A Visual Object Recognition System Invariant to Scale and Rotation

Sato, Yasuomi D.; Jitsev, Jenia; Malsburg, Christoph von der

doi:10.1007/978-3-540-87536-9_101

Cited by 8 publications

(7 citation statements)

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“…Surely, our model still leaves open a number of important 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, Jitsev, & von der Malsburg, 2008) as well as other image transformations such as changing illumination and perspective deformation. As discussed before, the system as proposed here assumes direct dynamic links from all positions in the Input Layer to all positions of the Input Assembly.…”

Section: Discussionmentioning

confidence: 99%

A recurrent dynamic model for correspondence-based face recognition

et al. 2008

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Our aim here is to create a fully neural, functionally competitive, and correspondence-based model for invariant face recognition. By recurrently integrating information about feature similarities, spatial feature relations, and facial structure stored in memory, the system evaluates face identity ("what"-information) and face position ("where"-information) using explicit representations for both. The network consists of three functional layers of processing, (1) an input layer for image representation, (2) a middle layer for recurrent information integration, and (3) a gallery layer for memory storage. Each layer consists of cortical columns as functional building blocks that are modeled in accordance with recent experimental findings. In numerical simulations we apply the system to standard benchmark databases for face recognition. We find that recognition rates of our biologically inspired approach lie in the same range as recognition rates of recent and purely functionally motivated systems.

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Section: Discussionmentioning

confidence: 99%

A recurrent dynamic model for correspondence-based face recognition

et al. 2008

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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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“…However, they do not use real images, but only orientation features. The only binding model that reproduces the vision process from real images is the Maplet model [52] from Malsburg's lab. Their model is computationally quite intensive, but is able to achieve face recognition.…”

Section: Binding-by-synchrony Modelsmentioning

confidence: 99%