Sparse Occlusion Detection with Optical Flow

Ayvaci, Alper; Raptis, Michalis; Soatto, Stefano

doi:10.1007/s11263-011-0490-7

Cited by 96 publications

(131 citation statements)

References 39 publications

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“…We use the method of Ayvaci et al [5] for estimating disparities. It performs a search in a 2D window, and thus can deal with small vertical displacements.…”

Section: Methodsmentioning

confidence: 99%

Pose Estimation and Segmentation of People in 3D Movies

Alahari

Seguin

Šivic

et al. 2013

2013 IEEE International Conference on Computer Vision

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We seek to obtain a pixel-wise segmentation and pose estimation of multiple people in a stereoscopic video. This involves challenges such as dealing with unconstrained stereoscopic video, non-stationary cameras, and complex indoor and outdoor dynamic scenes. The contributions of our work are two-fold: First, we develop a segmentation model incorporating person detection, pose estimation, as well as colour, motion, and disparity cues. Our new model explicitly represents depth ordering and occlusion. Second, we introduce a stereoscopic dataset with frames extracted from feature-length movies "StreetDance 3D" and "Pina". The dataset contains 2727 realistic stereo pairs and includes annotation of human poses, person bounding boxes, and pixel-wise segmentations for hundreds of people. The dataset is composed of indoor and outdoor scenes depicting multiple people with frequent occlusions. We demonstrate results on our new challenging dataset, as well as on the H2view dataset from (Sheasby et al. ACCV 2012).

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“…We use the method of Ayvaci et al [5] for estimating disparities. It performs a search in a 2D window, and thus can deal with small vertical displacements.…”

Section: Methodsmentioning

confidence: 99%

Pose Estimation and Segmentation of People in 3D Movies

Alahari

Seguin

Šivic

et al. 2013

2013 IEEE International Conference on Computer Vision

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“…There, individual pixels were eliminated from the region-based segmentation terms to prevent skewing and misleading the segmentation. This method is related to similar approaches in occlusion detection in optical flow [1] and salt-and-pepper denoising [73]. Here, the goal is to isolate defects and artifacts from interfering with the regular modes.…”

Section: Artifact Indicator Functionmentioning

confidence: 99%

“…-have smooth or sharp boundaries (with or without TV/L 1 terms on A k ), -overlap or form a partition of the domain (image segmentation), -be essentially wavelike (single mode) or crystalline (coupled submodes), -reconstruct the input image exactly or up to Gaussian noise, -identify outlier pixels/regions and inpaint them.…”

Section: Colloidal Imagementioning

confidence: 99%

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Two-Dimensional Compact Variational Mode Decomposition

et al. 2017

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Decomposing multidimensional signals, such as images, into spatially compact, potentially overlapping modes of essentially wavelike nature makes these components accessible for further downstream analysis. This decomposition enables space-frequency analysis, demodulation, estimation of local orientation, edge and corner detection, texture analysis, denoising, inpainting, or curvature estimation. Our model decomposes the input signal into modes with narrow Fourier bandwidth; to cope with sharp region boundaries, incompatible with narrow bandwidth, we introduce binary support functions that act as masks on the narrow-band mode for image recomposition. L 1 and TV terms promote sparsity and spatial compactness. Constraining the support functions to partitions of the signal domain, we effectively get an image segmentation model based on spectral homogeneity. By coupling several submodes together with a single support function, we are able to decompose an image into several crystal grains. Our efficient algorithm is based on variable splitting and alternate direction optimization; we employ Merriman-Bence-Osher-like threshold dynamics to handle efficiently the motion by mean curvature of the support function boundaries under the sparsity promoting terms. The versatility and effectiveness of our proposed model is demonstrated on a broad variety of example images from different modalities. These demonstrations include the decomposition of images into overlapping modes with smooth or sharp boundaries, segmentation of images of crystal grains, and inpainting of damaged image regions through artifact detection.

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“…These errors are propagated to novel views causing incorrect confidences in the appearance change. Some of these issues can be tackled by more robust motion estimation methods which explicitly model occlusion phenomena [30] or more advanced stereo reconstruction techniques. Availability of accurate 3D model would improve the accuracy of the reprojection stage [6].…”

Section: Pairwise Termmentioning

confidence: 99%

Detecting Changes in Images of Street Scenes

Košecká

2013

Computer Vision – ACCV 2012

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Abstract. In this paper we propose an novel algorithm for detecting changes in street scenes when the vehicle revisits sections of the street at different times. The proposed algorithm detects structural geometric changes, changes due to dynamically moving objects and as well as changes in the street appearance (e.g. posters put up) between two traversal times. We exploit geometric, appearance and semantic information to determine which areas have changed and formulate the problem as an optimal image labeling problem in the Markov Random Field framework. The approach is evaluated on street sequences from 3 different locations which were visited multiple times by the vehicle. The proposed method is applicable to monitoring and updating models and images of urban environments.

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Sparse Occlusion Detection with Optical Flow

Cited by 96 publications

References 39 publications

Pose Estimation and Segmentation of People in 3D Movies

Pose Estimation and Segmentation of People in 3D Movies

Two-Dimensional Compact Variational Mode Decomposition

Detecting Changes in Images of Street Scenes

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