Cognitive science: an introduction
Cognitive science: an introduction
Perception as Bayesian inference
Perception as Bayesian inference
Spatiotemporal Segmentation Based on Region Merging
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Framework for Spatio-Temporal Video Representation & Indexing
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Combining Top-Down and Bottom-Up Segmentation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
Integration of form and motion within a generative model of visual cortex
Neural Networks - 2004 Special issue Vision and brain
Image Parsing: Unifying Segmentation, Detection, and Recognition
International Journal of Computer Vision
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Dynamic Appearance Modeling for Human Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Automatic video object segmentation using volume growing and hierarchical clustering
EURASIP Journal on Applied Signal Processing
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
Unsupervised video segmentation based on watersheds and temporal tracking
IEEE Transactions on Circuits and Systems for Video Technology
Video object segmentation using Bayes-based temporal tracking and trajectory-based region merging
IEEE Transactions on Circuits and Systems for Video Technology
A Bayesian approach to video object segmentation via merging 3-D watershed volumes
IEEE Transactions on Circuits and Systems for Video Technology
Background motion, clutter, and the impact on virtual object motion perception in augmented reality
JVRC '13 Proceedings of the 5th Joint Virtual Reality Conference
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We review recent biological vision studies that are related to human motion segmentation. Our goal is to develop a practically plausible computational framework that is guided by recent cognitive and psychological studies on the human visual system for the segmentation of human body in a video sequence. Specifically, we discuss the roles and interactions of bottom-up and top-down processes in visual perception processing as well as how to combine them synergistically in one computational model to guide human motion segmentation. We also examine recent research on biological movement perception, such as neural mechanisms and functionalities for biological movement recognition and two major psychological tracking theories. We attempt to develop a comprehensive computational model that involves both bottom-up and top-down processing and is deeply inspired by biological motion perception. According to this model, object segmentation, motion estimation, and action recognition are results of recurrent feedforward (bottom-up) and feedback (top-down) processes. Some open technical questions are also raised and discussed for future research.