Li et al., 2015 - Google Patents
Visual tracking via random walks on graph modelLi et al., 2015
- Document ID
- 12993642770223195975
- Author
- Li X
- Han Z
- Wang L
- Lu H
- Publication year
- Publication venue
- IEEE transactions on Cybernetics
External Links
Snippet
In this paper, we formulate visual tracking as random walks on graph models with nodes representing superpixels and edges denoting relationships between superpixels. We integrate two novel graphs with the theory of Markov random walks, resulting in two Markov …
- 230000000007 visual effect 0 title abstract description 23
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
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
- G06K9/6203—Shifting or otherwise transforming the patterns to accommodate for positional errors
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30048—Heart; Cardiac
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
-
- 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
-
- 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
-
- 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
- G06T2207/20112—Image segmentation 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
-
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Visual tracking via random walks on graph model | |
| Zhuang et al. | Visual tracking via discriminative sparse similarity map | |
| Dong et al. | Sub-Markov random walk for image segmentation | |
| Yang et al. | SiamAtt: Siamese attention network for visual tracking | |
| Pishchulin et al. | Deepcut: Joint subset partition and labeling for multi person pose estimation | |
| Gao et al. | Transfer learning based visual tracking with gaussian processes regression | |
| Wang et al. | Robust visual tracking via least soft-threshold squares | |
| Chen et al. | Learning linear regression via single-convolutional layer for visual object tracking | |
| Khan et al. | A review of human pose estimation from single image | |
| Zhang et al. | UAST: Uncertainty-aware Siamese tracking | |
| Kumar et al. | A novel approach for multi-cue feature fusion for robust object tracking | |
| Johnson et al. | Combining discriminative appearance and segmentation cues for articulated human pose estimation | |
| Lunayach et al. | Fsd: Fast self-supervised single rgb-d to categorical 3d objects | |
| Lu et al. | Visual tracking via probabilistic hypergraph ranking | |
| He et al. | A semantic segmentation algorithm for fashion images based on modified mask RCNN | |
| Wang et al. | Swimmer’s posture recognition and correction method based on embedded depth image skeleton tracking | |
| Heber et al. | Segmentation-based tracking by support fusion | |
| Yang et al. | Image segmentation and proto-objects detection based visual tracking | |
| Ruan et al. | Object tracking via online trajectory optimization with multi-feature fusion | |
| Chen et al. | SGD-SLAM: a visual SLAM system with a dynamic feature rejection strategy combining semantic and geometric information for dynamic environments | |
| Hu et al. | Crowd R-CNN: An object detection model utilizing crowdsourced labels | |
| Chang et al. | Fast Online Upper Body Pose Estimation from Video. | |
| CN109101872A (en) | A kind of generation method of 3D gesture mouse | |
| Yang et al. | Learning online structural appearance model for robust object tracking | |
| Kim et al. | Adaptive directional walks for pose estimation from single body depths |