Genetic Stereo Matching Algorithm with Fuzzy Fitness
This work addresses stereo matching for computer vision applications, but it appears incremental as it combines existing genetic and fuzzy methods.
The paper tackles stereo matching by proposing a genetic algorithm with a fuzzy fitness function, achieving accurate dense disparity maps suitable for real-time applications with reasonable computational time.
This paper presents a genetic stereo matching algorithm with fuzzy evaluation function. The proposed algorithm presents a new encoding scheme in which a chromosome is represented by a disparity matrix. Evolution is controlled by a fuzzy fitness function able to deal with noise and uncertain camera measurements, and uses classical evolutionary operators. The result of the algorithm is accurate dense disparity maps obtained in a reasonable computational time suitable for real-time applications as shown in experimental results.