Narrow Results

Fast and robust classification of feature vectors is a crucial task in a number of real-time systems. A cellular neural/nonlinear network universal machine (CNN-UM) can be very efficient as a feature detector. The next step is to post-process the results for object recognition. This paper shows how a robust classification scheme based on adaptive resonance theory (ART) can be mapped to the CNN-UM. Moreover, this mapping is general enough to include different types of feed-forward neural networks. The designed analogic CNN algorithm is capable of classifying the extracted feature vectors keeping the advantages of the ART networks, such as robust, plastic and fault-tolerant behaviors. An analogic algorithm is presented for unsupervised classification with tunable sensitivity and automatic new class creation. The algorithm is extended for supervised classification. The presented binary feature vector classification is implemented on the existing standard CNN-UM chips for fast classification. The experimental evaluation shows promising performance after 100% accuracy on the training set.

We are watching the news on TV: the change of the background tells us when a new story begins. A glance at the clock and we can clearly see what time it is. These are special spatial-temporal episodes caused by ballistic eye movements and sudden optical changes. In this paper we give a useful definition for generalized sudden global change events, present its main properties and give an algorithm to recognize them in any video-flow. The proposed algorithm is implemented on a standard Cellular Nonlinear Network Universal Machine (CNN-UM). The processing time of the detection is roughly one millisecond on the ACE4k CNN-UM chip in the Aladdin environment. Therefore it can serve as a common function in any online, real-time spatial-temporal algorithm. The sudden global change event can be used to define the instant when the time-dependent part of that algorithm, e.g., homotopy, has to be initialized. The first steps of the multimedia processing, such as searching for key frames — for compression, browsing or indexing — can be helped by the proposed algorithm as a robust video shot change detection method.