Synergetic computers and cognition: a top-down approach to neural nets
Synergetic computers and cognition: a top-down approach to neural nets
Local Invariants For Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
Visual Image Retrieval by Elastic Matching of User Sketches
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetry Detection by Generalized Complex (GC) Moments: A Close-Form Solution
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Object-Oriented Framework for Content-Based Image Retrieval Based on 5-Tier Architecture
APSEC '99 Proceedings of the Sixth Asia Pacific Software Engineering Conference
An FFT-based technique for translation, rotation, and scale-invariant image registration
IEEE Transactions on Image Processing
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Synergetic Neural Network (SNN) as proposed by Hermann Haken is a novel top-down self-organized system. In this chapter, its associated discrete SNN is proposed and the recognition stability and the convergence of a generalized discrete SNN is analyzed. We propose an adaptive algorithm of iterative step length refinement for synergetic recognition, which can ensure fast convergence and network steadily for all kinds of input pattern. Additionally, we apply the SNN to trademark retrieval and study its ability to support affine invariant retrieval of 2D patterns. To this end, we propose an affine invariant input vector in the frequency domain for the SNN to evaluate the retrieval ability of such networks for different types of input queries, for example, query by complete trademark pattern and query by image components. We show experimentally that our proposed SNN method is noise tolerant as well as able to support affine invariant retrieval. This led us to propose a novel paradigm for trademark retrieval based on visual keywords whereby trademark images can be queried in terms of simple geometric components.