IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine vision
Shape representation and recognition from multiscale curvature
Computer Vision and Image Understanding
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fast correspondence-based system for shape retrieval
Pattern Recognition Letters
Visual pathways for detection of landmark points
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Retrieval by shape similarity with perceptual distance andeffective indexing
IEEE Transactions on Multimedia
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Multiscale curvature-based shape representation using B-spline wavelets
IEEE Transactions on Image Processing
Integrated Computer-Aided Engineering
Spectral shape descriptor using spherical harmonics
Integrated Computer-Aided Engineering
Visual pathways for shape abstraction
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Multi-object segmentation approach based on topological derivative and level set method
Integrated Computer-Aided Engineering
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Inspired by the rotated orientation specific receptive fields of the simple neurons that were discovered by Hubel and Wiesel we describe a multi-layered neural architecture for calculating the local curvature at each point of a planar shape without extracting the underlying contour. Our architecture resembles the visual pathway of primates as we demonstrate how the rotated orientation specific receptive fields of the simple neurons can perform local curvature calculation of the planar shape that is projected on the retina of the eye. We then use the same method to encode planar curvature into the intensity of gray scale images and we demonstrate the effectiveness of this encoding by proposing a shape-based triple correlation invariant image classification scheme. We present experimental results illustrating that by encoding planar curvature into the intensity values we improve the recognition capability of multi layered neural network classifiers without imposing additional complexity to the learning process.