The MPM-MAP algorithm for motion segmentation
Computer Vision and Image Understanding
Tracking based motion segmentation under relaxed statistical assumptions
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion segmentation using inertial sensors
Proceedings of the 2006 ACM international conference on Virtual reality continuum and its applications
An a contrario Decision Framework for Region-Based Motion Detection
International Journal of Computer Vision
Motion representation using composite energy features
Pattern Recognition
Anisotropic virtual electric field for active contours
Pattern Recognition Letters
Extraction and temporal segmentation of multiple motion trajectories in human motion
Image and Vision Computing
Tracking based motion segmentation under relaxed statistical assumptions
Computer Vision and Image Understanding
Taxonomy of directing semantics for film shot classification
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Step-by-step description of lateral interaction in accumulative computation
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Motion objects segmentation using a new level set based method
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
Rough set based image segmentation of video sequences
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
2D motion description and contextual motion analysis: issues and new models
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
3D Video Based Segmentation and Motion Estimation with Active Surface Evolution
Journal of Signal Processing Systems
| Hi-index | 0.01 |
Segmentation of motion in an image sequence is one of the most challenging problems in image processing, while at the same time one that finds numerous applications. To date, a wealth of approaches to motion segmentation have been proposed. Many of them suffer from the local nature of the models used. Global models, such as those based on Markov random fields, perform, in general, better. In this paper, we propose a new approach to motion segmentation that is based on a global model. The novelty of the approach is twofold. First, inspired by recent work of other researchers we formulate the problem as that of region competition, but we solve it using the level set methodology. The key features of a level set representation, as compared to active contours, often used in this context, are its ability to handle variations in the topology of the segmentation and its numerical stability. The second novelty of the paper is the formulation in which, unlike in many other motion segmentation algorithms, we do not use intensity boundaries as an accessory; the segmentation is purely based on motion. This permits accurate estimation of motion boundaries of an object even when its intensity boundaries are hardly visible. Since occasionally intensity boundaries may prove beneficial, we extend the formulation to account for the coincidence of motion and intensity boundaries. In addition, we generalize the approach to multiple motions. We discuss possible discretizations of the evolution (PDE) equations and we give details of an initialization scheme so that the results could be duplicated. We show numerous experimental results for various formulations on natural images with either synthetic or natural motion.