Detection of saliency map as image feature outliers using random projections based method
| Autor: | Marcin Gabryel, Robertas Damaševičius, Dawid Połap, Marcin Wozniak, Rytis Maskeliunas, Tatjana Sidekerskiene |
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| Rok vydání: | 2017 |
| Předmět: |
Computer science
Image quality business.industry Gaussian Kernel density estimation Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications Pattern recognition Probability density function Image processing 02 engineering and technology symbols.namesake Robustness (computer science) Computer Science::Computer Vision and Pattern Recognition Outlier 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | 2017 13th International Computer Engineering Conference (ICENCO). |
| DOI: | 10.1109/icenco.2017.8289768 |
| Popis: | We describe a novel method based on Random Projections for construction of image saliency maps. The method identifies outliers in the 2D projections of image point features as salient image points using Random Projections and kernel density estimation. We compare the method with other known methods in the area and validated on a number of benchmark images. The robustness of the method when Gaussian blurring is applied to an image is demonstrated and evaluated using F-statistics of several image quality metrics. Application of the proposed method for image processing is discussed. |
| Databáze: | OpenAIRE |
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