Convexity rule for shape decomposition based on discrete contour evolution
Computer Vision and Image Understanding
Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Shape Histograms for Similarity Search and Classification in Spatial Databases
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Classification of Contour Shapes Using Class Segment Sets
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Efficient Shape Matching Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex Grouping Combining Boundary and Region Information
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Proceedings of the 13th annual ACM international conference on Multimedia
Partial Elastic Matching of Time Series
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
New EM derived from Kullback-Leibler divergence
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Globally Optimal Grouping for Symmetric Boundaries
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
2D shape classification and retrieval
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Optimal partial shape similarity
Image and Vision Computing
Contour grouping with prior models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distance sets for shape filters and shape recognition
IEEE Transactions on Image Processing
Contour Extraction Using Particle Filters
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
A Pragmatic Approach for Qualitative Shape and Qualitative Colour Similarity Matching
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Characterization of contour regularities based on the Levenshtein edit distance
Pattern Recognition Letters
Computational-geometry approach to digital image contour extraction
Transactions on computational science XIII
Circle detection using electro-magnetism optimization
Information Sciences: an International Journal
Journal of Visual Communication and Image Representation
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Due to distortion, noise, segmentation errors, overlap, and occlusion of objects in digital images, it is usually impossible to extract complete object contours or to segment the whole objects. However, in many cases parts of contours can be correctly reconstructed either by performing edge grouping or as parts of boundaries of segmented regions. Therefore, recognition of objects based on their contour parts seems to be a promising as well as a necessary research direction. The main contribution of this paper is a system for detection and recognition of contour parts in digital images. Both detection and recognition are based on shape similarity of contour parts. For each contour part produced by contour grouping, we use shape similarity to retrieve the most similar contour parts in a database of known contour segments. A shape-based classification of the retrieved contour parts performs then a simultaneous detection and recognition. An important step in our approach is the construction of the database of known contour segments. First complete contours of known objects are decomposed into parts using discrete curve evolution. Then, their representation is constructed that is invariant to scaling, rotation, and translation.