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The genetic algorithm census transform: evaluation of census windows of different size and level of sparseness through hardware in-the-loop training

Research group:


Publication Type:

Journal article

Venue:

Open Access Journal of Real-Time Image Processing

DOI:

https://doi.org/10.1007/s11554-020-00993-w


Abstract

Stereo correspondence is a well-established research topic and has spawned categories of algorithms combining several processing steps and strategies. One core part to stereo correspondence is to determine matching cost between the two images, or patches from the two images. Over the years several different cost metrics have been proposed, one being the Census Transform (CT). The CT is well proven for its robust matching, especially along object boundaries, with respect to outliers and radiometric differences. The CT also comes at a low computational cost and is suitable for hardware implementation. Two key developments to the CT are non-centric and sparse comparison schemas, to increase matching performance and/or save computational resources. Recent CT algorithms share both traits but are handcrafted, bounded with respect to symmetry, edge lengths and defined for a specific window size. To overcome this, a Genetic Algorithm (GA) was applied to the CT, proposing the Genetic Algorithm Census Transform (GACT), to automatically derive comparison schemas from example data. In this paper, FPGA-based hardware acceleration of GACT, has enabled evaluation of census windows of different size and shape, by significantly reducing processing time associated with training. The experiments show that lateral GACT windows produce better matching accuracy and require less resources when compared to square windows.

Bibtex

@article{Ahlberg5960,
author = {Carl Ahlberg and Miguel Leon Ortiz and Fredrik Ekstrand and Mikael Ekstr{\"o}m},
title = {The genetic algorithm census transform: evaluation of census windows of different size and level of sparseness through hardware in-the-loop training},
month = {July},
year = {2020},
journal = {Open Access Journal of Real-Time Image Processing},
url = {http://www.es.mdh.se/publications/5960-}
}