Saliency generation from complex scene via digraph and Bayesian inference
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Summary
This paper proposes a novel saliency detection method based on a directed graph model and multi-scale Bayesian inference that can overcome the shortcomings of traditional CFAR detector and has much fewer false alarms in cluttered background.
- Type
- article
- Published
- 2015-12-25
- Cited by
- 3
- References
- 40
- OpenAlex
- https://openalex.org/W755335161
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:37873435
Keywords
Computer science, Artificial intelligence, Inference, Graph, Benchmark (surveying)
References
- A Novel Multiresolution Spatiotemporal Saliency Detection Model and Its Applications in Image and Video Compression
- Digital image processing using MATLAB
- The relationship between Precision-Recall and ROC curves
- Context-aware saliency detection
- Visual saliency detection with center shift
- A unified approach to salient object detection via low rank matrix recovery
- Static and space-time visual saliency detection by self-resemblance.
- Image Signature: Highlighting Sparse Salient Regions
- Saliency Detection via Graph-Based Manifold Ranking
- Attention, intention and salience in the posterior parietal cortex
- Graph-Regularized Saliency Detection With Convex-Hull-Based Center Prior
- Hebbian-based neural networks for bottom-up visual attention and its applications to ship detection in SAR images
- Saliency detection using maximum symmetric surround
- A change of the leading player in flow Visualization technique
- Integration of the saliency-based seed extraction and random walks for image segmentation
- Automatic salient object segmentation based on context and shape prior
- Frequency-tuned salient region detection
- Dynamic visual attention: searching for coding length increments
- SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
- Saliency detection based on integration of boundary and soft-segmentation
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