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Once the human vision system has seen a 3D object from a few different viewpoints, depending on the nature of the object, it can generally recognize that object from new arbitrary viewpoints. This useful interpolative skill relies on the highly complex pattern matching systems in the human brain, but the general idea can be applied to a computer vi...
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Usually, decision tree induction algorithms are limited to work with non relational data. Given a record, they do not take into account other objects attributes even though they can provide valuable information for the learning task. In this paper we present GGQ-ID3, a multi-relational decision tree learning algorithm that uses Generalized Graph Qu...
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... Fuzzy associative memory models, introduced by Kong and Kosko [15], [16], have been employed to store rules for classifications based on fuzzy LVQ. This is the approach we have taken in our previous work on the problem of 2D and 3D object recognition [17]- [19]. ...
1 It is desirable for automated object recognition using computer vision systems to emulate the human capacity for recognition of shapes invariant to vari-ous transformations. We present an algorithm, based on a Fuzzy Associative Database approach, which uses appropriately invariant metrics and a neu-ro-fuzzy inference method to accurately classify both two-and three-dimensional objects (using different metrics for each). The system is trained using a small number of images of each object class under varying degrees of the transformations, and as we show expe-rimentally, is then able to identify objects under oth-er non-explicitly-trained degrees of the transforma-tions.
