본문 바로가기

Paper Reading/CVPR

(13)
Discriminatively Trained Deformable Part Models http://people.cs.uchicago.edu/~pff/latent/ Discriminatively Trained Deformable Part ModelsVersion 4. Updated on April 21, 2010. Over the past few years we have developed a complete learning-based system for detecting and localizing objects in images. Our system represents objects using mixtures of deformable part models. These models are trained using a discriminative method that only requires b..
Automatically Mining Person Models of Celebrities for Visual Search Applications Methods and systems for automated identification of celebrity face images are provided that generate a name list of prominent celebrities, obtain a set of images and corresponding feature vectors for each name, detect faces within the set of images, and remove non-face images. An analysis of the... Inventors: David ROSS, Andrew RABINOVICH, Anand PILLAI, Hartwig ADAM Assignee: Google Inc. http://..
Scalable Face Image Retrieval with Identity-Based Quantization and Multi-Reference Re-ranking Scalable Face Image Retrieval with Identity-Based Quantization and Multi-Reference Re-ranking Zhong Wu Tsinghua Univ., Ctr Adv Study Qifa Ke y1 , Jian Sun y2 Microsoft Research 1 Silicon Valley Lab, 2 Asia Lab Heung-Yeung Shumy Microsoft Corporation http://research.microsoft.com/pubs/122158/cvpr2010.pdf
A simple object detector with boosting http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html
Homography 정리 잘된 것
Microsoft Research Street Slide View Paper http://research.microsoft.com/en-us/um/people/kopf/street_slide/paper/street_slide.pdf
Interest Seam Image We propose interest seam image, an efficient visual synopsis for video. To extract an interest seam image, a spatiotemporal energy map is constructed for the target video shot. Then an optimal seam which encompasses the highest energy is identified by an efficient dynamic programming algorithm. The optimal seam is used to extract a seam of pixels from each video frame to form one column of an im..
SPEC Hashing: Similarity Preserving algorithm for Entropy-based Coding, Ruei-Sung Lin, David Ross, Jay Yagnik. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010. http://www.cs.toronto.edu/~dross/LinRossYagnik_CVPR2010.pdf Abstract: Searching approximate nearest neighbors in large scale high dimensional data set has been a challenging problem. This paper presents a novel and fast algorithm for learning binary hash functions for fast nearest neig..