Dynamic Link Matching between Feature Columns for Different Scale and Orientation

Sato, Yasuomi D.; Wolff, Christian; Wolfrum, Philipp; Malsburg, Christoph von der

doi:10.1007/978-3-540-69158-7_41

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…At least, in the real-time face detection demonstration (not shown here), the MS-EGM is robust to illumination and distortion. In the future, introducing the rotation invariance [37,38] into our MS-EGM, we develop a more accurate face detection system that can easily detect the face when putting the head on one side. Also, it is interesting to use the face graph, instead of the square graph.…”

Section: Discussionmentioning

confidence: 99%

Multi-scale elastic graph matching for face detection

Sato

Kuriya

2013

EURASIP J. Adv. Signal Process.

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We propose a multi-scale elastic graph matching (MS-EGM) algorithm for face detection, in which the conventional EGM is improved with two simple image processing techniques of the Gabor wavelet-based pyramid and the weak Gabor feature elimination. It is expected to solve difficulties of the real-time process in the conventional EGM. The Gabor wavelet-based pyramid effectively reduces not only the computational cost of the Gabor filtering but also the computational complexity of feature representation of a model face, preserving the facial information. The elimination of the weak Gabor feature extracted from an input image facilitates an accuracy of the Gabor feature similarity computations as unexpected. We then test that the MS-EGM can be capable of rapid face detection processing while achieving a high correct detection rate, comparable to the AdaBoost Haar-like (HL) feature cascade. We also show that the MS-EGM has strong robustness to the image of a face occluded with sunglasses and scarfs because of topologically preserved feature representations.

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Section: Discussionmentioning

confidence: 99%

Multi-scale elastic graph matching for face detection

Sato

Kuriya

2013

EURASIP J. Adv. Signal Process.

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“…Surely, the model still leaves open a number of problems for future work. As is, the model is invariant only to translation and needs to be generalized to changing scale and orientation (which will require dynamic relinking of feature connections, see Sato et al 2007Sato et al , 2008 as well as other image transformations such as changing illumination and perspective deformation. Another important extension of the system is autonomous learning of the contents of the Gallery.…”

Section: Discussionmentioning

confidence: 99%

Information Routing, Correspondence Finding, and Object Recognition in the Brain

Wolfrum¹

2010

Studies in Computational Intelligence

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This work would not have been possible without the people I have had the honor of working with over the past years. First of all I want to thank Christoph von der Malsburg, whose scientific concepts have inspired large parts of this thesis, and who granted me enough freedom to follow my own research directions while at the same time providing motivation whenever this was necessary. I also thank Rudolf Mester for good discussions that helped me retain my engineering viewpoint on the interdisciplinary problems that I worked on. Discussions with Geoff Goodhill, Bruno Olshausen, Jochen Triesch, and Alan Yuille, among others, have shaped my thinking and had an important impact on this thesis.I thank Urs Bergmann, Jenia Jitsev, and Junmei Zhu for proof-reading parts of the thesis, and Jörg Lücke for good collaboration. The neurogroups at FIAS have provided a rich and stimulating environment, it has been fun working and thinking with you! I also thank all colleagues at FIAS for good company and the truly interdisciplinary interaction we had (e.g. at Hirschegg). Last but not least I owe this thesis to my parents, who planted curiosity and a desire for understanding in me that were stronger than the difficulties I met during this dissertation. iv

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“…Receptive fields of primary cortical neurons, for example, are specific for stimulus orientation and size. When shifter circuits are to be used to normalize the image of a given object under change not only in position but also size and orientation, then also feature types are to be transformed (as modeled in Sato, Wolff, Wolfrum, & von der Malsburg, 2008) for the simplified assumption of higher cortical areas sharing the same representation as V1) to establish correspondence to a stored model. What has been treated here as a single link between input and output sheet therefore could be interpreted as a whole trunk of connections between all feature units in the image and model points connected by the link.…”

Section: Future Perspectives and Conclusionmentioning

confidence: 99%

Self-Organization of Topographic Bilinear Networks for Invariant Recognition

Bergmann

Malsburg

2011

Neural Computation

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We present a model for the emergence of ordered fiber projections that may serve as a basis for invariant recognition. After invariance transformations are self-organized, so-called control units competitively activate fiber projections for different transformation parameters. The model builds on a well-known ontogenetic mechanism, activity-based development of retinotopy, and it employs activity blobs of varying position and size to install different transformations. We provide a detailed analysis for the case of 1D input and output fields for schematic input patterns that shows how the model is able to develop specific mappings. We discuss results that show that the proposed learning scheme is stable for complex, biologically more realistic input patterns. Finally, we show that the model generalizes to 2D neuronal fields driven by simulated retinal waves.

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Dynamic Link Matching between Feature Columns for Different Scale and Orientation

Cited by 7 publications

References 12 publications

Multi-scale elastic graph matching for face detection

Multi-scale elastic graph matching for face detection

Information Routing, Correspondence Finding, and Object Recognition in the Brain

Self-Organization of Topographic Bilinear Networks for Invariant Recognition

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