A Real Time Implementation of the Saliency-Based Model of Visual Attention on a SIMD Architecture

Abstract

Visual attention is the ability to rapidly detect the visually salient parts of a given scene. Inspired by biological vision, the saliency-based algorithm efficiently models the visual attention process. Due to its complexity, the saliency-based model of visual attention needs, for a real time implementation, higher computation resources than available in conventional processors. This work reports a real time implementation of this attention model on a highly parallel Single Instruction Multiple Data (SIMD) architecture called ProtoEye. Tailored for low-level image processing, ProtoEye consists of a 2D array of mixed analog-digital processing elements (PE). The operations required for visual attention computation are optimally distributed on the analog and digital parts. The analog diffusion network is used to implement the spatial filtering-based transformations such as the conspicuity operator and the competitive normalization of conspicuity maps. Whereas the digital part of Proto-Eye allows the implementation of logical and arithmetical operations, for instance, the integration of the normalized conspicuity maps into the final saliency map. Using 64 × 64 gray level images, the on ProtoEye implemented attention process operates in real-time. It runs at a frequency of 14 images per second.KeywordsVisual AttentionAttention ModelSingle Instruction Multiple DataReal Time ImplementationDigital PartThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.