Shen et al., 2010 - Google Patents
Sparsity model for robust optical flow estimation at motion discontinuitiesShen et al., 2010
View PDF- Document ID
- 11508020919350303809
- Author
- Shen X
- Wu Y
- Publication year
- Publication venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
External Links
Snippet
This paper introduces a new sparsity prior to the estimation of dense flow fields. Based on this new prior, a complex flow field with motion discontinuities can be accurately estimated by finding the sparsest representation of the flow field in certain domains. In addition, a …
- 230000003287 optical 0 title description 23
Classifications
-
- 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
- G06T2207/20172—Image enhancement details
- G06T2207/20182—Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
-
- 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
- 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
- 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
-
- 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
- G06T2207/10016—Video; Image sequence
-
- 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
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/40—Scaling the whole image or part thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
- G06T5/001—Image restoration
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Elad et al. | Image denoising: The deep learning revolution and beyond—a survey paper | |
| Li et al. | A simple baseline for video restoration with grouped spatial-temporal shift | |
| Liu et al. | Depth super-resolution via joint color-guided internal and external regularizations | |
| Baker et al. | Hallucinating faces | |
| Shen et al. | Sparsity model for robust optical flow estimation at motion discontinuities | |
| Sevilla-Lara et al. | Optical flow estimation with channel constancy | |
| Peng et al. | Pdrf: progressively deblurring radiance field for fast scene reconstruction from blurry images | |
| Liu et al. | Video frame interpolation via optical flow estimation with image inpainting | |
| Li et al. | Coarse-to-fine PatchMatch for dense correspondence | |
| Rochefort et al. | An improved observation model for super-resolution under affine motion | |
| Rai et al. | Robust face hallucination algorithm using motion blur embedded nearest proximate patch representation | |
| Chen et al. | Fast optical flow estimation based on the split bregman method | |
| Viola et al. | Marigold-dc: Zero-shot monocular depth completion with guided diffusion | |
| Chen et al. | Optical flow estimation based on the frequency-domain regularization | |
| Fortun et al. | Fast piecewise-affine motion estimation without segmentation | |
| Su et al. | Super-resolution without dense flow | |
| Ning et al. | Multi-frame super-resolution algorithm based on a WGAN | |
| Al Ismaeil et al. | Real-time enhancement of dynamic depth videos with non-rigid deformations | |
| Zhang et al. | A variational Retinex model with structure-awareness regularization for single-image low-light enhancement | |
| Drozdov et al. | Robust recovery of heavily degraded depth measurements | |
| Van Vo et al. | High dynamic range video synthesis using superpixel-based illuminance-invariant motion estimation | |
| Rawat et al. | Gaussian kernel filtering for video stabilization | |
| Xu et al. | TKO‐SLAM: Visual SLAM algorithm based on time‐delay feature regression and keyframe pose optimization | |
| Nawaz et al. | Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements | |
| Szeliski | Motion estimation |