PPT-A Fast Local Descriptor for Dense Matching

Engin Tola Vincent Lepetit Pascal Fua Computer Vision Laboratory EPFL 20080610 Motivation Narrow baseline Pixel Difference Graph Cuts groundtruth pixel difference input frame. We also present an EMbased algorithm to compute dense depth and occlusion maps from widebaseline image pairs using this descriptor This yields much better results in widebaseline situations than the pixel and correlationbased algorithms that are com Yacov. Hel-Or. The Interdisciplinary Center (IDC), Israel . Visiting Scholar - Google . Hagit. Hel-Or and Eyal David. U. of Haifa, Israel . A given pattern . p. is sought in an image. . The pattern may appear at any location in the image.. Tal Hassner. The Open University of Israel. CVPR’14 Tutorial on. Dense Image Correspondences for Computer Vision. Matching Pixels. Invariant detectors + robust descriptors + matching. In different views, scales, scenes, etc.. Matthew Brown. University of British Columbia. (prev.) Microsoft Research. [ Collaborators: . †. Simon Winder, *Gang . Hua. , . †. Rick . Szeliski. . †. =MS Research, *=MS Live Labs]. Applications @MSFT. 3 types of descriptors. :. SIFT / PCA-SIFT . (. Ke. , . Sukthankar. ). GLOH . (. Mikolajczyk. , . Schmid. ). DAISY . (. Tola. , et al, Winder, et al). Comparison of descriptors . (. Mikolajczyk. Attributed . Graphs . Yu Su. University of California at Santa Barbara. with . Fangqiu. Han, Richard E. . Harang. , and . Xifeng. Yan . Introduction. A Fast Kernel for Attributed Graphs. Graph Kernel. Microsoft Corporate. ganghua@microsoft.com. Online Contextual Face Recognition: . Towards Large Scale Photo Tagging for Sharing. Photo sharing has become a main online social activity. FaceBook. receives 850 million photo uploads/month. Local Regions. Jaechul. Kim and Kristen . Grauman. Univ. of Texas at Austin. Local feature detection. A crucial building block for many applications. Image retrieval. Object recognition. Image matching. Akhil. . Vij. Anoop. . Namboodiri. . Overview. 2. Introduction. Major Challenges . Motivation. Local Structures for Indexing. Local Structures for Matching. Summary and Conclusion. Introduction. 3. in Tensors . with Quality Guarantees. Kijung Shin. , Bryan . Hooi. , Christos . Faloutsos. Carnegie Mellon University . Motivation: Review Fraud. M-Zoom:. Fast Dense-Block Detection in Tensors with Quality Guarantees . Engin. Tola, Vincent . Lepetit. , Pascal . Fua. Computer Vision Laboratory. EPFL. 2008-06-10. Motivation. Narrow baseline : Pixel Difference + Graph Cuts*. groundtruth. pixel difference. input frame. Akhil. . Vij. Anoop. . Namboodiri. . Overview. 2. Introduction. Major Challenges . Motivation. Local Structures for Indexing. Local Structures for Matching. Summary and Conclusion. Introduction. 3. James Hays. cs195g Computational Photography. Brown University, Spring 2010. Recap from Monday. What imagery is available on the Internet. What different ways can we use that imagery. aggregate statistics. Computer Vision, FCUP, . 2018/19. Miguel Coimbra. Slides by Prof. Kristen . Grauman. Today. Local . invariant . features. Detection of interest points. (Harris corner detection). Scale invariant blob detection: .