Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Occlusions and binocular stereo
International Journal of Computer Vision
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Dense structure from a dense optical flow sequence
Computer Vision and Image Understanding
A Multibody Factorization Method for Independently Moving Objects
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Statistical Edge Detection: Learning and Evaluating Edge Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dense Structure-from-Motion: An Approach Based on Segment Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Bayesian inference of visual motion boundaries
Exploring artificial intelligence in the new millennium
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex Optimization
Recursive Estimation of 3D Motion and Surface Structure from Local Affine Flow Parameters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Consistent Segmentation for Optical Flow Estimation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
On the Spatial Statistics of Optical Flow
International Journal of Computer Vision
Occlusion Boundaries from Motion: Low-Level Detection and Mid-Level Reasoning
International Journal of Computer Vision
Make3D: Learning 3D Scene Structure from a Single Still Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detachable object detection with efficient model selection
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Sparse Occlusion Detection with Optical Flow
International Journal of Computer Vision
Silhouette-aware warping for image-based rendering
EGSR'11 Proceedings of the Twenty-second Eurographics conference on Rendering
Efficient closed-form solution to generalized boundary detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
2.1 depth estimation of frames in image sequences using motion occlusions
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Hi-index | 0.00 |
We address the problem of detecting occlusion boundaries from motion sequences, which is important for motion segmentation, estimating depth order, and related tasks. Previous work by Stein and Hebert has addressed this problem and obtained good results on a benchmarked dataset using two-dimensional image cues, motion estimation, and a global boundary model [1]. In this paper we describe a method for detecting occlusion boundaries which uses depth cues and local segmentation cues. More specifically, we show that crude scaled estimates of depth, which we call pseudo-depth, can be extracted from motion sequences containing a small number of image frames using standard SVD factorization methods followed by weak smoothing using a Markov Random Field defined over super-pixels. We then train a classifier for occlusion boundaries using pseudo-depth and local static boundary cues (adding motion cues only gives slightly better results). We evaluate performance on Stein and Hebert's dataset and obtain results of similar average quality which are better in the low recall/high precision range. Note that our cues and methods are different from [1] - in particular we did not use their sophisticated global boundary model - and so we conjecture that a unified approach would yield even better results.