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Hippocampal-like categorization of object views: A self-organizing learning approach to vision modeling using stochastic grammar inference and associative memory

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3 Author(s)
Goebel, P.M. ; ACIN - Autom.&Control Inst., Vienna Univ. of Technol., Vienna ; Vincze, M. ; Favre-Bulle, B.

The categorization problem in object recognition is the assignment of semantic categories to objects or parts of objects. Here, the best categorization performance provide mammalian brain functions that motivate to partly mimic such a formation for the application to machine learning and automation. Curiosity and experimental willingness support human recognizing and detecting in relation to still unknown objects. In order to learn an understanding of the topology of such objects, objects are observed from a series of different viewpoints. This results in a collection of object views that afterward can be used by cognitive processes. State-of-the-art systems provide learning structures that are subsequently utilized for object recognition and tracking tasks. However, most of these systems aim at very specific goals in restricted domains, and therefore, it remains no room for learning and understanding of object structures. In the work of this paper, we propose a new robot vision model for neural categorization. This is close to our initial idea of mimicking mammalian brain functions for robot vision. We use the combinatorial solution of our cognitive framework with an embedding of a recently presented stochastic n-gram model, supported by a three-dimensional grammar model on a discrete three-dimensional lattice. Furthermore, we use ant colony optimization heuristics for collecting the transition probabilities of the n-gram model. The proposed solution is exemplified by applying the method to several object view series of 69 polytopes generated out of the five platonic solids by truncation; thus, generating images from 32 viewpoints each, yields an object set of 2208 images. These set is further expanded to four sets perturbed by Gaussian-noise with varying sigma = {0, 0.1, 0.5, 1, 2}. Finally, we show results for selected objects and conclude with an outlook on further work.

Published in:

Emerging Technologies and Factory Automation, 2008. ETFA 2008. IEEE International Conference on

Date of Conference:

15-18 Sept. 2008

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