Abstract
Several novel methods based on locally extracted object features and spatial constellation models have recently been introduced for invariant object detection and recognition. The accuracy and reliability of the methods depend on the success of both tasks: evidence extraction and spatial constellation model search. In this study an accurate and efficient method for evidence extraction is introduced. The proposed method is based on simple Gabor features and their statistical ranking.
Academy of Finland (#204708) and EU (#70056/04) are acknowledged for support.
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Fischler, M., Elschlager, R.: The representation and matching of pictorial structures. IEEE Trans. on Computers 22, 67–92 (1973)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. of the Fourth Alvey Vision Conf., pp. 147–151 (1988)
Kadir, T.: Scale, Saliency and Scene Description. PhD thesis, Oxford University (2002)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. Journal of Computer Vision 60, 91–110 (2004)
Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (2003)
Hamouz, M., Kittler, J., Kamarainen, J.K., Paalanen, P., Kälviäinen, H.: Affine-invariant face detection and localization using GMM-based feature detector and enhanced appearance model. In: Proc. of the 6th Int. Conference on Automatic Face and Gesture Recognition, Seoul, Korea, pp. 67–72 (2004)
Kyrki, V., Kamarainen, J.K., Kälviäinen, H.: Simple Gabor feature space for invariant object recognition. Pattern Recognition Letters 25, 311–318 (2003)
Paalanen, P., Kamarainen, J.K., Ilonen, J., Kälviäinen, H.: Feature representation and discrimination based on Gaussian mixture model probability densities – practices and algorithms. Research report 95, Department of Information Technology, Lappeenranta University of Technology (2005)
Hamouz, M.: Feature-based affine-invariant detection and localization of faces. PhD thesis, University of Surrey (2004)
Kämäräinen, J.K.: Feature Extraction Using Gabor Filters. PhD thesis, Lappeenranta University of Technology (2003)
Figueiredo, M., Jain, A.: Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 381–396 (2002)
Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: XM2VTSDB: The extended M2VTS Database. In: Proc. of the 2nd Int. Conf. on Audio and Video-based Biometric Person Authentication, pp. 72–77 (1999)
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Kamarainen, J.K., Ilonen, J., Paalanen, P., Hamouz, M., Kälviäinen, H., Kittler, J. (2005). Object Evidence Extraction Using Simple Gabor Features and Statistical Ranking. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds) Image Analysis. SCIA 2005. Lecture Notes in Computer Science, vol 3540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499145_14
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DOI: https://doi.org/10.1007/11499145_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26320-3
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