Visual pattern recognition and visual object recognition are central aspects of high level computer vision systems. This paper describes a method of recognizing patterns and objects in digital images with several type...
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ISBN:
(纸本)0819422355
Visual pattern recognition and visual object recognition are central aspects of high level computer vision systems. This paper describes a method of recognizing patterns and objects in digital images with several types of objects in different positions. The moment invariants of such real world, noise containing images are processed by a neural network, which performs a pattern classification. Two learning methods are adopted for training the network: the conjugate gradient and the Levenber-Maquardt algorithms, both in conjunction with simulated annealing, for different sets of error conditions and features. Real images are used for testing the net's correct class assignments and rejections. We present results and comments focusing on the system's capacity to generalize, even in the presence of noise, geometrical transformations, object shadows and other types of image degradation. One advantage of the artificialneural Network (ANN) employed is its low execution time, allowing the system to be integrated to an assembly industry line for Automated Visual Inspection.
Traditional forecasting models such as the Box-Jenkins ARIMA model are almost all based on models that assume a linear relationship amongst variables and cannot approximate the non- linear relationship that exists amo...
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ISBN:
(纸本)0819420387
Traditional forecasting models such as the Box-Jenkins ARIMA model are almost all based on models that assume a linear relationship amongst variables and cannot approximate the non- linear relationship that exists amongst variables in real-world data such as stock-price data. artificialneuralnetworks, on the other hand, consist of two or more levels of nonlinearity that have been successfully used to approximate the underlying relationships of time series data. neuralnetworks however, pose a design problem: their optimum topology and training rule parameters including learning rate and momentum, for the problem at hand need to be determined. In this paper, we use genetic algorithms to determine these design parameters. In general genetic algorithms are an optimization method that find solutions to a problem by an evolutionary process based on natural selection. The genetic algorithm searches through the network parameter space and the neural network learning algorithm evaluates the selected parameters. We then use the optimally configured network to predict the stock market price of a blue-chip company on the UK market.
Recent studies of the visual cortices of cats and monkeys has led to the development of a new class of artificial neuron models. Eckhorn and his co-workers have developed one such neuron model. They have demonstrated ...
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ISBN:
(纸本)0819421413
Recent studies of the visual cortices of cats and monkeys has led to the development of a new class of artificial neuron models. Eckhorn and his co-workers have developed one such neuron model. They have demonstrated that the recurrent networks of Eckhorn's neurons are capable of duplicating some of the neuro-physiological phenomena observed in cat's visual cortex. We have modified Eckhorn's neuron model in a way that the resulting neuron, referred to as the pulsed coupled neuron, becomes more suitable for imageprocessingapplications than his original model. It has been shown that a single layered laterally connected pulse coupled neural network (PCNN) is capable of smoothing, segmenting digital images. This paper describes an iterative segmentation scheme that utilizes smoothing, segmentation and feature extraction capabilities of PCNN. The knowledge driven iterative segmentation technique is powerful, flexible and has potential in real-time imageprocessing systems.
An important difference between biological vision systems and their electronic counterparts is the large number of feedback signals controlling each aspect of the image collection process. For every forward path of in...
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ISBN:
(纸本)0819421413
An important difference between biological vision systems and their electronic counterparts is the large number of feedback signals controlling each aspect of the image collection process. For every forward path of information in the brain, from sensor to comprehension, there appears to be several neural bundles which send information back to the sensor to modify the way the information is collected. In this paper we will examine the role of such feedback signals and suggest algorithms for intelligent processing of images directly on the focal plane, using feedback. We consider first what form these signals might take and how they can be used to implement functions common to conventional imageprocessing with the objective of moving the computation out of the digital domain and place much of its on the focal plane, or analog processing close to the focal plane. While this work falls under the general heading of artificialneuralnetworks, it goes beyond the static processing of signals suggested by the McCulloch and Pitts model of the neuron and the Laplacian imageprocessing suggested by Carver Mead by including the dynamics of temporal encoding in the analysis process.
Edge detection can be treated as a problem in optimization - a problem that can be solved with a Hopfield neural network. This paper describes a neural-network based model capable of producing high-quality edge imagery.
ISBN:
(纸本)0819421413
Edge detection can be treated as a problem in optimization - a problem that can be solved with a Hopfield neural network. This paper describes a neural-network based model capable of producing high-quality edge imagery.
Today optical measuring devices are used in many applications. The measurement accuracy should be very good. But when operating with image signals, irregularities of the scanning system must often be corrected. Blur, ...
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Today optical measuring devices are used in many applications. The measurement accuracy should be very good. But when operating with image signals, irregularities of the scanning system must often be corrected. Blur, geometric distortion and unequal brightness distribution can lead to difficulties during further processing of an image. In the following, it is shown how an artificialneural Network can be applied to image restoration. In order to calibrate the correcting system the weights of the neural network are trained. Using suitable training patterns and an appropriate optimization criterion for the degraded images, in the result the dimensioned network represents a space-variant filter with a behavior similar to the well-known Wiener filter. A pipeline processor simulates a neural network operating in real time. Theoretical considerations and experimental results are given in this paper.
Active vision refers to a purposeful change in the camera setup to aid the processing of visual information. An important issue in using active vision is the need to represent the 3D environment in a manner that is in...
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ISBN:
(纸本)0819421413
Active vision refers to a purposeful change in the camera setup to aid the processing of visual information. An important issue in using active vision is the need to represent the 3D environment in a manner that is invariant to changing camera configurations. Conventional methods require precise knowledge of various camera parameters in order to build this representation. However, these parameters are prone to calibration errors. This motivates us to explore a neural network based approach using Vector Associative Map to learn the invariant representation of 3D point targets for active vision. An efficient learning scheme is developed that is suitable for robotic implementation. The representation thus learned is also independent of the intrinsic parameters of the imaging system, making it immune to systematic calibration errors. To evaluate the effectiveness of this scheme, computer simulations were first performed using a detailed model of the University of Illinois Active Vision System (UIAVS). This is followed by an experimental verification on the actual UIAVS. Several robotic applications are then explored that utilize the invariance property of the learned representation. These applications include motion detection, active vision based robot control, robot motion planning, and saccade sequence planning.
Typical vision-based vehicle detection systems use a video camera mounted on an overpass or an adjacent utility pole to observe vehicles passing on the road. Classical image-processing techniques are applied to the di...
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Typical vision-based vehicle detection systems use a video camera mounted on an overpass or an adjacent utility pole to observe vehicles passing on the road. Classical image-processing techniques are applied to the digitized video image to obtain pulse and presence signals, traditionally produced by inductive loop detectors. These image-processing techniques can also be performed by artificialneuralnetworks. Using a neural network requires that the network be first trained on several example video images in which the position of the vehicle is already indicated by a human operator. The trained network is then used to locate and track vehicles in images it has never been exposed to. Recently, Nestor Inc. and Intel Corporation have developed a hardware chip, called Ni1000 Recognition Accelerator, which is capable of implementing Radial Basis Function (RBF) networks in real time. This paper describes the results of converting a software feedforward network based detection system to a real time hardware implemented RBF network based detection system. Success rates greater than 90% were obtained for the RBF network based detection system.
Receptive field structures found in the visual cortex of the mammalian brain act as oriented, localized spatial frequency filters. There has been interest in the use of such receptive field profiles for image coding a...
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Receptive field structures found in the visual cortex of the mammalian brain act as oriented, localized spatial frequency filters. There has been interest in the use of such receptive field profiles for image coding and texture processing. These receptive field structures resemble Gabor filters. Systems employing such Gabor filters have been implemented in software for a variety of applications. We believe a hardware implementation of such cells will be helpful in artificial visual processing. We have implemented analog VLSI cells whose outputs resemble the receptive field profiles found in the visual cortex. We describe experimental results of our circuit. Our circuit is the first silicon model of visual cortical processing.
This paper uses a high level vision model to describe the information passing and linking within the primate visual system. Information linking schemes, such as state dependent modulation and temporal synchronization,...
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ISBN:
(纸本)0819421413
This paper uses a high level vision model to describe the information passing and linking within the primate visual system. Information linking schemes, such as state dependent modulation and temporal synchronization, are presented as methods the vision system uses to combine information using expectation to fill in missing information and remove unneeded information. The possibility of using linking methods derived from physiologically based theoretical models to combine current imageprocessing techniques for pattern recognition purposes is investigated. These imageprocessing techniques are transforms such as (but not limited to) wavelet filters, hit or miss filters, morphological filters, and difference of gausian filters. These particular filters are chosen because they simulate functions that are performed in the primate visual system. To implement the physiologically motivated linking methods, the Pulse Coupled neural Network (PCNN) is chosen as a basic building block for the vision model which performs linking at the neuronal pulse level. Last, an image fusion network which incorporates information linking based on the PCNN is described, and initial results are presented.
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