Abstract
This paper introduces a novel scheme which works on symbolizing every line in an object image for object recognition. Symbolizing is accomplished in terms of angles of intersection with regard to a line under consideration. Spatial relationship existing among the symbolized lines is represented using the notion of Triangular Spatial Relationship (TSR). A set of quadruples which preserves the TSR is subjected to principal component analysis to obtain the principal component vectors. These vectors are then stored in the knowledgebase for the purpose of recognition. Experimental results demonstrate that the proposed approach is efficient, invariant to linear transformations and capable of learning. To substantiate the success of the proposed model, a comparative study is performed with Murase and Nayar approach.
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References
Bergevin, R., Levine, M.: Generic Object Recognition: Building and matching coarse descriptions from line drawings. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(1), 19–36 (1993)
Canny, J.F.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models- their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Guru, D.S., Shekar, B.H., Nagabhushan, P.: A simple and robust line detection algorithm based on small eigenvalue analysis. Pattern Recognition Letters 25(1), 1–13 (2004)
Guru, D.S., Nagabhushan, P.: Triangular spatial relationship: A new approach for spatial knowledge representation. Pattern Recognition Letters 22(9), 999–1006 (2001)
Kruger, N., Peters, G.: ORASSYLL: Object recognition with autonomously learned and sparse symbolic representations based on metrically organized local line detectors. Computer Vision and Image Understanding 77(1), 48–77 (2000)
Lamdan, Y., Wolfson, H.: Geometric Hashing: A general and efficient model based recognition scheme. In: Second International Conference on Computer Vision (1988)
Leonardis, A., Bischof, H.: Robust recognition using Eigenimages. Computer Vision and Image Understanding 78(1), 99–118 (2000)
Leonardis, A., Bischof, H., Jasna, M.: Multiple Eigenspaces. Pattern Recognition 35(11), 2613–2627 (2002)
Murase, H., Nayar, S.K.: Visual learning and recognition of 3-d objects from appearance. International Journal of Computer Vision 14(5), 1–24 (1995)
Murase, H., Nayar, S.K.: Detection of 3D objects in cluttered scenes using hierarchical eigenspace. Pattern Recognition Letters 18(4), 375–384 (1997)
Nene, S.A., Nayar, S.K., Murase, H.: Columbia Object Image Library (COIL-20), Technical Report CUCS-005-96, Columbia University, New York (1996)
Ohba, K., Ikeuchi, K.: Detectability, Uniqueness, and Reliability of eigen windows for stable verification of partially occluded objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(9), 1043–1048 (1997)
Pentland, A., Moghddam, B., Straner, T.: View-based and Modular Eigenspaces for Face Recognition, Technical Report 245, MIT Media Laboratory (1994)
Tsai, F.C.D.: Geometric hashing with line features. Pattern Recognition 27(3), 377–389 (1994)
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© 2006 Springer-Verlag Berlin Heidelberg
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Shekar, B.H., Guru, D.S., Nagabhushan, P. (2006). Object Recognition Through the Principal Component Analysis of Spatial Relationship Amongst Lines. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_18
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DOI: https://doi.org/10.1007/11612032_18
Publisher Name: Springer, Berlin, Heidelberg
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