[go: up one dir, main page]

WO2008018460A1 - Procédé, appareil et programme de traitement d'image, et appareil de prise d'image - Google Patents

Procédé, appareil et programme de traitement d'image, et appareil de prise d'image Download PDF

Info

Publication number
WO2008018460A1
WO2008018460A1 PCT/JP2007/065447 JP2007065447W WO2008018460A1 WO 2008018460 A1 WO2008018460 A1 WO 2008018460A1 JP 2007065447 W JP2007065447 W JP 2007065447W WO 2008018460 A1 WO2008018460 A1 WO 2008018460A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
edge
image processing
processing method
pixel value
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/JP2007/065447
Other languages
English (en)
Japanese (ja)
Inventor
Akihiko Utsugi
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.)
Nikon Corp
Original Assignee
Nikon Corp
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 Nikon Corp filed Critical Nikon Corp
Publication of WO2008018460A1 publication Critical patent/WO2008018460A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

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
    • G06T7/12—Edge-based segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation
    • 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
    • G06T2207/30201—Face

Definitions

  • Image processing method image processing apparatus, image processing program, imaging apparatus
  • the present invention relates to an image processing method and an image processing apparatus for performing edge detection in an acquired image.
  • the present invention relates to an image processing program and an imaging apparatus.
  • Patent Document 1 Japanese Unexamined Patent Application Publication No. 2004-199386
  • edge extraction methods such as a Gabor filter can sufficiently extract edge structure information.
  • the positions of eyes, nose, mouth, etc. in the face image are locally darker than the surroundings. Therefore, when identifying facial images, it is important to know if there are locally dark structures at positions corresponding to the eyes, nose and mouth. Also, in the face image showing teeth and laughing, the teeth are locally brighter than the surroundings. Therefore, identify the smile It is important to know if there is a locally bright structure in the mouth position.
  • the edge structure is locally identified as ⁇ ! /, A force that is a structure, a locally bright structure, or another structure. There was a problem that it was not possible.
  • an image processing method acquires an image including a plurality of pixels, and based on the acquired image, an edge having a concave structure in which a pixel value is locally recessed from the periphery. An edge image is generated based on the detected edge of the concave structure.
  • the edge image is generated by calculating a nonlinear filter that detects the edge of the concave structure with respect to the acquired image.
  • the nonlinear finisher is based on a difference between a pixel value in the target region and a minimum value of the pixel values in the peripheral region of the target region! / It is preferable to output the calculation result!
  • the nonlinear finisher is based on a difference between a minimum pixel value in the target region and a minimum pixel value in the peripheral region.
  • the image processing method when the minimum value of the pixel value in the target area is smaller than the minimum value of the pixel value in the peripheral area of the target area, according to the difference
  • the value is the value of the edge pixel and the minimum value of the pixel value in the target area is larger than the minimum value of the pixel value in the peripheral area of the target area, it is preferable to perform the clipping process with the value of the edge pixel set to zero.
  • an image processing method acquires an image composed of a plurality of pixels, and detects a convex structure edge in which pixel values protrude locally from the periphery based on the acquired image. Then, an edge image is generated based on the detected edge of the convex structure.
  • the edge image is generated by calculating a nonlinear filter that detects the edge of the convex structure with respect to the acquired image.
  • the nonlinear filter is based on the difference between the pixel value in the target region and the maximum value of the pixel value in the peripheral region of the target region! / It is preferable to output the calculation result!
  • the nonlinear filter is based on a difference between the maximum pixel value in the target region and the maximum pixel value in the peripheral region.
  • the difference is determined according to the difference.
  • the edge pixel value is preferably clipped to zero.
  • a luminance image based on a luminance component is generated based on the acquired image, and the generated luminance image is used.
  • the target area is only one target pixel or the target pixel and its adjacent pixels. It is a 2-pixel area, and the surrounding area is preferably a 2-pixel area located on both sides of the target area.
  • the nonlinear filter is preferably operated in at least two directions. Yes.
  • an image processing method acquires an image composed of a plurality of pixels, and on the basis of the acquired image, the edge of a concave structure that protrudes locally from the periphery and protrudes.
  • the edge of the convex structure is detected, an edge image of the concave structure is generated based on the detected edge of the concave structure, and an edge image of the convex structure is generated based on the detected edge of the convex structure.
  • an image processing method acquires an image composed of a plurality of pixels, and based on the acquired image, the edge of a concave structure in which pixel values are locally recessed from the periphery.
  • an image processing method acquires an image composed of a plurality of pixels, and based on the acquired image, the edge of the concave structure where the pixel value is locally recessed from the periphery and locally At least one of the edges of the convex structure whose pixel value protrudes from the periphery is detected, and an edge image is generated based on at least one of the detected concave structure edge and convex structure edge.
  • an image processing method acquires an image composed of a plurality of pixels, detects an edge component of the acquired image, performs gamma conversion on the detected edge component, and performs gamma conversion. An edge image based on the edge component is generated.
  • the eighteenth aspect of the present invention in the image processing method according to any one of the first to seventeenth aspects, it is preferable to detect a face image using the generated edge image.
  • the image processing program is an image processing program for causing a computer to execute the image processing method according to any one of the first to eighteenth aspects.
  • the image processing apparatus is an image processing apparatus equipped with the image processing program of the nineteenth aspect.
  • an imaging apparatus is an imaging apparatus that mounts the image processing program of the nineteenth aspect.
  • an edge can be accurately detected.
  • the edge of the concave structure is detected in claim 1, it is possible to accurately detect a local crease or structure such as an eye of the face image as an edge.
  • FIG. 1 is a diagram showing an image processing apparatus according to an embodiment of the present invention.
  • FIG. 2 is a diagram showing a flowchart of an image processing program executed by the personal computer 1.
  • FIG. 3 is a diagram showing an edge extraction target pixel and peripheral pixels with coordinates xy.
  • FIG. 4 is a diagram showing the result of creating a luminance concave image (x, y) for various luminance structures.
  • FIG. 1 A first figure.
  • FIG.6 Creates a specific edge image! /, Generates facial appearance V (x, y), and calculates facial appearance V
  • FIG. 8 is a diagram showing a flowchart of the processing after obtaining the face-likeness Vsum to Vsum of the partial image in the face determination processing in step S6 of FIG.
  • FIG. 9 is a diagram showing a flowchart of a process for obtaining facial appearance L (E).
  • FIG. 10 is a diagram showing a configuration of a digital camera 100 that is an imaging apparatus.
  • FIG. 1 is a diagram showing an image processing apparatus according to an embodiment of the present invention.
  • the image processing apparatus is realized by the personal computer 1.
  • the personal computer 1 is connected to a digital camera 2, a recording medium 3 such as a CD-ROM, another computer 4, etc., and receives various images (image data).
  • the personal computer 1 performs the image processing described below on the provided image.
  • the computer 4 is connected via the Internet and other telecommunications lines 5.
  • the program executed by the personal computer 1 for image processing is similar to the configuration shown in FIG. 1, such as a recording medium such as a CD-ROM, other computer power via the Internet or other electric communication line, and the like.
  • a recording medium such as a CD-ROM
  • the personal computer 1 is composed of a CPU (not shown) and its peripheral circuits (not shown), and executes a program in which the CPU is installed.
  • the program may be connected to a telecommunications line, ie, a signal on a carrier wave carrying a transmission medium. Converted and sent.
  • a telecommunications line ie, a signal on a carrier wave carrying a transmission medium. Converted and sent.
  • the program is supplied as a computer readable computer program product in various forms such as a recording medium and a carrier wave.
  • the personal computer 1 performs image processing for detecting a medium facial image of a captured image. Specifically, an edge component is extracted based on the input image to generate an edge image, and it is determined whether there is a face image based on the generated edge image.
  • the processing in the present embodiment is characterized by the edge component extraction method and the face determination method based on the edge image.
  • an edge is a portion (area, pixel) where the luminance value or pixel value is smaller than the surrounding area, is larger than the surrounding area! /, And the value is protruding! /
  • the indented area (area, pixel) is called a concave edge
  • the protruding area (area, pixel) is called a convex edge.
  • FIG. 2 is a diagram showing a flowchart of an image processing program executed by the personal computer 1.
  • step S1 an image (image data) to be detected for a face photographed (captured) with a digital camera or the like is input (acquired).
  • Each pixel of the input image includes R, G, and B color components, and each color component ranges from 0 to 255.
  • step S2 a luminance image Y is generated by the following formula based on R, G, and B of the input image. That is, the luminance image Y plane is generated.
  • step S3 the generated luminance image is hierarchically reduced and output. For example, given by the reduction ratio ⁇ of 0.9 n for integer n of 0 to 3 1, and outputs the luminance image which has been reduced by the reduction magnification ⁇ of the 32 patterns.
  • a reduction method for example, Cubic scaling or linear scaling may be used. The reason why multiple reduced images are generated in this way is that it is unclear whether the input image has a face image of any size, so that it can handle face images of any size. .
  • step S4 four types of edge images E (x, y) to E (x, y) are generated by the following procedure for each reduced luminance image Y (x, y) force.
  • the x direction is the horizontal direction of the image
  • the horizontal direction and the y direction are vertical or vertical.
  • Y (x, y) (Y (x, y-l) + 2XY (x, y) + Y (x, y + 1) ⁇ / 4
  • Y (x, y) (Y (x-l, y) + 2XY (x, y) + Y (x + l, y) ⁇ / 4
  • Di-image E (x, y) is generated.
  • Each pixel of the edge image is called an edge pixel.
  • a vertical edge image E (x, y) is generated from the following equation.
  • a lateral edge image E (x, y) is generated from the following equation.
  • Min () is a function that returns the minimum value of 0.
  • ⁇ (E) is This is a clipping function that performs the following operations and outputs an integer between 0 and 31.
  • This Ml N () process is a non-linear filter process. It can also be called non-linear filter processing, including ⁇ conversion and clipping processing.
  • FIG. 3 is a diagram in which the edge extraction target pixel and the surrounding pixels are represented by coordinates xy.
  • the above E ′ (x, y) is 4 pixels in the vertical direction Y (x, y— 1), Y (x, y), Y (x, y + l),
  • E ′ (x, y) indicates that the value near the target pixel (x, y) is smaller than the value in the vertical peripheral pixel, that is, the pixel value is in the vertical direction. Indicates that it is dented from the surroundings. Therefore, the value of E (x, y) generated in this way is treated as a pixel value, and the generated image is called a vertical luminance concave image.
  • a value obtained by adding a difference in luminance value with the adjacent pixel is shown. That is, a large value is generated when the luminance value changes greatly between the adjacent pixels in the vertical direction. Therefore, the value of E (x, y) generated in this way is treated as a pixel value, and the generated image is treated as a vertically adjacent pixel.
  • the vertically adjacent pixel difference image detects the edge of the concave structure, the edge of the convex structure, and the edge of the step without distinction.
  • E (x, y) generated in this way is the horizontal luminance concave image
  • E (x, y) is the horizontal adjacent image
  • FIG. 4 is a diagram showing the result of creating the luminance concave image E (x, y) for various luminance structures.
  • Fig. 4 (a) shows the case where the luminance is concave
  • Fig. 4 (b) shows the case where the luminance is protruding
  • Fig. 4 (c) shows the case where the brightness is stepped.
  • the luminance concave image has a positive value only when the luminance is concave. Therefore, if the negative value of the luminance recess image E ′ is clipped to 0, an edge image E (x, y) that reacts only to the luminance recess is generated.
  • Fig. 5 shows the four types of edge images E (x, y) to E (x, y
  • the luminance depression image has a sharp peak at the position of the eyes and nose!
  • the vertical luminance concave image E in FIG. 5 it reacts to the eyes, nostrils, mouth, etc. Among them, it reacts strongly to the eyes, nostrils, etc. and becomes white. In other words, the value of E at that position is large. Therefore, the face can be detected with high accuracy by analyzing such a luminance concave image.
  • the reason why the edge image is gamma-converted is to convert the edge amount into an appropriate feature amount E.
  • a subtle difference in the edge amount in a place where there is almost no edge has a larger meaning than a slight difference in the amount of edge in a place where there is a large edge! /.
  • By applying gamma conversion to the edge amount the above effect is achieved. Almost no edge! /
  • the difference in edge amount at the location is converted into a large difference in feature amount E.
  • the difference in edge amount is converted into a small difference in feature amount E.
  • a face determination target area of 19 ⁇ 19 pixels is set for every other pixel of the reduced image, and a partial image of the edge image in that area is output. This is performed for all reduced images.
  • the 19 x 19 pixel face detection target area is suitable for detecting the eyes, nose, mouth, etc. with about 2 pixels when the area is a face.
  • step S 6 it is determined for each partial image of the edge image output in step 5 whether this area is a face image.
  • the determination of the face image is performed by the method described below.
  • V (x, y) is a numerical expression of the facial appearance at each pixel position, and indicates the degree and degree of facial appearance.
  • V (x, y) may be said to be a likelihood representing a degree of likelihood as a face.
  • V (x, y) L (E (x, y))
  • L (E) is described later for each pixel position (x, y) (0 ⁇ x ⁇ 18, 0 ⁇ v ⁇ 18).
  • Y represents the face likeness when the edge E (x, y) is E.
  • the generated facial appearance V (x, y) is integrated for all pixels (x, y) (0 ⁇ x ⁇ 18, 0 ⁇ y ⁇ 18) to calculate the facial appearance V.
  • FIG. 6 is a diagram illustrating an example in which the above processing is performed on a specific edge image.
  • the face-like image generated from the face edge image shown in Fig. 6 (a) has a large overall value. That is, the overall image is whitish.
  • the facial image generated from the non-facial edge image shown in Fig. 6 (b) has small values in some places. That is, the image becomes dark in some places.
  • FIG. 7 shows specific values of the lookup table L (E) for each edge size.
  • FIG. 7 the larger the face-like value, the more white it is displayed.
  • the left side is the facial appearance when the edge is small, and the right side is the facial appearance when the edge is large.
  • look-up table L (E) in Fig. 7 has a specific value for each edge size.
  • the diagram on the left represents the facial appearance when the edge is small.
  • the facial appearance of the eyes, nose and mouth is small. This means that if the edges of the eyes, nose, and mouth are small, the area is not likely to be a face. For example, in the non-face example in Fig. 6 ⁇ , the edge of the part corresponding to the nose is small, so the part does not look like a face.
  • the diagram on the right side of FIG. 7 represents the facial appearance when the edge is large.
  • the face-likeness of parts other than the eyes, nose, and mouth is small. This means that if the edge of a part other than the eyes, nose, or mouth is large, that part does not look like a face.
  • the edge of the part corresponding to the space between the eyes and both sides of the mouth is large, so the part is not likely to be a face.
  • a face image is a specific type of image and eyes, nose, mouth, and the like are characteristic elements of the specific type of image, they correspond to characteristic elements of the specific type of image.
  • the degree of the particular kind of image when the edge component of the pixel is large is smaller than the degree of the particular kind of image when the edge component is small.
  • the degree of a particular type of image when the edge component of that pixel is large is expressed as the degree of a particular type of image when the edge component is small V.
  • look-up table L (E) corresponding to the value of edge E is selected from 32 look-up tables.
  • the facial appearance Vsum of the partial image is generated based on the edge image E (x, y). Then, based on the edge images E (x, y) to E (x, y), the facial image Vsu
  • FIG. 8 shows the face-likeness Vsum of the partial image in the face determination process of step S6 of FIG. It is a figure which shows the flowchart of the process after calculating
  • facial appearance Vsum to Vsum are generated step by step
  • the face is determined.
  • the process of comparing the evaluation value with the threshold value is performed at each stage as shown in Fig. 8, so that images that are clearly not faces are excluded at an early stage and at a stage so that efficient processing can be performed.
  • step S 11 an evaluation value for determining whether or not the partial image is a face image is set as the face likelihood Vsum of the edge image E (x, y).
  • step S12 it is determined whether or not the evaluation value is larger than a predetermined threshold value th. If this evaluation value is larger than the threshold value th, the process proceeds to step S13. If this evaluation value is not larger than the threshold value thl, the partial image is a face image. If it is not an image, the face determination process for the target partial image is terminated.
  • step S13 the evaluation value is changed to the evaluation value in step S11, and the face appearance of the edge image E (x, y) is displayed.
  • step S14 this evaluation value is greater than a predetermined threshold th2.
  • step S15 If the evaluation value is greater than the threshold value th2, the process proceeds to step S15. If the evaluation value is not greater than the threshold value th2, the face determination process for the target partial image is performed assuming that the partial image is not a face image. finish.
  • step S15 the evaluation value is changed to the evaluation value in step S13, and the face appearance of the edge image E (x, y) is set.
  • step S16 this evaluation value is greater than a predetermined threshold th3.
  • step S17 If the evaluation value is greater than the threshold th3, the process proceeds to step S17. If the evaluation value is not greater than the threshold th3, the partial image is determined not to be a face image, and the face determination process for the target partial image is performed. finish.
  • step S17 the evaluation value is changed to the evaluation value in step S15, and the facial appearance of the edge image E (x, y) is displayed.
  • step S18 this evaluation value is greater than a predetermined threshold th4.
  • step S18 judges whether or not. If the evaluation value is larger than the threshold th4 in step S18, it is finally determined that the partial image is a face image. If this evaluation value is not greater than the threshold th4, the partial image is not a face image, and the face determination process for the target partial image is terminated.
  • step S 7 when it is determined in step 6 that a partial image is a face, the face size S and coordinates (X, Y) for the input image of the partial image are output.
  • the position and size of the face image are detected and output.
  • FIG. 10 is a diagram showing a flowchart of processing for obtaining the facial appearance L (E). This process is
  • step S 21 images of several hundred or more faces are acquired. That is, several hundred or more faces are photographed (captured) with a digital camera or the like, and the images (image data) are acquired.
  • the acquired image is an image composed of the same color components as the image input in step S1 of FIG.
  • step S 22 the image of the face imaged is scaled so that the size of the face area becomes 19 ⁇ 19 pixels, and the partial images obtained by cutting out the face area are set as face image sample groups.
  • step S23 several hundred patterns of non-face image sample groups of 19 X 19 pixels are acquired. This is appropriately extracted from images other than the face photographed with a digital camera and made into a non-face image sample group. It is also possible to extract from an image showing a face while avoiding the face area. In this case, the user may appropriately designate the non-face image area from the image captured on the monitor.
  • step S24 an edge component is extracted from the face image sample group to generate a face edge image sample group.
  • This process is the same as the process for generating the edge image E (x, y) in the face detection process.
  • step S25 edge components are extracted from the non-face image sample group. And a non-face edge image sample group is generated. This process is also performed in the same manner as the process for generating the edge image E (x, y) in the face detection process.
  • step S26 the frequency P (x, y, E) at which the edge of (x, y) becomes E is obtained for the face edge image sample group!
  • step S27 for the non-face edge image sample group, (x, y),
  • step S28 the facial appearance L (E) of the pixel when the edge E (x, y) at the pixel position (x, y) is E is calculated by the following equation.
  • L (E) log ⁇ (P ( ⁇ , ⁇ , ⁇ ) + 8) / ( ⁇ ( ⁇ , ⁇ , ⁇ ) + ⁇ ) ⁇
  • ⁇ and ⁇ are predetermined constants, introduced to suppress logarithmic divergence and overlearning.
  • the value of ⁇ should be set to about 1/1000 of the average value of P (x, y, E).
  • the value of ⁇ should be set to several tens of times the value of ⁇ .
  • the value increases monotonously in the direction of addition, and the value decreases monotonously in the direction of increase in the distribution of non-face image samples whose edge E (x, y) is E at the pixel position (x, y). It is a function that does.
  • the distribution of image samples is usually normal.
  • the luminance concave image has a sharp peak at the position of the eyes, nose and mouth. Therefore, the face can be detected with high accuracy by analyzing such a luminance concave image.
  • edge edge is gamma-converted.
  • image analysis a subtle difference in the edge amount in a place where there is almost no edge has a larger meaning than a slight difference in edge amount in a place where there is a large edge.
  • the difference in the edge amount at the point where there is almost no edge is converted into a large difference in the feature amount E, and the difference in the edge amount at the point where there is a large edge is the feature amount E. Is translated into a small difference.
  • the difference in edge amount matches the difference in image structure.
  • the accuracy of face determination is increased.
  • the luminance concave image has a positive value only when the luminance is concave. Therefore, in this embodiment, the negative value of the luminance recess image E ′ is clipped to 0. As a result, an edge image E (xy) that reacts only to the luminance depression is generated, and the processing using the edge image E becomes frustrating.
  • a face image can be detected by a simple and high-speed process in which pixel values of an edge image are converted into facial appearance using a lookup table and integrated. Also, by determining the edge image, there is an effect of suppressing the influence of the lighting conditions when shooting the image.
  • a luminance concave image is generated and a locally dark spot such as the eyes and nose of the face is appropriately determined.
  • the brightness of the mouth laughing while showing teeth and the nose shining with light is locally brighter than the surroundings.
  • a face image is detected more accurately than in the first embodiment by appropriately detecting a locally bright portion of the face.
  • the second embodiment is realized by the personal computer 1 as in the first embodiment. Therefore, for the configuration of the image processing apparatus of the second embodiment, refer to FIG. 1 of the first embodiment. Further, the image processing program executed by the personal computer 1 is the same as the flowchart of FIG. 2 of the first embodiment, and the processing flow will be described below with reference to FIG.
  • Steps S1 to S3 are the same as those in the first embodiment, and thus description thereof is omitted.
  • each of the reduced luminance images Y (x, y) force also generates six types of edge images E (x, y) to E (x, y).
  • Di-image E (x, y) is generated.
  • Max () is a function that returns the maximum value among 0. 7 (E) is a function similar to that of the first embodiment. Clipping processing is also performed in the same way as in the first embodiment.
  • the E (x, y) generated above is called the vertical luminance convex image, and E (x, y) is called the horizontal luminance convex image. [0073] For generation of the edge images E (x, y) and E (x, y), refer to FIG. 3 of the first embodiment.
  • E '(x, y) above is 4 pixels Y in the vertical direction on the luminance image Y (x, y) plane.
  • E ′ (x, y) indicates a positive value because the value near the target pixel (x, y)
  • E (x, y) generated in this way is referred to as a vertical luminance convex image.
  • E (x, y) generated in this way is used as the horizontal luminance convex image.
  • step S5 a 19 x 19 pixel face determination target area is set for every other pixel of the reduced image, and partial images of edge images E (x, y) to E (x, y) in that area are set. Output.
  • step S 6 it is determined for each partial image of the edge image output in step 5 whether or not this area is a face image, as in the first embodiment.
  • the facial appearance Vsum to Vsum of the partial image is generated based on the images E (x, y) to E (x, y)
  • step S7 when it is determined in step 6 that a partial image is a face, the size S and coordinates (X, Y) of the face with respect to the input image of the partial image are set in the first implementation.
  • S S '/ ⁇
  • the position and size of the face image are detected and output.
  • Luminance is brighter locally in the mouth laughing while showing teeth, and in the nose when it shines when exposed to light. According to the present embodiment, such a locally bright spot is effectively detected to create an edge image. Therefore, if the edge image created in this way is used in the same manner as the determination of the face image by the concave image in the first embodiment, in addition to the locally dark portion of the face image, the face image It is possible to perform face determination considering local bright spots. As a result, the face can be detected with higher accuracy than in the first embodiment.
  • a luminance convex image is generated in addition to the luminance concave image, and in addition to a locally dark spot such as the eyes, nose and mouth of the face, a mouth laughing with a tooth or a light hits it.
  • a locally dark spot such as the eyes, nose and mouth of the face, a mouth laughing with a tooth or a light hits it.
  • a nose that is shining the example in which the location where the brightness is locally brighter than the surroundings is also detected appropriately.
  • the third embodiment an example will be described in which information on the luminance concave portion image and the luminance convex portion image is combined into a luminance uneven portion image and processed.
  • the third embodiment is realized by the personal computer 1 as in the first embodiment. Therefore, for the configuration of the image processing apparatus of the third embodiment, refer to FIG. 1 of the first embodiment.
  • the image processing program executed by the personal computer 1 is the same as the flow chart of FIG. 2 used in the first embodiment and referenced in the second embodiment. Similarly, the following explanation will be made with reference to FIG.
  • Steps S1 to S3 are the same as those in the second embodiment, and thus description thereof is omitted.
  • step S4 edge images E (x, y) to E (x, y) are generated as in the second embodiment. To do. Then, based on the following equation, longitudinal luminance unevenness portion image E 7 (x, y) and transverse luminance concave protrusions image E (x, y) to produce a.
  • step S5 a face determination target area of 19 X 19 pixels is set every other pixel of the reduced image, and edge images E (x, y), E (x, y), E (x , Y), E (x, y)
  • step S6 the edge images E (x, y), E (x, y), E (x, y), ⁇ (x
  • the process of generating m to Vsum is also a partial image based on the edge images E (x, y) to E (x, y)
  • This concept may be replaced with a concept combining concave and convex portions.
  • Step S7 is the same as in the first embodiment. As described above, when a face image is included in the input image, the position and size of the face image are detected and output.
  • the edge image output in step 5 may be subjected to a known learning discrimination process such as a neural network to determine whether this region is a face image.
  • a facial expression determination process may be performed by applying a known technique to the edge image in the detected face image area.
  • it is detected that the brightness of the mouth of the face that is laughing while showing teeth is locally high, so such a smile is determined with high accuracy. be able to.
  • the edge detection filter that generates the vertical luminance concave image may be as follows.
  • the target pixel and three pixels adjacent in the vertical direction may be used.
  • the edge detection filter that generates the vertical luminance concave image may be as follows.
  • a plurality of different sizes may be used as the size of the filter for creating the luminance concave image or luminance convex image, and a convex structure or a concave structure in a plurality of frequency bands may be detected.
  • convex structures or concave structures in a plurality of frequency bands may be detected by calculating filters of the same size for a plurality of luminance images with different reduction magnifications.
  • the personal computer 1 performs the image processing for detecting the face image from the photographed image.
  • the above-described processing may be performed on the captured image in an imaging apparatus such as a digital still camera.
  • FIG. 10 is a diagram showing a configuration of a digital camera 100 that is such an imaging apparatus.
  • the digital camera 100 includes a photographing lens 102, an image sensor 103 including a CCD, a control device 104 including a CPU and peripheral circuits, a memory 105, and the like.
  • the image sensor 103 captures (captures) the subject 101 via the photographing lens 102 and outputs the captured image data to the control device 104.
  • the control device 104 performs image processing for detecting the face image described above on the image (image data) captured by the image sensor 103. Then, the control device 104 performs white balance adjustment and various other image processing on the image captured based on the detection result of the face image! /, And stores the image data after the image processing in the appropriate memory 105. Store. Further, the control device 104 can also use the detection result of the face image for autofocus processing or the like.
  • the image processing program executed by the control device 104 is stored in a ROM (not shown).
  • the processing described above can also be applied to a video camera. Furthermore, it can also be applied to surveillance cameras that monitor suspicious individuals and devices that identify individuals based on captured face images and estimate gender, age, and facial expressions. That is, the present invention can be applied to all devices such as an image processing device and an imaging device that extract and process a specific type of image such as a face image.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Human Computer Interaction (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Image Processing (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un procédé de traitement d'image comprend : l'acquisition d'une image se composant d'une pluralité de pixels; la détection, sur la base de l'image acquise, du bord d'un évidement dont les valeurs de pixels sont localement plus petites que celles de la périphérie de l'évidement; et la génération d'une image de bord sur la base du bord de l'évidement.
PCT/JP2007/065447 2006-08-08 2007-08-07 Procédé, appareil et programme de traitement d'image, et appareil de prise d'image Ceased WO2008018460A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2006215944A JP2009258771A (ja) 2006-08-08 2006-08-08 画像処理方法、画像処理装置、画像処理プログラム、撮像装置
JP2006-215944 2006-08-08

Publications (1)

Publication Number Publication Date
WO2008018460A1 true WO2008018460A1 (fr) 2008-02-14

Family

ID=39032990

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2007/065447 Ceased WO2008018460A1 (fr) 2006-08-08 2007-08-07 Procédé, appareil et programme de traitement d'image, et appareil de prise d'image

Country Status (2)

Country Link
JP (1) JP2009258771A (fr)
WO (1) WO2008018460A1 (fr)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005038119A (ja) * 2003-07-18 2005-02-10 Canon Inc 画像処理装置および方法
JP2006013988A (ja) * 2004-06-28 2006-01-12 Sony Corp イメージセンサ
JP2006092095A (ja) * 2004-09-22 2006-04-06 Sony Corp 画像処理装置および方法、並びにプログラム

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005038119A (ja) * 2003-07-18 2005-02-10 Canon Inc 画像処理装置および方法
JP2006013988A (ja) * 2004-06-28 2006-01-12 Sony Corp イメージセンサ
JP2006092095A (ja) * 2004-09-22 2006-04-06 Sony Corp 画像処理装置および方法、並びにプログラム

Also Published As

Publication number Publication date
JP2009258771A (ja) 2009-11-05

Similar Documents

Publication Publication Date Title
EP1058909B1 (fr) Nouvelle determination perceptive de seuil pour detection localisee de contours sur la base de gradients
KR101662846B1 (ko) 아웃 포커싱 촬영에서 빛망울 효과를 생성하기 위한 장치 및 방법
KR101390756B1 (ko) 안면 특징 검출 방법 및 장치
CN103098078B (zh) 笑容检测系统及方法
US20160307324A1 (en) Image processing apparatus, image processing method, and storage medium for lighting processing on image using model data
US20140079319A1 (en) Methods for enhancing images and apparatuses using the same
JP2004357277A (ja) デジタル画像処理方法
KR20150024769A (ko) 촬상 장치 및 그 촬상 방법, 화상 처리 장치 및 그 화상 처리 방법, 및, 프로그램
CN101828201A (zh) 图像处理装置及方法、学习装置及方法以及程序
TWI632894B (zh) 動態影像之心率活動偵測系統與其方法
JP2012008100A (ja) 画像処理装置、画像処理方法及び画像処理プログラム
JP6352547B2 (ja) 画像処理装置及び画像処理方法
JP2004310475A (ja) 画像処理装置、画像処理を行う携帯電話、および画像処理プログラム
WO2011067755A1 (fr) Procédé et système pour récupérer automatiquement la chromaticité et la variation d'image de régions d'images de couleur écrêtées
JP5437414B2 (ja) 画像処理装置、画像処理方法、プログラムおよび記録媒体
US11315360B2 (en) Live facial recognition system and method
JP2009123081A (ja) 顔検出方法及び撮影装置
CN112001853A (zh) 图像处理设备、图像处理方法、摄像设备和存储介质
JP6527765B2 (ja) シワ状態分析装置及びシワ状態分析方法
KR20120117604A (ko) 피부 상태 분석 방법 및 장치
JP5286215B2 (ja) 輪郭抽出装置、輪郭抽出方法、および輪郭抽出プログラム
WO2008018459A1 (fr) Procédé de traitement d'image, appareil de traitement d'image, programme de traitement d'image, et appareil de prise d'image
JP5203159B2 (ja) 画像処理方法、画像処理システムおよび画像処理プログラム
Fathy et al. Benchmarking of pre-processing methods employed in facial image analysis
JP2005326323A (ja) 画質検査装置

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: 07792116

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

NENP Non-entry into the national phase

Ref country code: RU

NENP Non-entry into the national phase

Ref country code: JP

122 Ep: pct application non-entry in european phase

Ref document number: 07792116

Country of ref document: EP

Kind code of ref document: A1