Abstract
This paper presents a new approach for statistical object localization. The localization scheme is directely based on local features, which are extracted for all image positions, in contrast to segmentation in classical schemes. Hierarchical Gabor filters are used to extract local features. With these features statistical object models are built for the different scale levels of the Gabor filters. The localization is then performed by a maximum likelihood estimation on the different scales successively. Results for the localization of real images of 2-D and 3-D objects are shown.
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© 1997 Springer-Verlag Berlin Heidelberg
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Pösl, J., Niemann, H. (1997). Statistical 3-D object localization without segmentation using wavelet analysis. In: Sommer, G., Daniilidis, K., Pauli, J. (eds) Computer Analysis of Images and Patterns. CAIP 1997. Lecture Notes in Computer Science, vol 1296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63460-6_148
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DOI: https://doi.org/10.1007/3-540-63460-6_148
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Online ISBN: 978-3-540-69556-1
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