Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Hierarchical Image Analysis Using Irregular Tessellations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of information theory
Elements of information theory
Statistical Approaches to Feature-Based Object Recognition
International Journal of Computer Vision
Recognition of Articulated and Occluded Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Variational Methods for Graphical Models
Machine Learning
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Partially Occluded Object Recognition Using Statistical Models
International Journal of Computer Vision
M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
Combining Belief Networks and Neural Networks for Scene Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reconstruction of Planar Surfaces Behind Occlusions in Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Trees: A Structured Variational Method Giving Efficient Propagation Rules
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Image Modeling with Position-Encoding Dynamic Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Dynamic Trees for Unsupervised Segmentation and Matching of Image Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the computational rationale for generative models
Computer Vision and Image Understanding
Learning appearance and transparency manifolds of occluded objects in layers
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Information Theory
Statistical modeling and conceptualization of visual patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surfaces with occlusions from layered stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
An overlapping tree approach to multiscale stochastic modeling and estimation
IEEE Transactions on Image Processing
Discrete Markov image modeling and inference on the quadtree
IEEE Transactions on Image Processing
Multiscale methods for the segmentation and reconstruction of signals and images
IEEE Transactions on Image Processing
Multiscale Bayesian segmentation using a trainable context model
IEEE Transactions on Image Processing
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
A multiscale random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
Guest Editorial: Generative model based vision
Computer Vision and Image Understanding
On the computational rationale for generative models
Computer Vision and Image Understanding
| Hi-index | 0.00 |
This paper addresses the problem of object detection and recognition in complex scenes, where objects are partially occluded. The approach presented herein is based on the hypothesis that a careful analysis of visible object details at various scales is critical for recognition in such settings. In general, however, computational complexity becomes prohibitive when trying to analyze multiple sub-parts of multiple objects in an image. To alleviate this problem, we propose a generative-model framework-namely, dynamic tree-structure belief networks (DTSBNs). This framework formulates object detection and recognition as inference of DTSBN structure and image-class conditional distributions, given an image. The causal (Markovian) dependencies in DTSBNs allow for design of computationally efficient inference, as well as for interpretation of the estimated structure as follows: each root represents a whole distinct object, while children nodes down the sub-tree represent parts of that object at various scales. Therefore, within the DTSBN framework, the treatment and recognition of object parts requires no additional training, but merely a particular interpretation of the tree/subtree structure. This property leads to a strategy for recognition of objects as a whole through recognition of their visible parts. Our experimental results demonstrate that this approach remarkably outperforms strategies without explicit analysis of object parts.