Automated target recognition (ATR) software has been designed to perform image segmentation and scene analysis. Specifically, this software was developed as a package for the Army's Minefield and Reconnaissance an...
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We present here an algorithm which performs radar cross-section estimation by using techniques based on simulated annealing. Standard simulated annealing approaches to image restoration attempt to categorize each imag...
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ISBN:
(纸本)0819415472
We present here an algorithm which performs radar cross-section estimation by using techniques based on simulated annealing. Standard simulated annealing approaches to image restoration attempt to categorize each image element as belonging to one of a small number of predefined image states or values. This is restrictive for tasks such as radar cross-section estimation and we present here an algorithm which is capable of producing a real-valued output. This is achieved by introducing an edge detection stage into the simulated annealing process. The action of the annealing algorithm may be viewed as a filter which adapts to local image structure. We present results which demonstrate this behavior and in so doing allow us to estimate the residual noise levels we might expect.
PREENS - a Parallel Research Execution Environment for neural Systems - is a distributed neurosimulator, targeted on networks of workstations and transputer systems. As current applications of neuralnetworks often co...
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The proceedings contain 79 papers. The topics discussed include: a consequence-finding approach for feature recognition in CAPP;case-based block division planning in shipbuilding;self-organizing compensating informati...
ISBN:
(纸本)2884491287
The proceedings contain 79 papers. The topics discussed include: a consequence-finding approach for feature recognition in CAPP;case-based block division planning in shipbuilding;self-organizing compensating information scheduler for computer integrated manufacturing;an environment for self-testing of logic programs;quality control of software specifications written in natural language;a user-centric methodology for building usable expert systems;fingerprint image compression by a clustering learning network;power system voltage instability monitoring with artificialneuralnetworks;an algebraic method to evaluate spatial stability in imageprocessing neuro chips;the use of neural network to predict welding parameters;and principled modeling and automatic classification for enhancing the reusability of problem-solving methods of expert systems.
Automated target recognition (ATR) software has been designed to perform image segmentation and scene analysis. Specifically, this software was developed as a package for the Army's Minefield and Reconnaissance an...
详细信息
ISBN:
(纸本)0819415472
Automated target recognition (ATR) software has been designed to perform image segmentation and scene analysis. Specifically, this software was developed as a package for the Army's Minefield and Reconnaissance and Detector (MIRADOR) program. MIRADOR is an on/off road, remote control, multisensor system designed to detect buried and surface- emplaced metallic and nonmetallic antitank mines. The basic requirements for this ATR software were the following: (1) an ability to separate target objects from the background in low signal-noise conditions;(2) an ability to handle a relatively high dynamic range in imaging light levels;(3) the ability to compensate for or remove light source effects such as shadows;and (4) the ability to identify target objects as mines. The image segmentation and target evaluation was performed using an integrated and parallel processing approach. Three basic techniques (texture analysis, edge enhancement, and contrast enhancement) were used collectively to extract all potential mine target shapes from the basic image. Target evaluation was then performed using a combination of size, geometrical, and fractal characteristics, which resulted in a calculated probability for each target shape. Overall results with this algorithm were quite good, though there is a tradeoff between detection confidence and the number of false alarms. This technology also has applications in the areas of hazardous waste site remediation, archaeology, and law enforcement.
Near-simultaneous, multispectral, coregistered imagery of ground target and background signatures were collected over a full diurnal cycle in visible, infrared, and ultraviolet spectrally filtered wavebands using Batt...
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ISBN:
(纸本)0819415189
Near-simultaneous, multispectral, coregistered imagery of ground target and background signatures were collected over a full diurnal cycle in visible, infrared, and ultraviolet spectrally filtered wavebands using Battelle's portable sensor suite. The imagery data were processed using classical statistical algorithms, artificialneuralnetworks and data clustering techniques to classify objects in the imaged scenes. imagery collected at different times throughout the day were employed to verify algorithm robustness with respect to temporal variations of spectral signatures. In addition, several multispectral sensor fusion medical imaging applications were explored including imaging of subcutaneous vasculature, retinal angiography, and endoscopic cholecystectomy. Work is also being performed to advance the state of the art using differential absorption lidar as an active remote sensing technique for spectrally detecting, identifying, and tracking hazardous emissions. These investigations support a wide variety of multispectral signature discrimination applications including the concepts of automated target search, landing zone detection, enhanced medical imaging, and chemical/biological agent tracking.
In the classification of chromosomes, as well as in many other applications, patterns must be recognized regardless of the orientation of the image. This study investigates the performance of two neuralnetworks, LVQ ...
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In the classification of chromosomes, as well as in many other applications, patterns must be recognized regardless of the orientation of the image. This study investigates the performance of two neuralnetworks, LVQ and ARTMAP, that are based on a combination of clustering and supervised learning. Each network was trained with images of several chromosome pairs, presented in a few different rotations, and tested with the training patterns at various intermediate rotations. Successful recognition was achieved over a wide range of rotation angles. Comparisons of the two nets for this application also illustrate the characteristics that influence their suitability for a variety of imageprocessing problems.
We present a new methodology for describing the functioning of artificial neurons, including new, as yet untested, types of behaviour. It also provides the possibility of defining artificial neurons of any order, and ...
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We present a new methodology for describing the functioning of artificial neurons, including new, as yet untested, types of behaviour. It also provides the possibility of defining artificial neurons of any order, and a wide range of functions from which to choose. As an illustration of the new formulation, a practical realization is analyzed, consisting of a multilayered neural network applied to the imageprocessing of black and white scenes, making manifest the possibilities of this new type of neuron in the field of cellular logic but with new types of processing. This is just an early stage in the development of the new neurons, so that many of their possible applications have yet to be initiated. Among them, one can already foresee those related to fuzzy models, analogue models, and many others. For these applications, it will no longer be necessary to make any change in the network design, just to make a choice from the proposed library of functions.< >
A straightforward method for implementing a neural network (NN) solution in the field of imageprocessing, for attitude and position determination, is presented in this paper. The proposed evolutive training algorithm...
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A straightforward method for implementing a neural network (NN) solution in the field of imageprocessing, for attitude and position determination, is presented in this paper. The proposed evolutive training algorithm is capable of setting the appropriate dimension of the neural network and the adequate weights interconnecting the neurons. The recommended solution is based on simulation results.< >
The incorporation of information-processing technology into analytical systems in the form of standard computing software has recently been advanced by the introduction of artificial intelligence (AI) both as expert s...
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The incorporation of information-processing technology into analytical systems in the form of standard computing software has recently been advanced by the introduction of artificial intelligence (AI) both as expert systems and as neuralnetworks. This paper considers the role of software in system operation, control and automation and attempts to define intelligence. AI is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engineers that have programmed them to represent the real world. neuralnetworks are intended to emulate the pattern-recognition and parallel-processing capabilities of the human brain and are taught rather than programmed. The future may lie in a combination of the recognition ability of the neural network and the rationalization capability of the expert system. In the second part of this paper, examples are given of applications of AI in stand-alone systems for knowledge engineering and medical diagnosis and in embedded systems for failure detection, image analysis, user interfacing, natural language processing, robotics and machine learning, as related to clinical laboratories. It is concluded that AI constitutes a collective form of intellectual property and that there is a need for better documentation, evaluation and regulation of the systems already being used widely in clinical laboratories.
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