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Object detection
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#	Item
461	IMAGE CLASSIFICATION WITH BAGS OF LOCAL FEATURES A Dissertation Presented by DIMITRI A. LISIN Add to Reading List Source URL: vis-www.cs.umass.edu Language: English - Date: 2014-06-18 18:40:04 Artificial intelligence Mathematics Feature Object recognition Interest point detection Vector space Scale space K-nearest neighbor algorithm Vector Computer vision Image processing Algebra
462	MIT/HARVARD/CALTECH MURI VISION MEETING Tuesday, January 28, 1997 9:00 am - 5:00 pm American Academy of Arts and Sciences 200 Beacon Street Cambridge, Massachusetts Add to Reading List Source URL: cbcl.mit.edu Language: English - Date: 2003-03-14 13:52:48 Image processing Artificial intelligence Computational neuroscience Laurent Itti Face detection Object recognition Segmentation Vision Imaging Computer vision
463	Unsupervised Tattoo Segmentation Combining Bottom-Up and Top-Down Cues Josef D. Allen1a,b, Nan Zhaob, Jiangbo Yuanb, Xiuwen Liub Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, U.S 37831; b Department Add to Reading List Source URL: ww2.cs.fsu.edu Language: English - Date: 2012-04-27 14:55:50 Artificial intelligence Segmentation Tattoo Content-based image retrieval Watershed Face detection Cluster analysis Simple interactive object extraction Range segmentation Computer vision Image processing Statistics
464	Online Domain Adaptation of a Pre-Trained Cascade of Classifiers Vidit Jain Yahoo! Labs Bangalore Erik Learned-Miller University of Massachusetts Amherst Add to Reading List Source URL: vis-www.cs.umass.edu Language: English - Date: 2011-04-05 21:56:43 Statistical classification Supervised learning Binary classification Face detection AdaBoost Receiver operating characteristic Boosting methods for object categorization Statistics Machine learning Artificial intelligence
465	Matching Interest Points Using Affine Invariant Concentric Circles Han-Pang Chiu Tomas Lozano-Perez CSAIL, Massachusetts Institute of Technology {chiu, tlp}@csail.mit.edu Abstract Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2012-06-11 20:15:21 Artificial intelligence Scale-invariant feature transform Interest point detection Object recognition Ellipse Affine transformation Feature Kadir–Brady saliency detector Harris affine region detector Computer vision Image processing Geometry
466	Virtual Training for Multi-View Object Class Recognition Han-Pang Chiu Leslie Pack Kaelbling Tom´as Lozano-P´erez MIT Computer Science and Artificial Intelligence Laboratory Cambridge, MA 02139, USA Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2012-06-11 20:15:36 Object recognition Gesture recognition View model 3D modeling Face detection Scale-invariant feature transform Computer vision Vision Imaging
467	A Systematic Approach to Learning Object Segmentation from Motion Michael G. Ross and Leslie Pack Kaelbling {mgross,lpk}@csail.mit.edu MIT Computer Science and Artificial Intelligence Laboratory Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2004-07-01 07:47:52 Statistics Probability theory Networks Applied mathematics Artificial intelligence Segmentation Belief propagation Edge detection Bayesian network Image processing Computer vision Graphical models
468	Virtual Training for Multi-View Object Class Recognition Han-Pang Chiu Leslie Pack Kaelbling Tom´as Lozano-P´erez MIT Computer Science and Artificial Intelligence Laboratory Cambridge, MA 02139, USA Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2007-05-24 12:16:45 Object recognition Gesture recognition View model 3D modeling Face detection Scale-invariant feature transform Computer vision Vision Imaging
469	Bingham Procrustean Alignment for Object Detection in Clutter Jared Glover and Sanja Popovic Abstract— A new system for object detection in cluttered RGB-D images is presented. Our main contribution is a new method cal Add to Reading List Source URL: lis.csail.mit.edu Language: English - Date: 2013-08-12 17:46:21 Optics 3D computer graphics Object recognition Normal Rotation matrix Curvature 3D single object recognition Quaternion Computer vision Vision Imaging
470	Draft Fact Sheet NDSA Infrastructure &Standards Working Groups Checking Your Digital Content: How, What and When to Check Fixity? How do I know what I have and the file/object is not corrupted, changed or altered? Add to Reading List Source URL: blogs.loc.gov Language: English - Date: 2014-02-05 15:41:04 Digital libraries Library science Preservation Museology Hashing Digital preservation Checksum BagIt SHA-2 Error detection and correction Archival science Cryptographic hash functions