Discovering object instances from scenes of Daily Living

We propose an approach to identify and segment objects from scenes that a person (or robot) encounters in Activities of Daily Living (ADL). Images collected in those cluttered scenes contain multiple objects. Each image provides only a partial, possibly very different view of each object. An object instance discovery program must be able to link pieces of visual information from multiple images and extract the consistent patterns.

[1]  David A. Forsyth,et al.  Unsupervised Segmentation of Objects using Efficient Learning , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Benjamin Kuipers,et al.  Bootstrap learning for object discovery , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[3]  Alexei A. Efros,et al.  Using Multiple Segmentations to Discover Objects and their Extent in Image Collections , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[4]  Christoph H. Lampert,et al.  Unsupervised Object Discovery: A Comparison , 2010, International Journal of Computer Vision.

[5]  Ann Buzaid,et al.  Activities of daily living: evaluation and treatment in persons with multiple sclerosis. , 2013, Physical medicine and rehabilitation clinics of North America.

[6]  Vittorio Ferrari,et al.  Affine invariant regions++ , 2004 .

[7]  Narendra Ahuja,et al.  Extracting Subimages of an Unknown Category from a Set of Images , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[8]  Cordelia Schmid,et al.  3D Object Modeling and Recognition Using Local Affine-Invariant Image Descriptors and Multi-View Spatial Constraints , 2006, International Journal of Computer Vision.

[9]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Dimitris N. Metaxas,et al.  D - Clutter: Building object model library from unsupervised segmentation of cluttered scenes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Luc Van Gool,et al.  Video mining with frequent itemset configurations , 2006 .

[13]  Andrew Blake,et al.  Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[14]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[15]  Chandra Kambhamettu,et al.  D - Clutter: Building object model library from unsupervised segmentation of cluttered scenes , 2009, CVPR.

[16]  Tsuhan Chen,et al.  A Topic-Motion Model for Unsupervised Video Object Discovery , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Alexei A. Efros,et al.  Geometric context from a single image , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[18]  Luc Van Gool,et al.  Simultaneous Object Recognition and Segmentation from Single or Multiple Model Views , 2006, International Journal of Computer Vision.

[19]  Sinisa Todorovic,et al.  From a Set of Shapes to Object Discovery , 2010, ECCV.

[20]  Pietro Perona,et al.  Towards automatic discovery of object categories , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[21]  Jiri Matas,et al.  Large-Scale Discovery of Spatially Related Images , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Alexei A. Efros,et al.  Discovering object categories in image collections , 2005 .

[23]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[24]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[25]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[26]  Loong Fah Cheong,et al.  Active segmentation with fixation , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[27]  Rahul Sukthankar,et al.  A theory of the quasi-static world , 2002, Object recognition supported by user interaction for service robots.

[28]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[29]  Roland Siegwart,et al.  Segmentation and Unsupervised Part-based Discovery of Repetitive Objects , 2010, Robotics: Science and Systems.

[30]  조민수,et al.  Unsupervised Detection and Segmentation of Identical Objects , 2010 .

[31]  Stefano Soatto,et al.  Local Features, All Grown Up , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[32]  Bernt Schiele,et al.  Decomposition, discovery and detection of visual categories using topic models , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Jian Sun,et al.  Lazy snapping , 2004, SIGGRAPH 2004.

[34]  Luc Van Gool,et al.  Efficient Mining of Frequent and Distinctive Feature Configurations , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[35]  Shuicheng Yan,et al.  Common visual pattern discovery via spatially coherent correspondences , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[36]  Cordelia Schmid,et al.  Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search , 2008, ECCV.

[37]  Jean Ponce,et al.  Discriminative clustering for image co-segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[38]  Bernardo A. Huberman,et al.  Email as spectroscopy: automated discovery of community structure within organizations , 2003 .

[39]  Christos Faloutsos,et al.  Unsupervised modeling of object categories using link analysis techniques , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[40]  Alvaro Collet,et al.  Making specific features less discriminative to improve point-based 3D object recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[41]  Vikas Singh,et al.  An efficient algorithm for Co-segmentation , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[42]  Thomas Deselaers,et al.  Localizing Objects While Learning Their Appearance , 2010, ECCV.

[43]  Esther M. Arkin,et al.  An efficiently computable metric for comparing polygonal shapes , 1991, SODA '90.

[44]  Mary Skruppy.  Activities of Daily Living Evaluations , 1993 .

[45]  Yong Jae Lee,et al.  Collect-cut: Segmentation with top-down cues discovered in multi-object images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.