A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A feature-based algorithm for detecting and classifying production effects
Multimedia Systems
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Multiclass Object Recognition with Sparse, Localized Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robot steering with spectral image information
IEEE Transactions on Robotics
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In the field of visual attention, bottom-up or saliency-based visual attention allows primates to detect non-specific conspicuous objects or targets in cluttered scenes. Simple multi-scale "feature maps" detect local spatial discontinuities in intensity, color, orientation, and are combined into a "saliency" map. HMAX is a feature extraction method and this method is motivated by a quantitative model of visual cortex. In this paper, we introduce the Saliency Criteria to measure the perspective fields. This model is based on cortex-like mechanisms and sparse representation, Saliency Criteria is obtained from Shannon's self-information and entropy. We demonstrate that the proposed model achieves superior accuracy with the comparison to classical approach in static saliency map generation on real data of natural scenes and psychology stimuli patterns.