the aforementioned loss function is not limited to the aforementioned step A-step C.
  • the aforementioned loss function can also be calculated by the following formula (1):
  • LOSS 1 is the loss function of the above image segmentation network
  • N is the total number of sample images
  • F1 j is used to measure the gap between the sample mask corresponding to the jth sample image and the generated mask
  • F2 j is used to measure the The difference between the sample edge information corresponding to j sample images and the generated edge information
  • the calculation method of F1 j may be: calculating the cross entropy loss between the sample mask and the generated mask corresponding to the j-th sample image, and the specific formula is as follows:
  • M is the total number of pixels in the j-th sample image
  • the value of y ji is determined according to the sample mask corresponding to the j-th sample image
  • y ji is used to indicate the i-th sample image in the j-th sample image.
  • p ji is the probability that the i-th pixel in the j-th sample image predicted by the image segmentation network is in the image area where the target object is located
  • x is the logarithm log Bottom value.
  • the value of y ji is determined according to the sample mask corresponding to the j-th sample image. For example, if the sample mask corresponding to the j-th sample image is in the sample mask, it indicates that the j-th sample image is If i pixels are located in the image area where the target object is located, y ji can be 1. If the sample mask corresponding to the j-th sample image is in the sample mask, it indicates that the i-th pixel in the j-th sample image is not located where the target object is In the image area, y ji can be 0. Those skilled in the art should understand that the value of y ji is not limited to 1 and 0, and can also be other values. The value of y ji is preset, such as 1 or 0.
  • the value of y ji is greater than when the sample mask indicates that the i-th pixel is not located in the image area where the target object is located.
  • the value of y ji at time That is, if the sample mask indicates that the i-th pixel is located in the image area where the target object is located, y ji is 1, otherwise, y ji is 0. Or, if the sample mask indicates that the i-th pixel is located in the image area where the target object is located, y ji is 2, otherwise, y ji is 1. Or, if the sample mask indicates that the i-th pixel is located in the image area where the target object is located, y ji is 0.8, otherwise, y ji is 0.2.
  • the calculation method of F2 j may be similar to the above formula (2), that is, calculating the cross entropy loss of the sample edge information corresponding to the j-th sample image and the generated edge information.
  • mask 1 is the generation mask corresponding to the j-th sample image
  • mask 2 is the sample mask corresponding to the j-th sample image
  • h c (mask 1 ) is when the trained edge neural network input is mask 1
  • the first The output of c convolutional blocks, h c (mask 2 ) is the output of the c-th convolutional block when the trained edge neural network input is mask 2
  • ⁇ c is a constant.
  • the sample edge information can be measured by the above formula (3) The gap with generating edge information.
  • the edge neural network can be formed by cascading three convolutional blocks, and each convolutional block is a convolutional layer.
  • step S105 it is determined whether the aforementioned loss function is less than the first preset threshold, if so, step S107 is executed, otherwise, step S106 is executed.
  • step S106 adjust each parameter of the above-mentioned image segmentation network, and then return to perform step S102.
  • step S107 a trained image segmentation network is obtained.
  • the parameters of the image segmentation network are continuously adjusted until the loss function is less than the first preset threshold.
  • the parameter adjustment method is not specifically limited, and a gradient descent algorithm, a power update algorithm, etc. can be used, and the method used for adjusting the parameters is not limited here.
  • the sample image when the image segmentation network is trained, before the sample image is input to the image segmentation network, the sample image can be preprocessed first, and then the preprocessed sample image can be input to the image Split the network.
  • the above-mentioned preprocessing may include: image cropping and/or normalization processing and so on.
  • the test set can also be used to evaluate the trained image segmentation network.
  • the method of obtaining the test set can be referred to the prior art, and will not be repeated here.
  • the evaluation function can be:
  • X is the image area of the target object indicated by the generated mask output by the image segmentation network after the sample image is input to the trained image segmentation network.
  • Y is the image area of the target object indicated by the sample mask corresponding to the sample image.
  • the IoU Intersection-over-Union
  • X and Y are used to evaluate the image segmentation network after training.
  • By evaluating the trained image segmentation network we can further evaluate whether the performance of the trained image segmentation network meets the requirements. For example, if it is determined that the performance of the trained image segmentation network does not meet the requirements, the training of the trained image segmentation network is continued.
  • the training method provided in the first embodiment of the application ensures that the generated mask output by the image segmentation network is close to the sample mask, and at the same time, it will further ensure that the contour edge of the target object represented in the generated mask output by the image segmentation network is true The contour edge of is closer. Therefore, the image corresponding to the generated mask output by the image segmentation network provided by this application can more accurately represent the contour edge of the target object.
  • the training method includes the training process of the edge neural network. Please refer to Figure 7.
  • the training method includes:
  • each sample image containing the target object, the sample mask corresponding to each sample image, and the sample edge information corresponding to each sample mask are obtained, where each sample mask is used to indicate the corresponding sample In the image area where the target object is located, each sample edge information is used to indicate the contour edge of the image area where the target object is indicated by the corresponding sample mask.
  • step S301 please refer to the part of step S101 in the first embodiment, which will not be repeated here.
  • step S302 for each sample mask, the sample mask is input to the edge neural network to obtain edge information output by the edge neural network, and the edge information is used to indicate the area where the target object indicated by the sample mask is located Contour edges.
  • the step S302 to the subsequent step S306 are the training process of the edge neural network to obtain the trained edge neural network.
  • steps S302-S306 are executed before the subsequent step S308, and need not be executed before the step S307.
  • an edge neural network needs to be established in advance, and the edge neural network is used to obtain the contour edge of the area where the target object indicated by the input sample mask is located.
  • the edge neural network can be formed by cascading three convolutional layers.
  • each sample mask is input to the edge neural network to obtain each edge information output by the edge neural network, wherein each sample mask corresponds to one edge information output by the edge neural network.
  • step S303 the loss function of the edge neural network is determined, and the loss function is used to measure the difference between the sample edge information corresponding to each sample mask and the edge information output by the edge neural network.
  • step S303 determines the loss function of the above-mentioned edge neural network, where the loss function is positively correlated with the edge gap corresponding to each sample mask (the edge gap is corresponding to the sample mask). The difference between the edge information of the sample and the edge information output by the edge neural network after the sample mask is input to the edge neural network).
  • the calculation method of the loss function of the above-mentioned edge neural network may be:
  • the loss function of the edge neural network may be the difference between the edge information of the sample and the edge information output by the edge neural network.
  • Image difference the image difference calculation method can be referred to as described in step A in the first embodiment, which will not be repeated here).
  • the calculation method of the loss function of the edge neural network may be: for each sample mask, calculate the cross-entropy loss of the corresponding sample edge information and the edge information output by the edge neural network, and then calculate the average.
  • the specific calculation formula is as follows:
  • LOSS 2 is the loss function of the aforementioned edge neural network
  • N is the total number of sample masks (it is easy for those skilled in the art to understand that the total number of sample images, sample masks, and sample edge information are all the same, and they are all N )
  • M is the total number of pixels in the j-th sample mask
  • the value of r ji is determined according to the sample edge information corresponding to the j-th sample image
  • r ji is used to indicate the number of pixels in the j-th sample mask.
  • q ji is the probability that the i-th pixel in the j-th sample mask predicted by the edge neural network is the contour edge
  • x is the bottom value of the logarithm log.
  • the value of r ji is determined according to the sample edge information corresponding to the jth sample mask. For example, if the sample edge information corresponding to the jth sample mask indicates the jth sample mask If the i-th pixel in the film is a contour edge, r ji can be 1. If the sample edge information corresponding to the j-th sample mask indicates that the i-th pixel is not a contour edge, then r ji can be 0 . Those skilled in the art should understand that the value of r ji is not limited to 1 and 0, and may also be other values. The value of r ji is preset, such as 1 or 0.
  • the edge of the sample information indicates the i-th pixel as the edge contour
  • the value of r ji when the sample is greater than the edge information indicates an i-th pixel is not the value of r ji edge contour. That is, if the sample edge information indicates that the i-th pixel is a contour edge, r ji is 1, otherwise, r ji is 0. Or, if the sample edge information indicates that the i-th pixel is a contour edge, r ji is 2, otherwise, r ji is 1. Or, if the sample edge information indicates that the i-th pixel is a contour edge, r ji is 0.8, otherwise, r ji is 0.2.
  • step S304 it is determined whether the loss function of the aforementioned edge neural network is less than a second preset threshold, if not, step S305 is executed, and if yes, step S306 is executed.
  • step S305 adjust each parameter of the above-mentioned edge neural network model, and then return to step S302.
  • step S306 a trained edge neural network is obtained.
  • the parameters of the edge neural network are continuously adjusted until the loss function is less than the second preset threshold.
  • the parameter adjustment method is not specifically limited, and a gradient descent algorithm, a power update algorithm, etc. can be used, and the method used for adjusting the parameters is not limited here.
  • step S307 for each sample image, the sample image is input to the image segmentation network, and the generated mask output by the image segmentation network for indicating the area of the target object in the sample image is obtained.
  • step S308 for each generated mask, input the generated mask to the trained edge neural network to obtain the generated edge information output by the edge neural network, and the generated edge information is used to indicate what the generated mask indicates The contour edge of the area where the target object is located.
  • step S309 the loss function of the above-mentioned image segmentation network is determined.
  • the loss function is used to measure the difference between the sample mask corresponding to each sample image and the generated mask, and the loss function is also used to measure each sample image. Respectively correspond to the gap between the generated edge information and the sample edge information.
  • step S310 it is determined whether the aforementioned loss function is less than a first preset threshold, if so, step S312 is executed, otherwise, step S311 is executed.
  • step S31 adjust the various parameters of the above-mentioned image segmentation network, and then return to perform step S102.
  • step S312 a trained image segmentation network is obtained.
  • the training process of the edge neural network First, input the sample mask into the edge neural network to obtain the edge information output by the edge neural network. Secondly, the cross-entropy loss is calculated according to the edge neural network and the edge information of each sample. The edge information of the sample is obtained by the expansion operation and the subtraction operation of the sample mask. For details, please refer to the description of the first embodiment, which will not be repeated here. Then, the various cross entropy losses are averaged to obtain the loss function. Finally, continuously adjust the various parameters of the edge neural network until the loss function is small, so as to obtain the edge neural network after training.
  • the training method described in the second embodiment of the present application has an additional training process of the edge neural network, which can make the samples used for training the edge neural network consistent with the samples used for training the image segmentation network Therefore, the accuracy of the edge of the mask output by the image segmentation network can be better measured according to the output result of the edge neural network, so as to better train the image segmentation network.
  • the third embodiment of the present application provides an image processing method. Please refer to FIG. 9.
  • the image processing method includes:
  • step S401 an image to be processed is obtained, and the image to be processed is input to the trained image segmentation network to obtain a mask corresponding to the image to be processed, wherein the trained image segmentation network uses the trained edge
  • the neural network is trained, and the trained edge neural network is used to output the edge contour of the area where the target object indicated by the mask is located according to the input mask.
  • the trained edge neural network described in this step S401 is a neural network obtained by training using the method described in the first or second embodiment above.
  • step S402 the target objects contained in the image to be processed are segmented based on the mask corresponding to the image to be processed.
  • step S402 a specific operation of changing the background can also be performed. This operation is in the prior art and will not be repeated here.
  • the method described in the third embodiment can be a method applied in a terminal device (such as a mobile phone).
  • This method can facilitate the user to replace the background in the image to be processed.
  • This method can accurately segment the target object and more accurately replace the background. Can improve user experience to a certain extent.
  • the fourth embodiment of the present application provides a training device for an image segmentation network. For ease of description, only the parts related to the present application are shown. As shown in FIG. 10, the training device 500 includes:
  • the sample acquisition module 501 is used to acquire each sample image containing the target object, a sample mask corresponding to each sample image, and sample edge information corresponding to each sample mask, wherein each sample mask is used to indicate Corresponding to the image area where the target object is located in the sample image, each sample edge information is used to indicate the contour edge of the image area where the target object indicated by the corresponding sample mask is located.
  • the generation mask acquisition module 502 is configured to input the sample image to the image segmentation network for each sample image, and obtain the generation mask output by the image segmentation network for indicating the area of the target object in the sample image.
  • the generated edge acquisition module 503 is used to input the generated mask to the trained edge neural network for each generated mask to obtain the generated edge information output by the edge neural network, and the generated edge information is used to indicate the generated mask.
  • the contour edge of the area where the target object indicated by the film is located.
  • the loss determination module 504 is used to determine the loss function of the image segmentation network.
  • the loss function is used to measure the difference between the sample mask and the generated mask corresponding to each sample image, and the loss function is also used to Measure the gap between the generated edge information and the sample edge information corresponding to each sample image.
  • the parameter adjustment module 505 is used to adjust various parameters of the image segmentation network, and then trigger the generation mask acquisition module to continue to perform corresponding steps until the loss function of the image segmentation network is less than the first preset threshold, thereby Get the trained image segmentation network.
  • the aforementioned loss determination module 504 is specifically configured to:
  • LOSS 1 is the loss function of the image segmentation network
  • N is the total number of sample images
  • F1 j is used to measure the gap between the sample mask corresponding to the jth sample image and the generated mask
  • F2 j is used to measure The difference between the sample edge information corresponding to the j-th sample image and the generated edge information
  • M is the total number of pixels in the j-th sample image
  • the value of y ji is determined according to the sample mask corresponding to the j-th sample image
  • y ji is used to indicate the i-th sample image in the j-th sample image.
  • p ji is the probability that the i-th pixel in the j-th sample image predicted by the image segmentation network is in the image area where the target object is located
  • x is the logarithm log Bottom value.
  • the value of y ji is greater than when the sample mask indicates the i-th pixel of the image region of the target object is not located in the y ji value.
  • the above-mentioned trained edge neural network is formed by cascading A convolutional blocks, and each convolutional block is composed of B convolutional layers.
  • mask 1 is the generation mask corresponding to the j-th sample image
  • mask 2 is the sample mask corresponding to the j-th sample image
  • h c (mask 1 ) is when the trained edge neural network input is mask 1
  • the first The output of c convolutional blocks, h c (mask 2 ) is the output of the c-th convolutional block when the trained edge neural network input is mask 2
  • ⁇ c is a constant.
  • the above-mentioned training device further includes an edge neural network training module, and the edge neural network training module includes:
  • the edge information acquisition unit is used to input the sample mask to the edge neural network for each sample mask to obtain the edge information output by the edge neural network, and the edge information is used to indicate the target object indicated by the sample mask The contour edge of the area.
  • the edge loss determining unit is used to determine the loss function of the edge neural network, and the loss function is used to measure the difference between the sample edge information corresponding to each sample mask and the edge information output by the edge neural network.
  • the edge parameter adjustment unit is used to adjust various parameters of the edge neural network, and then trigger the edge information acquisition unit to continue to perform corresponding steps until the loss function value of the edge neural network is less than the second preset threshold, thereby obtaining training After the edge of the neural network.
  • the aforementioned edge loss determining unit is specifically used for:
  • LOSS 2 is the loss function of the edge neural network
  • N is the total number of sample images
  • M is the total number of pixels in the j-th sample mask
  • the value of r ji is based on the j-th sample image
  • the corresponding sample edge information is determined
  • r ji is used to indicate whether the i-th pixel in the j-th sample mask is a contour edge
  • q ji is the i-th pixel in the j-th sample mask predicted by the edge neural network
  • the pixel point is the probability of the edge of the contour
  • x is the base value of the logarithm log.
  • the value of r ji when the sample is greater than the edge information indicates an i-th pixel is not the value of r ji edge contour.
  • the image processing apparatus 600 includes:
  • the mask acquisition module 601 is used to acquire the image to be processed, and input the image to be processed into the trained image segmentation network to obtain the mask corresponding to the image to be processed, wherein the trained edge neural network is used After training, the trained edge neural network is used to output the edge contour of the area where the target object indicated by the mask is located according to the input mask (specifically, the trained image segmentation network adopts the method as in the first embodiment or The training method described in the second embodiment is obtained through training).
  • the target object segmentation module 602 is configured to segment the target object contained in the image to be processed based on the mask corresponding to the image to be processed.
  • FIG. 12 is a schematic diagram of a terminal device provided in Embodiment 6 of the present application.
  • the terminal device 700 of this embodiment includes a processor 701, a memory 702, and a computer program 703 that is stored in the memory 702 and can run on the processor 701.
  • the above-mentioned processor 701 implements the steps in the above-mentioned method embodiments when the above-mentioned computer program 703 is executed.
  • the processor 701 executes the computer program 703, the function of each module/unit in the foregoing device embodiments is realized.
  • the foregoing computer program 703 may be divided into one or more modules/units, and the foregoing one or more modules/units are stored in the foregoing memory 702 and executed by the foregoing processor 701 to complete the present application.
  • the foregoing one or more modules/units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the foregoing computer program 703 in the foregoing terminal device 700.
  • the aforementioned computer program 703 can be divided into a sample acquisition module, a mask generation module, an edge generation module, a loss determination module, and a parameter adjustment module.
  • the specific functions of each module are as follows:
  • S101 Obtain each sample image containing a target object, a sample mask corresponding to each sample image, and sample edge information corresponding to each sample mask, where each sample mask is used to indicate the target in the corresponding sample image In the image area where the object is located, the edge information of each sample is used to indicate the contour edge of the image area where the target object is indicated by the corresponding sample mask.
  • S102 For each sample image, input the sample image to an image segmentation network, and obtain a generation mask output by the image segmentation network for indicating a region where a target object in the sample image is located.
  • S103 For each generated mask, input the generated mask to the trained edge neural network to obtain generated edge information output by the edge neural network, and the generated edge information is used to indicate the target object indicated by the generated mask The contour edge of the area.
  • S104 Determine the loss function of the image segmentation network, where the loss function is used to measure the difference between the sample mask corresponding to each sample image and the generated mask, and the loss function is also used to measure each sample image Respectively correspond to the gap between the generated edge information and the sample edge information.
  • S105 Adjust various parameters of the image segmentation network, and then return to execute S102 until the loss function of the image segmentation network is less than a first preset threshold, thereby obtaining a trained image segmentation network.
  • the aforementioned computer program 703 can be divided into a mask acquisition module and a target object segmentation module, and the specific functions of each module are as follows:
  • the target object contained in the image to be processed is segmented.
  • the foregoing terminal device may include, but is not limited to, a processor 701 and a memory 702.
  • FIG. 12 is only an example of the terminal device 700, and does not constitute a limitation on the terminal device 700. It may include more or less components than those shown in the figure, or a combination of certain components, or different components.
  • the aforementioned terminal device may also include input and output devices, network access devices, buses, and so on.
  • the so-called processor 701 may be a central processing unit (Central Processing Unit, CPU), other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
  • the foregoing memory 702 may be an internal storage unit of the foregoing terminal device 700, such as a hard disk or a memory of the terminal device 700.
  • the memory 702 may also be an external storage device of the terminal device 700, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, and a flash memory equipped on the terminal device 700. Card (Flash Card), etc.
  • the aforementioned memory 702 may also include both an internal storage unit of the aforementioned terminal device 700 and an external storage device.
  • the above-mentioned memory 702 is used to store the above-mentioned computer program and other programs and data required by the above-mentioned terminal device.
  • the aforementioned memory 702 can also be used to temporarily store data that has been output or will be output.
  • the disclosed device/terminal device and method may be implemented in other ways.
  • the device/terminal device embodiments described above are only illustrative.
  • the division of the above-mentioned modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or Components can be combined or integrated into another system, or some features can be omitted or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.
  • the units described above as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.
  • the above-mentioned integrated modules/units are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, this application implements all or part of the processes in the foregoing method embodiments, and can also be completed by instructing relevant hardware through a computer program.
  • the foregoing computer program may be stored in a computer-readable storage medium. When the program is executed by the processor, it can implement the steps of the foregoing method embodiments.
  • the above-mentioned computer program includes computer program code, and the above-mentioned computer program code may be in the form of source code, object code, executable file, or some intermediate forms.
  • the above-mentioned computer-readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random Access memory (RAM, Random Access Memory), electric carrier signal, telecommunications signal, software distribution medium, etc.
  • ROM Read-Only Memory
  • RAM Random Access Memory
  • electric carrier signal telecommunications signal
  • software distribution medium etc.
  • the content contained in the above-mentioned computer-readable media can be appropriately added or deleted in accordance with the requirements of the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to the legislation and patent practice, the computer-readable media cannot Including electric carrier signal and telecommunication signal.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Image Analysis (AREA)

Abstract

La présente invention concerne un procédé d'entraînement de réseau, un procédé de traitement d'image, un réseau, un dispositif terminal et un support. Le procédé d'entraînement comprend les étapes consistant à : S1, acquérir des images échantillons contenant un objet cible, un masque échantillon correspondant à chaque image échantillon, et des informations marginales échantillons correspondant au masque échantillon ; S2, entrer chaque image échantillon dans un réseau de segmentation d'image pour obtenir un masque généré délivré par le réseau de segmentation d'image ; S3, entrer le masque généré dans un réseau neuronal marginal entraîné afin d'obtenir des informations marginales générées, délivrées par le réseau neuronal marginal ; S4, déterminer une fonction de perte en fonction de l'espace entre le masque échantillon et le masque généré, et de l'espace entre les informations marginales générées et les informations marginales échantillons ; S5, ajuster les paramètres du réseau de segmentation d'image et revenir à S2 jusqu'à ce que la fonction de perte soit inférieure à un seuil. La présente invention permet de représenter plus précisément le bord de contour de l'objet cible sur l'image de masque délivrée par le réseau de segmentation d'image.
PCT/CN2020/117470 2019-09-29 2020-09-24 Procédé d'entraînement de réseau, procédé de traitement d'image, réseau, dispositif terminal et support Ceased WO2021057848A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910931784.2A CN110660066B (zh) 2019-09-29 2019-09-29 网络的训练方法、图像处理方法、网络、终端设备及介质
CN201910931784.2 2019-09-29

Publications (1)

Publication Number Publication Date
WO2021057848A1 true WO2021057848A1 (fr) 2021-04-01

Family

ID=69039787

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/117470 Ceased WO2021057848A1 (fr) 2019-09-29 2020-09-24 Procédé d'entraînement de réseau, procédé de traitement d'image, réseau, dispositif terminal et support

Country Status (2)

Country Link
CN (1) CN110660066B (fr)
WO (1) WO2021057848A1 (fr)

Cited By (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113177606A (zh) * 2021-05-20 2021-07-27 上海商汤智能科技有限公司 图像处理方法、装置、设备及存储介质
US20210279883A1 (en) * 2020-03-05 2021-09-09 Alibaba Group Holding Limited Image processing method, apparatus, electronic device, and storage medium
CN113378948A (zh) * 2021-06-21 2021-09-10 梅卡曼德（北京）机器人科技有限公司 图像掩膜生成方法、装置、电子设备和存储介质
CN113610752A (zh) * 2021-06-15 2021-11-05 上海联影智能医疗科技有限公司 乳腺图像配准方法、计算机设备和存储介质
CN113724163A (zh) * 2021-08-31 2021-11-30 平安科技（深圳）有限公司 基于神经网络的图像矫正方法、装置、设备及介质
CN114049280A (zh) * 2021-11-25 2022-02-15 广州华多网络科技有限公司 图像消除修复方法及其装置、设备、介质、产品
CN114067228A (zh) * 2021-10-26 2022-02-18 神思电子技术股份有限公司 一种增强前景与背景区分度的目标检测方法及系统
CN114140574A (zh) * 2021-09-28 2022-03-04 每平每屋(上海)科技有限公司 一种生成三维虚拟模型的方法及装置
CN114494230A (zh) * 2022-02-14 2022-05-13 北京航空航天大学杭州创新研究院 乳腺病灶分割装置、模型训练方法及电子设备
CN115100404A (zh) * 2022-05-17 2022-09-23 阿里巴巴（中国）有限公司 处理图像、图像分割模型的方法及设置图像背景的方法
CN115222677A (zh) * 2022-07-01 2022-10-21 用友网络科技股份有限公司 车厢区域识别方法和系统、废钢智能判级系统
CN115223171A (zh) * 2022-03-15 2022-10-21 腾讯科技（深圳）有限公司 文本识别方法、装置、设备及存储介质
CN115330823A (zh) * 2022-07-18 2022-11-11 国网湖南省电力有限公司 一种无人机巡检图像边缘检测方法、系统及存储介质
CN116309478A (zh) * 2023-03-21 2023-06-23 合肥联亚制衣有限公司 一种非平面织物的车缝线检测方法、系统、设备及介质
CN116823864A (zh) * 2023-08-25 2023-09-29 锋睿领创(珠海)科技有限公司 基于平衡损失函数的数据处理方法、装置、设备及介质
CN117315263A (zh) * 2023-11-28 2023-12-29 杭州申昊科技股份有限公司 一种目标轮廓分割装置、训练方法、分割方法、电子设备
CN117315794A (zh) * 2022-06-24 2023-12-29 北京字跳网络技术有限公司 图像识别方法、装置、电子设备、存储介质及产品
CN119478516A (zh) * 2024-11-06 2025-02-18 南京航空航天大学 一种低空空域电线探测方法、装置、设备、介质及产品

Families Citing this family (32)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110660066B (zh) * 2019-09-29 2023-08-04 Oppo广东移动通信有限公司 网络的训练方法、图像处理方法、网络、终端设备及介质
CN111311485B (zh) * 2020-03-17 2023-07-04 Oppo广东移动通信有限公司 图像处理方法及相关装置
CN111415358B (zh) * 2020-03-20 2024-03-12 Oppo广东移动通信有限公司 图像分割方法、装置、电子设备及存储介质
CN111462086B (zh) * 2020-03-31 2024-04-26 推想医疗科技股份有限公司 图像分割方法及装置、神经网络模型的训练方法及装置
CN113744293A (zh) * 2020-05-13 2021-12-03 Oppo广东移动通信有限公司 图像处理方法、图像处理装置、电子设备和可读存储介质
CN111899273A (zh) * 2020-06-10 2020-11-06 上海联影智能医疗科技有限公司 图像分割方法、计算机设备和存储介质
CN113808003B (zh) * 2020-06-17 2024-02-09 北京达佳互联信息技术有限公司 图像处理模型的训练方法、图像处理方法及装置
CN111754521B (zh) * 2020-06-17 2024-06-25 Oppo广东移动通信有限公司 图像处理方法和装置、电子设备及存储介质
CN111488876B (zh) * 2020-06-28 2020-10-23 平安国际智慧城市科技股份有限公司 基于人工智能的车牌识别方法、装置、设备及介质
US11790533B2 (en) * 2020-07-02 2023-10-17 Sony Group Corporation Machine learning based image segmentation training with contour accuracy evaluation
CN112070793A (zh) * 2020-09-11 2020-12-11 北京邮电大学 一种目标提取方法及装置
CN114266889A (zh) * 2020-09-15 2022-04-01 新加坡依图有限责任公司(私有) 图像识别方法及其装置、可读介质和电子设备
CN112132847A (zh) * 2020-09-27 2020-12-25 北京字跳网络技术有限公司 模型训练方法、图像分割方法、装置、电子设备和介质
CN112669228B (zh) * 2020-12-22 2024-05-31 厦门美图之家科技有限公司 图像处理方法、系统、移动终端及存储介质
CN112465843A (zh) * 2020-12-22 2021-03-09 深圳市慧鲤科技有限公司 图像分割方法及装置、电子设备和存储介质
CN112580567B (zh) * 2020-12-25 2024-04-16 深圳市优必选科技股份有限公司 一种模型获取方法、模型获取装置及智能设备
CN115170581A (zh) * 2021-04-07 2022-10-11 武汉Tcl集团工业研究院有限公司 人像分割模型的生成方法、人像分割模型及人像分割方法
CN113159074B (zh) * 2021-04-26 2024-02-09 京东科技信息技术有限公司 图像处理方法、装置、电子设备和存储介质
CN113643311B (zh) * 2021-06-28 2024-04-09 清华大学 一种对边界误差鲁棒的图像分割方法和装置
CN113327210B (zh) * 2021-06-30 2023-04-07 中海油田服务股份有限公司 测井图像填补方法、装置、介质及电子设备
CN114022497B (zh) * 2021-09-30 2025-08-29 泰康保险集团股份有限公司 一种图像处理方法及装置
CN113822287B (zh) * 2021-11-19 2022-02-22 苏州浪潮智能科技有限公司 一种图像处理方法、系统、设备以及介质
WO2023097479A1 (fr) * 2021-11-30 2023-06-08 华为技术有限公司 Procédé et appareil d'apprentissage de modèle, et procédé et appareil de construction d'une structure tridimensionnelle d'oreillette
CN114255378B (zh) * 2021-12-20 2025-10-31 深圳万兴软件有限公司 基于损失值的抠图模型训练方法、装置、设备及介质
CN114419086B (zh) * 2022-01-20 2025-06-17 北京字跳网络技术有限公司 边缘提取方法、装置、电子设备及存储介质
CN114663656B (zh) * 2022-03-01 2025-11-04 杭州网易云音乐科技有限公司 图像分割网络的训练方法以及图像分割方法、装置及设备
CN114998168B (zh) * 2022-05-19 2024-07-23 清华大学 超声图像样本生成方法及设备
CN114758136B (zh) * 2022-06-13 2022-10-18 深圳比特微电子科技有限公司 目标去除模型建立方法、装置及可读存储介质
CN117237397B (zh) * 2023-07-13 2024-05-28 天翼爱音乐文化科技有限公司 基于特征融合的人像分割方法、系统、设备及存储介质
CN117474932B (zh) * 2023-12-27 2024-03-19 苏州镁伽科技有限公司 对象分割方法和装置、电子设备及存储介质
CN119477938A (zh) * 2024-10-31 2025-02-18 同济大学 一种基于形状距离场的三阶段牙齿智能精准分割方法
CN119941524A (zh) * 2024-12-31 2025-05-06 江苏惟德智能装备有限公司 粉体气力输送图像处理方法、设备及存储介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109325954A (zh) * 2018-09-18 2019-02-12 北京旷视科技有限公司 图像分割方法、装置及电子设备
CN109726644A (zh) * 2018-12-14 2019-05-07 重庆邮电大学 一种基于生成对抗网络的细胞核分割方法
US20190156154A1 (en) * 2017-11-21 2019-05-23 Nvidia Corporation Training a neural network to predict superpixels using segmentation-aware affinity loss
CN110660066A (zh) * 2019-09-29 2020-01-07 Oppo广东移动通信有限公司 网络的训练方法、图像处理方法、网络、终端设备及介质

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106846336B (zh) * 2017-02-06 2022-07-15 腾讯科技（上海）有限公司 提取前景图像、替换图像背景的方法及装置
CN109493347B (zh) * 2017-09-12 2021-03-23 深圳科亚医疗科技有限公司 在图像中对稀疏分布的对象进行分割的方法和系统
CN108647588A (zh) * 2018-04-24 2018-10-12 广州绿怡信息科技有限公司 物品类别识别方法、装置、计算机设备和存储介质
CN109377445B (zh) * 2018-10-12 2023-07-04 北京旷视科技有限公司 模型训练方法、替换图像背景的方法、装置和电子系统
CN109685067B (zh) * 2018-12-26 2022-05-03 江西理工大学 一种基于区域和深度残差网络的图像语义分割方法
CN110084234B (zh) * 2019-03-27 2023-04-18 东南大学 一种基于实例分割的声呐图像目标识别方法
CN110188760B (zh) * 2019-04-01 2021-10-22 上海卫莎网络科技有限公司 一种图像处理模型训练方法、图像处理方法及电子设备
CN110176016B (zh) * 2019-05-28 2021-04-30 招远市国有资产经营有限公司 一种基于人体轮廓分割与骨骼识别的虚拟试衣方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190156154A1 (en) * 2017-11-21 2019-05-23 Nvidia Corporation Training a neural network to predict superpixels using segmentation-aware affinity loss
CN109325954A (zh) * 2018-09-18 2019-02-12 北京旷视科技有限公司 图像分割方法、装置及电子设备
CN109726644A (zh) * 2018-12-14 2019-05-07 重庆邮电大学 一种基于生成对抗网络的细胞核分割方法
CN110660066A (zh) * 2019-09-29 2020-01-07 Oppo广东移动通信有限公司 网络的训练方法、图像处理方法、网络、终端设备及介质

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
CHEN XU; WILLIAMS BRYAN M.; VALLABHANENI SRINIVASA R.; CZANNER GABRIELA; WILLIAMS RACHEL; ZHENG YALIN: "Learning Active Contour Models for Medical Image Segmentation", 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), IEEE, 15 June 2019 (2019-06-15), pages 11624 - 11632, XP033686570, DOI: 10.1109/CVPR.2019.01190 *

Cited By (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11816842B2 (en) * 2020-03-05 2023-11-14 Alibaba Group Holding Limited Image processing method, apparatus, electronic device, and storage medium
US20210279883A1 (en) * 2020-03-05 2021-09-09 Alibaba Group Holding Limited Image processing method, apparatus, electronic device, and storage medium
CN113177606A (zh) * 2021-05-20 2021-07-27 上海商汤智能科技有限公司 图像处理方法、装置、设备及存储介质
CN113177606B (zh) * 2021-05-20 2023-11-28 上海商汤智能科技有限公司 图像处理方法、装置、设备及存储介质
CN113610752A (zh) * 2021-06-15 2021-11-05 上海联影智能医疗科技有限公司 乳腺图像配准方法、计算机设备和存储介质
CN113378948A (zh) * 2021-06-21 2021-09-10 梅卡曼德（北京）机器人科技有限公司 图像掩膜生成方法、装置、电子设备和存储介质
CN113724163A (zh) * 2021-08-31 2021-11-30 平安科技（深圳）有限公司 基于神经网络的图像矫正方法、装置、设备及介质
CN113724163B (zh) * 2021-08-31 2024-06-07 平安科技（深圳）有限公司 基于神经网络的图像矫正方法、装置、设备及介质
CN114140574A (zh) * 2021-09-28 2022-03-04 每平每屋(上海)科技有限公司 一种生成三维虚拟模型的方法及装置
CN114067228A (zh) * 2021-10-26 2022-02-18 神思电子技术股份有限公司 一种增强前景与背景区分度的目标检测方法及系统
CN114049280A (zh) * 2021-11-25 2022-02-15 广州华多网络科技有限公司 图像消除修复方法及其装置、设备、介质、产品
CN114494230A (zh) * 2022-02-14 2022-05-13 北京航空航天大学杭州创新研究院 乳腺病灶分割装置、模型训练方法及电子设备
CN115223171A (zh) * 2022-03-15 2022-10-21 腾讯科技（深圳）有限公司 文本识别方法、装置、设备及存储介质
CN115100404A (zh) * 2022-05-17 2022-09-23 阿里巴巴（中国）有限公司 处理图像、图像分割模型的方法及设置图像背景的方法
CN117315794A (zh) * 2022-06-24 2023-12-29 北京字跳网络技术有限公司 图像识别方法、装置、电子设备、存储介质及产品
CN115222677A (zh) * 2022-07-01 2022-10-21 用友网络科技股份有限公司 车厢区域识别方法和系统、废钢智能判级系统
CN115330823A (zh) * 2022-07-18 2022-11-11 国网湖南省电力有限公司 一种无人机巡检图像边缘检测方法、系统及存储介质
CN116309478A (zh) * 2023-03-21 2023-06-23 合肥联亚制衣有限公司 一种非平面织物的车缝线检测方法、系统、设备及介质
CN116823864A (zh) * 2023-08-25 2023-09-29 锋睿领创(珠海)科技有限公司 基于平衡损失函数的数据处理方法、装置、设备及介质
CN116823864B (zh) * 2023-08-25 2024-01-05 锋睿领创(珠海)科技有限公司 基于平衡损失函数的数据处理方法、装置、设备及介质
CN117315263A (zh) * 2023-11-28 2023-12-29 杭州申昊科技股份有限公司 一种目标轮廓分割装置、训练方法、分割方法、电子设备
CN117315263B (zh) * 2023-11-28 2024-03-22 杭州申昊科技股份有限公司 一种目标轮廓装置、训练方法、分割方法、电子设备及存储介质
CN119478516A (zh) * 2024-11-06 2025-02-18 南京航空航天大学 一种低空空域电线探测方法、装置、设备、介质及产品

Also Published As

Publication number Publication date
CN110660066B (zh) 2023-08-04
CN110660066A (zh) 2020-01-07

Similar Documents

Publication Publication Date Title
WO2021057848A1 (fr) Procédé d'entraînement de réseau, procédé de traitement d'image, réseau, dispositif terminal et support
CN114424253B (zh) 模型训练方法、装置、存储介质及电子设备
WO2020207190A1 (fr) Procédé de détermination d'informations tridimensionnelles, dispositif de détermination d'informations tridimensionnelles et appareil terminal
CN111489290B (zh) 一种人脸图像超分辨重建方法、装置及终端设备
CN109166156B (zh) 一种摄像头标定图像的生成方法、移动终端及存储介质
WO2024041108A1 (fr) Procédé et appareil d'entraînement de modèle de correction d'image, procédé et appareil de correction d'image, et dispositif informatique
WO2021164269A1 (fr) Procédé et appareil d'acquisition de carte de disparité basés sur un mécanisme d'attention
WO2021098618A1 (fr) Appareil et procédé de classification de données, dispositif terminal et support de stockage lisible
CN112668577B (zh) 大尺度图像中目标物体的检测方法、终端及装置
WO2020248841A1 (fr) Procédé et appareil de détection d'au pour une image et dispositif électronique et support d'informations
CN112232506B (zh) 网络模型训练方法、图像目标识别方法、装置和电子设备
CN108337551A (zh) 一种屏幕录制方法、存储介质及终端设备
CN108961267B (zh) 图片处理方法、图片处理装置及终端设备
CN111192312B (zh) 基于深度学习的深度图像获取方法、装置、设备及介质
CN111311485A (zh) 图像处理方法及相关装置
EP4447008A1 (fr) Procédé et appareil de reconnaissance faciale
CN112419231B (zh) 能见度确定方法、装置、计算机设备和存储介质
CN114399497A (zh) 文本图像质量检测方法、装置、计算机设备及存储介质
CN114332993A (zh) 人脸识别方法、装置、电子设备及计算机可读存储介质
CN114913567A (zh) 口罩佩戴的检测方法、装置、终端设备及可读存储介质
CN109165648B (zh) 一种图像处理方法、图像处理装置及移动终端
CN108932703B (zh) 图片处理方法、图片处理装置及终端设备
CN111340722B (zh) 图像处理方法、处理装置、终端设备及可读存储介质
CN111222446B (zh) 人脸识别方法、人脸识别装置及移动终端
CN111784607B (zh) 图像色调映射方法、装置、终端设备及存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20867569

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20867569

Country of ref document: EP

Kind code of ref document: A1

122 Ep: pct application non-entry in european phase

Ref document number: 20867569

Country of ref document: EP

Kind code of ref document: A1