The Application of Sparse Reconstruction Algorithms for Improving Background Dictionary in Visual Saliency Detection
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This article is published in Intelligent Automation and Soft Computing.The article was published on 2020-01-01 and is currently open access. It has received 1 citations till now.read more
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Research on the Relevant Methods and Technologies of Digital Watermarking
TL;DR: Wang et al. as discussed by the authors proposed and discussed digital watermarking pipeline with some, which is a typical technology of copyright protection and ownership identification, including watermark embedding and extraction.
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Journal ArticleDOI
A model of saliency-based visual attention for rapid scene analysis
TL;DR: In this article, a visual attention system inspired by the behavior and the neuronal architecture of the early primate visual system is presented, where multiscale image features are combined into a single topographical saliency map.
Book ChapterDOI
Shifts in selective visual attention: towards the underlying neural circuitry.
Christof Koch,Shimon Ullman +1 more
TL;DR: This study addresses the question of how simple networks of neuron-like elements can account for a variety of phenomena associated with this shift of selective visual attention and suggests a possible role for the extensive back-projection from the visual cortex to the LGN.
Proceedings ArticleDOI
Global contrast based salient region detection
TL;DR: This work proposes a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence, and consistently outperformed existing saliency detection methods.
Proceedings Article
Graph-Based Visual Saliency
TL;DR: A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is proposed, which powerfully predicts human fixations on 749 variations of 108 natural images, achieving 98% of the ROC area of a human-based control, whereas the classical algorithms of Itti & Koch achieve only 84%.
Proceedings ArticleDOI
Deeply Supervised Salient Object Detection with Short Connections
TL;DR: This paper proposes a new salient object detection method by introducing short connections to the skip-layer structures within the HED architecture, which takes full advantage of multi-level and multi-scale features extracted from FCNs, providing more advanced representations at each layer, a property that is critically needed to perform segment detection.