Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Active shape models—their training and application
Computer Vision and Image Understanding
An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Approaches to Feature-Based Object Recognition
International Journal of Computer Vision
Prior Learning and Gibbs Reaction-Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient deformable template detection and localization without user initialization
Computer Vision and Image Understanding
Probability Models for Clutter in Natural Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Object Localisation in Images
International Journal of Computer Vision
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Recognition of Planar Object Classes
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A Probabilistic Contour Discriminant for Object Localisation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Strings: Variational Deformable Models of Multivariate Continuous Boundary Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of Planar Shapes Using Geodesic Paths on Shape Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic target recognition organized via jump-diffusion algorithms
IEEE Transactions on Image Processing
A Study of Parts-Based Object Class Detection Using Complete Graphs
International Journal of Computer Vision
Spine detection and labeling using a parts-based graphical model
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Learning of graphical models and efficient inference for object class recognition
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Hi-index | 0.00 |
This paper presents a method for object recognition once parts have been detected. The recognition task is formulated as a graph problem searching for the characteristic geographical arrangements of (possibly missing) parts. The objective function is Bayesian maximum a posteriori estimation, integrating the image likelihood as a posteriori probability of the part detectors. The variability in the arrangement of object parts is captured by a Gaussian distribution after translation normalization. By employing two special properties of a Gaussian distribution, we are able to deal with missing parts situation where the chosen origin is not detected. We use an A^* algorithm to find the optimal solution for the graph search problem. Experiments are performed on both synthetic and real data to demonstrate good results and fast performance of the recognition.