Fast and Robust Object Recognition Based on Reduced Shock-Graphs and Pruned-SIFT

Fast algorithms for image recogition have become a priority when the number of images to be analyzed can be counted in the number of millions. Therefore, the need for fast algorithms for image recognition. This paper describes an algorithm capable of recognize an object in an image by the fusing information from a reduced version of the Shock-Graph algorithm and the Scale Invariant Feature Transform (SIFT) points. The proposed algorithm uses the reduced Shock-Graph to obtain a skeleton of an object in order to minimize the number of SIFT points to reduce the computational complexity of object image comparison. The proposed algorithm is capable of recognizing objects in a fast way under rotation, deformation and scaling. Using a collection of shapes, we demonstrate the performance of our implementation using a combination of the reduced Shock-Graph and SIFT points.

[1]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[2]  Allen R. Hanson,et al.  Computer Vision Systems , 1978 .

[3]  B. Kimia,et al.  3D object recognition using shape similiarity-based aspect graph , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[4]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Wendi Heinzelman,et al.  A general data fusion architecture , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[6]  Jonathon Shlens,et al.  Fast, Accurate Detection of 100,000 Object Classes on a Single Machine , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Erin W. Chambers,et al.  Extended grassfire transform on medial axes of 2D shapes , 2011, Comput. Aided Des..

[8]  Benjamin B. Kimia,et al.  A Similarity-Based Aspect-Graph Approach to 3D Object Recognition , 2004, International Journal of Computer Vision.

[9]  E. Rolls,et al.  Computational analysis of the role of the hippocampus in memory , 1994, Hippocampus.

[10]  Ali Shokoufandeh,et al.  View-based 3-D object recognition using shock graphs , 2002, Object recognition supported by user interaction for service robots.

[11]  Ali Shokoufandeh,et al.  Shock Graphs and Shape Matching , 1998, International Journal of Computer Vision.

[12]  Sabine Süsstrunk,et al.  Salient Region Detection and Segmentation , 2008, ICVS.

[13]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[14]  Jianbo Shi,et al.  Recognizing objects by piecing together the Segmentation Puzzle , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Horst Bischof,et al.  Robust Recognition Using Eigenimages , 2000, Comput. Vis. Image Underst..

[16]  Hiroshi Murase,et al.  Illumination Planning for Object Recognition Using Parametric Eigenspaces , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Ali Shokoufandeh,et al.  Graph-Theoretical Methods in Computer Vision , 2000, Theoretical Aspects of Computer Science.

[18]  Vladimir Kolmogorov,et al.  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision , 2004, IEEE Trans. Pattern Anal. Mach. Intell..