A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Computational strategies for object recognition
ACM Computing Surveys (CSUR)
A review of algorithms for shape analysis
Document image analysis
Knowledge-based image understanding systems: a survey
Computer Vision and Image Understanding
Bias Error Analysis of the Generalised Hough Transform
Journal of Mathematical Imaging and Vision
Empirical Evaluation Techniques in Computer Vision
Empirical Evaluation Techniques in Computer Vision
Darboux Frames, Snakes, and Super-Quadrics: Geometry from the Bottom Up
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Image Registration Using Hierarchical B-Splines
IEEE Transactions on Visualization and Computer Graphics
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Road extraction from motion cues in aerial video
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Image understanding as a second course in AI: preparing students for research
Proceedings of the 37th SIGCSE technical symposium on Computer science education
Autonomous Learning of Object Appearances using Colour Contour Frames
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Flexible Tracking of Object Contours Using LR-Traversing Algorithm
CGIV '06 Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation
Shape recognition using eigenvalues of the Dirichlet Laplacian
Pattern Recognition
Kernel PCA for similarity invariant shape recognition
Neurocomputing
GMAI '08 Proceedings of the 2008 3rd International Conference on Geometric Modeling and Imaging
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A novel Circular Augmented Rotational Trajectory (CART) algorithm to compute an R-Space based shape descriptors, allowing efficient shape matching, generalisation and classification, is given. The shape descriptor is rotation and scale invariant, capable of detecting invariant geometric properties despite the presence of considerable noise and quantisation errors. The method is capable of detecting distinctive features including general invariant curvatures and sharp features while properly addressing the ambiguity in shape approximation. Experimental analysis on complex and ambiguous shapes shows that the CART method can correctly detect and represent the inherent shape. Universality, robustness and consistent performance have been noted while applying to many difficult and ambiguous object boundaries.