An FPGA Implementation of Information Theoretic Visual-Saliency System and Its Optimization

Biological vision systems use saliency-based visual attention mechanisms to limit higher-level vision processing on the most visually-salient subsets of an input image. Among several computational models that capture the visual-saliency in biological system, an information theoretic AIM(Attention based on Information Maximization) algorithm has been demonstrated to predict human gaze patterns better than other existing models. We present an FPGA based implementation of this computationally intensive AIM algorithm to support embedded vision applications. Our implementation provides performance of processing about 4M pixels/sec for 25 basis functions with a convolution kernel size of 21 by 21 for each of the R, G, and B color-channels, when implemented on a Virtex-6 LX240T. We also provide an optimization aimed at controlling the trade-off between power consumption and latency, and performance comparisons with a GPU implementation.