Human image understanding: recent research and a theory
Papers from the second workshop Vol. 13 on Human and Machine Vision II
Model-based object recognition by geometric hashing
ECCV 90 Proceedings of the first european conference on Computer vision
ACM Computing Surveys (CSUR)
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in image analysis
Markov random field modeling in image analysis
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Object Recognition Using Multidimensional Receptive Field Histograms
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Class-Specific, Top-Down Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Feature-based cluster segmentation of image sequences
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Shape Matching and Object Recognition Using Shape Contexts
Shape Matching and Object Recognition Using Shape Contexts
A Cubist Approach to Object Recognition
A Cubist Approach to Object Recognition
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Multidimensional Morphable Models
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Spatially coherent clustering using graph cuts
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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In this contribution, we describe an object detection method that jointly considers low-level features and higher-level object knowledge. The method partitions a stereo image sequence into its most prominent moving groups with similar 3-dimensional (3D) motion and of consistent object-specific appearance. Image segmentation is performed by a Bayesian Maximum a Posteriori estimator assigning the most probable motion profile to each image point. The motion profiles of the elaborated motion models are iteratively refined by an object tracking procedure. Additionally, the probability of salient points to belong to an object category is considered in the probabilistic framework. Our expectation on spatial continuity of objects is expressed in a Markov Random Field (MRF) model.