Similarity-invariant signatures for partially occluded planar shapes
International Journal of Computer Vision
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Objects Using Scale Space Local Invariants
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Translation, rotation, and scale-invariant object recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiscale curvature-based shape representation using B-spline wavelets
IEEE Transactions on Image Processing
Planar Shapes Descriptors Based on the Turning Angle Scalogram
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
A novel multi-scale representation for 2-D shapes
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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In this paper, we present a new method and its preliminary results within the context of pattern analysis and recognition. This method is based on the multiscale analysis of a curve and deals with the contour of planar objects. Our method uses a low-pass Gaussian kernel to gradually smooth the contour by decreasing the filter bandwidth. Applying gain control to the smoothed contour stretches it to the same scale as the original one so that both contours intersect. By varying the bandwidth and marking all the intersection points between the smoothed contour and the original one we can generate the intersection points map (IPM) function. The initial results obtained by applying this method to various contours appears to indicate that the IPM function has some very interesting properties within the context of pattern recognition. It is translation and rotation insensitive and also scale change resistant for a large range of scaling. The IPM function generated when applied to noisy contours shows that the method is resistant to noise for a range of noise energy. Applying the features extracted by this method to retrieve a pattern from a database confirms the efficiency of the method.