2018
DOI: 10.1016/j.jvcir.2018.03.008
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Integrating QDWD with pattern distinctness and local contrast for underwater saliency detection

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Cited by 91 publications
(47 citation statements)
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“…Yuan et al [25] propose a recursive pyramid network with joint attention (RPJA) to learn the significance of the fine region. In order to get rich local information, Jian et al [30] use an improved wavelet-based salient-patch detector to detect patches.…”
Section: Related Workmentioning
confidence: 99%
“…Yuan et al [25] propose a recursive pyramid network with joint attention (RPJA) to learn the significance of the fine region. In order to get rich local information, Jian et al [30] use an improved wavelet-based salient-patch detector to detect patches.…”
Section: Related Workmentioning
confidence: 99%
“…A large-scale underwater image database was constructed in [36]. Later, Jian et al [37] designed an underwater saliency detection model by integrating Quaternionic Distance Based Weber Descriptor (QDWD) with pattern distinctness and Local Contrast, which incorporated quaternion number system and principal components analysis simultaneously.…”
Section: Related Workmentioning
confidence: 99%
“…Currently, a plenty of saliency detection models have been designed, which have been extended to various computer-vision and multimedia applications. Among them, typical applications include object detection [4,5,8], traffic congestion analysis [7,8], facial analysis [9,10], underwater vision [36,37], health prediction [51,52,53], visual prediction [56 -61], activity recognition [62,63], image retrieval [64 -68] and so on.…”
Section: Introductionmentioning
confidence: 99%
“…This Visual saliency detection is one of the hot research issues in computer vision, digital signal processing, pattern recognition and etc. Generally, visual saliency detection frameworks mainly fit into two categories: one is the bottomup saliency-detection model, which is primarily engaged in employing and extracting of the underlying features in the input image/video, such as colour, texture, intensity, contrast, direction, motion [1,2].…”
Section: Introductionmentioning
confidence: 99%