Journal Article10.1007/S11042-020-08849-Y
A brief survey of visual saliency detection
TL;DR: A detailed overview of the recent progress of saliency detection models in terms of heuristic- based techniques and deep learning-based techniques is demonstrated.
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Abstract: Salient object detection models mimic the behavior of human beings and capture the most salient region/object from the images or scenes, this field contains many important applications in both computer vision and pattern recognition tasks. Despite hundreds of models that have been proposed in this field, but still, it requires a large room for research. This paper demonstrates a detailed overview of the recent progress of saliency detection models in terms of heuristic-based techniques and deep learning-based techniques. we have discussed and reviewed its co-related fields, such as Eye-fixation-prediction, RGBD salient-object-detection, co-saliency object detection, and video-saliency-detection models. We have reviewed the key issues of the current saliency models and discussed future trends and recommendations. The broadly utilized datasets and assessment strategies are additionally investigated in this paper.
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Colorectal polyp region extraction using saliency detection network with neutrosophic enhancement
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Deep learning in electron microscopy
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Applications of human visual attention mechanisms in object detection
TL;DR: An ATR algorithm employing attention mechanisms with bottom- up and top-down control strategies is developed, and the experimental result with ship detection is given by use of bottom-up control strategy only.
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SpatioTemporal utilization of deep features for video saliency detection
Trung-Nghia Le,Akihiro Sugimoto +1 more
- 10 Jul 2017
TL;DR: This paper presents a method for detecting salient objects in a video where temporal information in addition to spatial information is fully taken into account and significantly outperforms state-of-the-art methods.
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Low-rank weighted co-saliency detection via efficient manifold ranking
TL;DR: An effective co-Saliency detection approach that first exploits an efficient manifold ranking scheme to extract a set of co- saliency regions, and then renders rank constraint to the feature matrix of the extracted regions to achieve a high-quality co-saliency map is presented.
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Top-Down Visual Saliency via Joint CRF and Dictionary Learning
Jimei Yang
TL;DR: The merits of the proposed top-down saliency model are demonstrated by applying it to prioritizing object proposals for detection and predicting human fixations and against state-of-the-art top-down saliency methods for target object localization.
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A Co-Saliency Model of Image Pairs
Hongliang Li,King Ngi Ngan +1 more
TL;DR: A method to detect co-saliency from an image pair that may have some objects in common and employ a normalized single-pair SimRank algorithm to compute the similarity score is introduced.
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