A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling Selective Attention Using a Neuromorphic Analog VLSI Device
Neural Computation
A high-speed multi-GPU implementation of bottom-up attention using CUDA
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Neural competitive structures for segmentation based on motion features
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
MAPS: multiscale attention-based presegmentation of color images
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
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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脳64 gray level images, the on ProtoEye implemented attention process operates in real-time. It runs at a frequency of 14 images per second.