{"id":157344,"date":"2009-01-01T00:00:00","date_gmt":"2009-01-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/a-perceptually-motivated-online-benchmark-for-image-matting\/"},"modified":"2018-10-16T22:00:17","modified_gmt":"2018-10-17T05:00:17","slug":"a-perceptually-motivated-online-benchmark-for-image-matting","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/a-perceptually-motivated-online-benchmark-for-image-matting\/","title":{"rendered":"A Perceptually Motivated Online Benchmark for Image Matting"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The availability of quantitative online benchmarks for low-level vision tasks such as stereo and optical \ufb02ow has led to signi\ufb01cant progress in the respective \ufb01elds. This paper introduces such a benchmark for image matting. There are three key factors for a successful benchmarking system: (a) a challenging, high-quality ground truth test set; (b) an online evaluation repository that is dynamically updated with new results; (c) perceptually motivated error functions. Our new benchmark strives to meet all three criteria. We evaluated several matting methods with our benchmark and show that their performance varies depending on the error function. Also, our challenging test set reveals problems of existing algorithms, not re\ufb02ected in previously reported results. We hope that our effort will lead to considerable progress in the \ufb01eld of image matting, and welcome the reader to visit our benchmark at www.alphamatting.com.<\/p>\n<p>Please find also the supplementary material and technical report under Related Info tab.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The availability of quantitative online benchmarks for low-level vision tasks such as stereo and optical \ufb02ow has led to signi\ufb01cant progress in the respective \ufb01elds. This paper introduces such a benchmark for image matting. There are three key factors for a successful benchmarking system: (a) a challenging, high-quality ground truth test set; (b) an online 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