Object Recognition Using Local Affine Frames on Maximally Stable Extremal Regions

Methods based on distinguished regions (transformation covariant detectable regions) have achieved considerable success in object recognition, retrieval and matching problems in both still images and videos. The chapter focuses on a method exploiting local coordinate systems (local affine frames) established on maximally stable extremal regions. We provide a taxonomy of affine-covariant constructions of local coordinate systems, prove their affine covariance and present algorithmic details on their computation. Exploiting processes proposed for computation of affine-invariant local frames of reference, tentative region-to-region correspondences are established. Object recognition is formulated as a problem of finding a maximal set of geometrically consistent matches.

[1]  Cordelia Schmid,et al.  Indexing Based on Scale Invariant Interest Points , 2001, ICCV.

[2]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[3]  Farzin Mokhtarian,et al.  A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Narendra Ahuja,et al.  Learning to Recognize 3D Objects with SNoW , 2000, ECCV.

[5]  Nuno Vasconcelos,et al.  The Kullback-Leibler Kernel as a Framework for Discriminant and Localized Representations for Visual Recognition , 2004, ECCV.

[6]  Gösta H. Granlund,et al.  Robust Multi-scale Extraction of Blob Features , 2003, SCIA.

[7]  Luc Van Gool,et al.  Edinburgh Research Explorer Simultaneous Object Recognition and Segmentation by Image Exploration , 2022 .

[8]  Jiří Matas,et al.  Computer Vision - ECCV 2004 , 2004, Lecture Notes in Computer Science.

[9]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[10]  Luc Van Gool,et al.  Content-Based Image Retrieval Based on Local Affinely Invariant Regions , 1999, VISUAL.

[11]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[12]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[13]  Luc Van Gool,et al.  HPAT Indexing for Fast Object/Scene Recognition Based on Local Appearance , 2003, CIVR.

[14]  Stepán Obdrzálek,et al.  Object Recognition using Local Affine Frames on Distinguished Regions , 2002, BMVC.

[15]  Manuel González,et al.  Affine Invariant Texture Segmentation and Shape from Texture by Variational Methods , 1998, Journal of Mathematical Imaging and Vision.

[16]  Heinrich Niemann,et al.  A Spin-Glass Markov Random Field for 3-D Object Recognition , 2002 .

[17]  Cordelia Schmid,et al.  An Affine Invariant Interest Point Detector , 2002, ECCV.

[18]  Luc Van Gool,et al.  Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions , 2000, BMVC.

[19]  L. Guibas,et al.  Finding color and shape patterns in images , 1999 .

[20]  Raphaël Marée,et al.  Random subwindows for robust image classification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[21]  Leszek Wojnar,et al.  Image Analysis , 1998 .

[22]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[23]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[24]  Janne Heikkilä,et al.  Pattern matching with affine moment descriptors , 2004, Pattern Recognit..

[25]  Urs Ramer,et al.  An iterative procedure for the polygonal approximation of plane curves , 1972, Comput. Graph. Image Process..

[26]  Glenn Healey,et al.  Using color for geometry-insensitive segmentation , 1989 .

[27]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[28]  M. S. Drew,et al.  Color constancy - Generalized diagonal transforms suffice , 1994 .

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

[30]  Anuj Srivastava,et al.  A spectral representation for appearance-based classification and recognition , 2002, Object recognition supported by user interaction for service robots.

[31]  O. Chum,et al.  ENHANCING RANSAC BY GENERALIZED MODEL OPTIMIZATION Onďrej Chum, Jǐ , 2003 .

[32]  G D Finlayson,et al.  Spectral sharpening: sensor transformations for improved color constancy. , 1994, Journal of the Optical Society of America. A, Optics, image science, and vision.

[33]  Andrew Zisserman,et al.  Geometric invariance in computer vision , 1992 .

[34]  Christopher M. Bishop,et al.  Non-linear Bayesian Image Modelling , 2000, ECCV.