Automated Design of Image Operators that Detect Interest Points

This work describes how evolutionary computation can be used to synthesize lowlevel image operators that detect interesting points on digital images. Interest point detection is an essential part of many modern computer vision systems that solve tasks such as object recognition, stereo correspondence and image indexing, to name but a few. The design of the specialized operators is posed as an optimization/search problem that is solved with genetic programming (GP), a strategy still mostly unexplored by the computer vision community. The proposed approach automatically synthesizes operators that are competitive with state-of-the-art designs, taking into account an operator's geometric stability and the global separability of detected points during fitness evaluation. The GP search space is defined using simple primitive operations that are commonly found in point detectors proposed by the vision community. The experiments described in this paper extend previous results (Trujillo a...

• Type: Journal
• Year: 2008
• Where: EC
• Authors: Leonardo Trujillo, Gustavo Olague
• Keywords: EC 2008, ECommerce, Lowlevel Image Operators, Operators, Point Detection