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We propose a visual recognition system for robotic applications in which distance to the visual objects can change a lot (for instance, trying to recognize a distant object learned from a short distance). Our system takes advantage of a single pan-tilt camera controllable in zoom and focus. Focus control allows to detect plans of sharpness in the scene and indirectly to compute a distance. Hence,...
Web advertising has become a major industry and many advertisements appear in the form of images. Although it makes considerable profit, these advertisements tend to disturb the internet surfing of normal users. Moreover, they always bring extra burden in indexing to commercial image search engines. In this paper we present the development and performance of a Neural Network (NN) for advertising images...
In order to decrease negative effects brought by the particularity and complexity of imaging environment, and satisfy the real-time need of the underwater task, combined invariant moments are extracted as recognition features. Furthermore, an underwater target recognition system based on neural network which improved by Artificial Fish Swarm Algorithm (AFSA) is proposed. AFSA is of capable of attaining...
Neuroscience has revealed many properties of neurons and of the functional organization of visual cortex that are believed to be essential to human vision, but are missing in standard artificial neural networks. Equally important may be the sheer scale of visual cortex requiring ~1 petaflop of computation, while the scale of human visual experience greatly exceeds standard computer vision datasets:...
An approach to identify visual quality of nonwoven products by combining wavelet transform and learning vector quantization (LVQ) neural network is proposed in this paper. 625 nonwoven images of 5 different visual quality grades, each including 125 images, are decomposed at four different levels using five wavelet bases of the Daubechies family. The energy values L2 extracted from the high frequency...
This work discusses a hybrid structure that conjugates connectionist associative memories and deterministic automata, for the implementation of context dependent pattern recognition. The associative component of the hybrid system is built through coupled recursive maps with bifurcation and chaotic dynamics (recursive processing elements - RPEs). Its output feeds a deterministic state machine that...
In this paper we briefly summarize the fundamental properties of spike events processing applied to artificial vision systems. This sensing and processing technology is capable of very high speed throughput, because it does not rely on sensing and processing sequences of frames, and because it allows for complex hierarchically structured cortical-like layers for sophisticated processing. The paper...
Object class recognition is a highly challenging area in computer vision and machine learning. In this paper, we introduce a novel approach to object class recognition using Neuro Evolution of Augmenting Topologies (NEAT) to evolve artificial neural networks (ANN) capable of taking advantage of the robust SIFT feature based descriptor histograms. We claim that NEAT can produce ANN classifier which...
A multimodal interface can achieve more natural and effective human-computer interaction. In this paper, we present an isolated-word recognizer using a fusion of speech and natural visual gestures. The fusion of audio and visual signals can be carried out either at the class level or the feature level. Our system incorporates a fusion system at the feature level which supports 10 natural gestures...
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