Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern recognition with moment invariants: a comparative study and new results
Pattern Recognition
A survey of moment-based techniques for unoccluded object representation and recognition
CVGIP: Graphical Models and Image Processing
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
View-Based Recognition Using an Eigenspace Approximation to the Hausdorff Measure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Twenty Years of Document Image Analysis in PAMI
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching and Retrieval of Distorted and Occluded Shapes Using Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Feature Hierarchical Template Matching Using Distance Transforms
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
WARP: Accurate Retrieval of Shapes Using Phase of Fourier Descriptors and Time Warping Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet descriptor of planar curves: theory and applications
IEEE Transactions on Image Processing
Contour detection based on nonclassical receptive field inhibition
IEEE Transactions on Image Processing
Distance sets for shape filters and shape recognition
IEEE Transactions on Image Processing
A cognitive evaluation procedure for contour based shape descriptors
International Journal of Hybrid Intelligent Systems - Recent developments in Hybrid Intelligent Systems
Knowledge-based part correspondence
Pattern Recognition
A biologically motivated multiresolution approach to contour detection
EURASIP Journal on Applied Signal Processing
Foundations and Trends® in Computer Graphics and Vision
Robust symbolic representation for shape recognition and retrieval
Pattern Recognition
Robust symbolic representation for shape recognition and retrieval
Pattern Recognition
Confidence interval approach to feature re-weighting
Multimedia Tools and Applications
An efficient garment visual search based on shape context
MUSP'09 Proceedings of the 9th WSEAS international conference on Multimedia systems & signal processing
An efficient garment visual search based on shape context
WSEAS Transactions on Computers
Shape recognition based on Kernel-edit distance
Computer Vision and Image Understanding
Incomplete contour representations and shape descriptors: ICR test studies
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
A novel ring radius transform for video character reconstruction
Pattern Recognition
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With inspiration from psychophysical researches of the human visual system, we propose a novel aspect and a method for performance evaluation of contour-based shape recognition algorithms regarding their robustness to incompleteness of contours. We use complete contour representations of objects as a reference (training) set. Incomplete contour representations of the same objects are used as a test set. The performance of an algorithm is reported using the recognition rate as a function of the percentage of contour retained. We call this evaluation procedure the ICR test. We consider three types of contour incompleteness, viz. segment-wise contour deletion, occlusion, and random pixel depletion. As an illustration, the robustness of two shape recognition algorithms to contour incompleteness is evaluated. These algorithms use a shape context and a distance multiset as local shape descriptors. Qualitatively, both algorithms mimic human visual perception in the sense that recognition performance monotonously increases with the degree of completeness and that they perform best in the case of random depletion and worst in the case of occluded contours. The distance multiset method performs better than the shape context method in this test framework.