[go: up one dir, main page]

Niu et al., 2020 - Google Patents

Boundary-aware RGBD salient object detection with cross-modal feature sampling

Niu et al., 2020

Document ID
6070836602662117200
Author
Niu Y
Long G
Liu W
Guo W
He S
Publication year
Publication venue
IEEE Transactions on Image Processing

External Links

Snippet

Mobile devices usually mount a depth sensor to resolve ill-posed problems, like salient object detection on cluttered background. The main barrier of exploring RGBD data is to handle the information from two different modalities. To cope with this problem, in this paper …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6201—Matching; Proximity measures
    • G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00268—Feature extraction; Face representation
    • G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled

Similar Documents

Publication Publication Date Title
Huo et al. Efficient context-guided stacked refinement network for RGB-T salient object detection
Wang et al. Adaptive fusion for RGB-D salient object detection
US12266211B2 (en) Forgery detection of face image
Zhang et al. Adaptive differentiation siamese fusion network for remote sensing change detection
CN111291809B (en) Processing device, method and storage medium
US11755889B2 (en) Method, system and apparatus for pattern recognition
CN111027576B (en) Co-saliency detection method based on co-saliency generative adversarial network
Yan et al. ASNet: Adaptive semantic network based on transformer–CNN for salient object detection in optical remote sensing images
CN110516536A (en) A Weakly Supervised Video Behavior Detection Method Based on the Complementation of Temporal Category Activation Maps
Kim et al. Deep stereo confidence prediction for depth estimation
Niu et al. Boundary-aware RGBD salient object detection with cross-modal feature sampling
CN110781744A (en) A small-scale pedestrian detection method based on multi-level feature fusion
Li et al. Robust deep neural networks for road extraction from remote sensing images
CN110827265B (en) Image anomaly detection method based on deep learning
CN105205453A (en) Depth-auto-encoder-based human eye detection and positioning method
Zhu et al. Supplement and suppression: Both boundary and nonboundary are helpful for salient object detection
Zhu et al. S $^ 3$ Net: Self-Supervised Self-Ensembling Network for Semi-Supervised RGB-D Salient Object Detection
Zong et al. A cascaded refined rgb-d salient object detection network based on the attention mechanism
Huang et al. Progressive context-aware dynamic network for salient object detection in optical remote sensing images
Xia et al. Surface Defect Detector Based on Deformable Convolution and Lightweight Multi‐Scale Attention
CN117830889A (en) Video saliency detection method, system and storage medium based on optical flow graph feature fusion mechanism
Madi et al. CNN-LPQ: convolutional neural network combined to local phase quantization based approach for face anti-spoofing
Chen et al. A Multi-Dimensional Attention Feature FusionMethod for Pedestrian Re-identification.
CN117409482A (en) Fine granularity action recognition and posture detection method based on attention mechanism
Wang et al. Transmission Line Detection Through Auxiliary Feature Registration With Knowledge Distillation