Efficient Coarse-to-Fine PatchMatch for Large Displacement Optical Flow
CPM光流主要針對大位移計算，一般大位移計算都涉及金字塔，但是某些可能會造成小結構物體光流缺失 算法主體流程 1.圖像按步長去採樣種子點，種子點按照縮放比例下采樣到每層，就近取整 2.在低分辨率層採用隨機搜索匹配算法patchmatch算法求該層最優匹配值 3. 高分辨率層採用低分辨層作爲初值，小範圍搜索優化位移值 其中每層的搜索半徑取值需要注意，低分辨率層取大搜索半徑可以獲得全局最好初值，高分辨
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相關文章
- 1. 論文解讀2-Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation
- 2. Optical Flow
- 3. FlowNet: Learning Optical Flow with Convolutional Networks
- 4. Optical-Flow ：Spynet
- 5. [cv] optical flow
- 6. ProFlow: Learning to Predict Optical Flow
- 7. optical flow（OFM）簡介
- 8. LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
- 9. SteadyFlow: Spatially Smooth Optical Flow for Video Stabilization
- 10. FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks