Sparse shapes prototype modeling using genetic algorithms

The process of finding representative shape patterns from sparse datasets is a challenging task: especially for non-rigid objects, shape deformations through time can produce very different sets of corners from frame to frame and a proper comparison of point features can be very difficult. Evaluating a multi-objective fitness function in a discrete voting space, partial similarities between deformable objects can be found and a correct data association can be performed. A genomic encoding of corner-based shapes is introduced and, taking advantage of a robust genetic-based search algorithm, sets of corners pertaining to objects of interest are mapped into common models. The most representative features are detected and used to evolve shape prototypes.

• Type: Journal
• Year: 2010
• Where: ICIP
• Authors: Stefano Maludrottu, Hany Sallam, Carlo S. Regazzoni
• Keywords: Discrete Voting Space, ICIP 2010, Image Processing, Multi-objective Fitness Function, Representative Shape Patterns