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
Shape representation and recognition from multiscale curvature
Computer Vision and Image Understanding
Fast correspondence-based system for shape retrieval
Pattern Recognition Letters
Algorithms for Shape Analysis of Contours and Waveforms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Retrieval by shape similarity with perceptual distance andeffective indexing
IEEE Transactions on Multimedia
Multiscale curvature-based shape representation using B-spline wavelets
IEEE Transactions on Image Processing
Shape representation and recognition through morphological curvature scale spaces
IEEE Transactions on Image Processing
A visual pathway for shape-based invariant classification of gray scale images
Integrated Computer-Aided Engineering - Artificial Neural Networks
Visual pathways for shape abstraction
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
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We describe a neuron multi-layered architecture that extracts landmark points of high curvature from 2d shapes and resembles the visual pathway of primates. We demonstrate how the rotated orientation specific receptive fields of the simple neurons that were discovered by Hubel and Wiesel can perform landmark point detection on the 2d contour of the shape that is projected on the retina of the eye. Detection of landmark points of high curvature is a trivial task with sophisticated machine equipment but we demonstrate how such a task can be accomplished by only using the hardware of the visual cortex of primates abiding to the discoveries of Hubel and Wiesel regarding the rotated arrangements of orientation specific simple neurons. The proposed layered architecture first extracts the 2dimensional shape from the projection on the retina then it rotates the extracted shape in multiple layers in order to detect the landmark points. Since rotating the image about the focal origin is equivalent to the rotation of the simple cells orientation field, our model offers an explanation regarding the mystery of the arrangement of the cortical cells in the areas of layer 2 and 3 on the basis of shape cognition from its landmark points.