IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Shape Description Via the Multiresolution Symmetric Axis Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analog VLSI and neural systems
Analog VLSI and neural systems
Simulating the Grassfire Transform Using an Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
A pulse-coded communications infrastructure for neuromorphic systems
Pulsed neural networks
Piecewise Linear Skeletonization Using Principal Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analog Integrated Circuits and Signal Processing
Signaling contours by neuromorphic wave propagation
Biological Cybernetics
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The symmetric-axis transform is a process that dynamically encodes the space of a visual shape through self-interaction of its contours. It is generally simulated using computer algorithms. A neural architecture of this transformation is presented that is conceptually simple enough for a hardware implementation. Its architecture consists of a wave-propagating map, orientation-selective columns detecting wave pieces of specific orientation, and a coincidence map detecting the clash of two wave fronts. We illustrate its operation on partial contours extracted from gray-scale images.