Symbolic Construction of a 2-D Scale-Space Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Color Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Rotation-invariant pattern matching using wavelet decomposition
Pattern Recognition Letters
Graph-theoretical methods in computer vision
Theoretical aspects of computer science
On the Representation and Matching of Qualitative Shape at Multiple Scales
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Qualitative Multi-scale Feature Hierarchies for Object Tracking
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
A Multi-scale Feature Likelihood Map for Direct Evaluation of Object Hypotheses
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Graph-Theoretical Methods in Computer Vision
Theoretical Aspects of Computer Science, Advanced Lectures [First Summer School on Theoretical Aspects of Computer Science, Tehran, Iran, July 2000]
Fast normalized cross correlation for defect detection
Pattern Recognition Letters
The representation and matching of categorical shape
Computer Vision and Image Understanding
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
CAe '12 Proceedings of the Eighth Annual Symposium on Computational Aesthetics in Graphics, Visualization, and Imaging
Multiscale Symmetric Part Detection and Grouping
International Journal of Computer Vision
Hi-index | 0.16 |
One approach to pattern classification is to match a structural description of a pattern to models which describe the structural properties of pattern classes. The central problem in structural pattern matching is to determine the correspondence between the symbols which comprise a model and symbols which describe a pattern. The difficulty of determining this correspondence depends critically on the representation that is used to describe patterns. This correspondence presents a probabilistic representation for structural models of pattern classes. Both pattern descriptions and models for pattern classes are based on symbols which represent grayscale information at multiple resolutions. A pattern description is given by a tree of symbols with attribute values. Structural models are represented by a tree of symbols with probabilistic attributes. The position and scale (resolution) of the symbols, as well as other ``features,'' are represented by these attributes. An algorithm is presented for determining the correspondence between symbols in a description of a pattern and symbols in a model of a pattern class. This algorithm uses the connectivity between symbols at different scales to constrain the search for correspondence. An interactive training program for learning models of pattern classes is described, and some conclusions from the work are presented.