FC-CRF: Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
參考: http://www.javashuo.com/article/p-zzmsjiqx-oq.html Abstract 傳統的圖像分割和標記技術是在像素或者圖片區域上定義CRF. 雖然區域模型上通常有密集的成對連通性, 但是像素級別的模型一般只允許用在稀疏圖結構. 本文是在整個圖像上運用全連接的CRF模型(FC-CRF). 結果CRF圖模型的邊非常多, 不能使用傳統方法. 本文主要貢獻是用
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相關文章
- 1. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
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- 5. 【論文閱讀】semantic image segmentation with deep convolutional nets and fully connected CRFs
- 6. 【論文閱讀筆記】Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Net
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