A two-dimensional scene is analyzed statistically by superimposing on it a rectangular grid and studying each square picture element (pixel) as one unit of the entire picture. With each pixel we want to associate one ...
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A two-dimensional scene is analyzed statistically by superimposing on it a rectangular grid and studying each square picture element (pixel) as one unit of the entire picture. With each pixel we want to associate one ofKlabels, but the label must be determined. Labels may be related to one another spatially, reflecting an underlying pattern in the scene. There is a noisy observation vector associated with each pixel, and these vectors are used contextually to classify the pixels; that is, to determine their labels in an effort to reconstruct the true scene. The discretized picture and theKlabels constitute a lattice structure. We assume there is prior information available to assist in the reconstruction. The adaptive Bayesian classification (ABC) procedure proposed is iterative. It starts with a formal predictive contextual Bayesian classification of the entire map, then proceeds by adaptively reclassifying all of the labels in the map at each iteration using an empirical Bayesian updating algorithm. The ABC algorithm provides an approximation to a local maximum for the joint posterior classification probability of all pixels in the map. The procedure attempts to improve on other such iterative procedures by reestimating parameters and reclassifying pixels by conditioning initially on prior information via the predictive Bayesian paradigm. It is “adaptive Bayesian” in that its initial reconstruction stage is formal Bayesian, and it also uses a Bayes theorem-type argument for updating the data at each iteration. Applications include imageprocessing of remotely sensed satellite data, photon emission tomography, and computer vision.
The success of automatic patternrecognition systems depends on the enhancement of significant features in relation to the irrelevant background information in the pre-processing stage. In images, the objects of relev...
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The success of automatic patternrecognition systems depends on the enhancement of significant features in relation to the irrelevant background information in the pre-processing stage. In images, the objects of relevance are generally enhanced by linear/non-linear stretching, histogram equalization and spatial filtering, which are all operations in a single band (univariate). In a multivariate space, linear transformations such as principal component analysis are very popular for this purpose. A simple rotation of axes, as in the principal component transformation, to the maximum variance direction is often insufficient to enhance objects camouflaged by the background. This is often due to the enhancement of the background together with the features of interest or non-background. In this paper a new technique is presented to address this problem. The images are modelled as having two classes, namely background and non-background. The technique, called background discriminant transformation (BDT), is designed to maximize the non-background class variance relative to the background variance. The technique has applications to image enhancement in mineral exploration, planetary sciences, biological and medical sciences, and defence applications.
Neural networks are self-organizing systems of simple interconnected processing units. They do well at solving complex patternrecognition problems implicit in understanding continuous speech, identifying hand-written...
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Neural networks are self-organizing systems of simple interconnected processing units. They do well at solving complex patternrecognition problems implicit in understanding continuous speech, identifying hand-written characters, and determining that a target seen from different angles is in fact one and the same object. The most important application areas for neural patternrecognition are: remotesensing, medical image analysis, industrial computer vision, and input devices for computers. This paper examines the utility of one layer neural network in aerial image classification. The model used for this work is trained with the delta rule. It was found that this model was faster than most of the neural network models used for image classification. With this neural network model, good results can be obtained comparable to conventional methods.
This paper outlines such a Heterogeneous Distributed Memory Parallel Processor (HDMPP) and discusses the software environment that is required to take advantage of it. These HDMPP systems have been applied to practica...
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This paper outlines such a Heterogeneous Distributed Memory Parallel Processor (HDMPP) and discusses the software environment that is required to take advantage of it. These HDMPP systems have been applied to practical applications including: high definition television, radar, sonar, medical imaging, remotesensing, machine, vision, data compression, image enhancement, and patternrecognition. These examples are used to illustrate how the HDMPP systems were applied and how applications have been prototyped, developed, optimized and deployed on a single commercial off-the-shelf product.
The problem of matching a model consisting of the point features of a flat object to point features found in an image that contains the object in an arbitrary three-dimensional pose is addressed. Once three points are...
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ISBN:
(纸本)0818621486
The problem of matching a model consisting of the point features of a flat object to point features found in an image that contains the object in an arbitrary three-dimensional pose is addressed. Once three points are matched, it is possible to determine the pose of the object. Assuming bounded sensing error, the author presents a solution to the problem of determining the range of possible locations in the image at which any additional model points may appear. This solution leads to an algorithm for determining the largest possible matching between image and model features that includes this initial hypothesis. He has implemented a close approximation to this algorithm, which is O(nmΕ6), where n is the number of image points, m is the number of model points, and Ε is the maximum sensing error. This algorithm is compared to existing methods, and it is shown that it produces more accurate results.
The E-filter is a homomorphic filter proposed by D.J.H. Moore and D.J. Parker (1973) which differentiates the small and large amplitude components of a signal. A description is given of a generalization of the E-filte...
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The E-filter is a homomorphic filter proposed by D.J.H. Moore and D.J. Parker (1973) which differentiates the small and large amplitude components of a signal. A description is given of a generalization of the E-filter to include a control parameter. Selective tuning of this parameter causes the behavior of the E-filter to range from linear to highly nonlinear. It is found that the E-filter can be simply implemented and has several properties which make it attractive for use in edge detection problems. The superiority of the E-filter to sale-space filtering for edge detection in one-dimensional systems is shown.
Automating image interpretation requires applying a combination of imageprocessing, cartographic information, and domain- and task-specific knowledge-based reasoning to a particular task. A group of projects in agric...
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Automating image interpretation requires applying a combination of imageprocessing, cartographic information, and domain- and task-specific knowledge-based reasoning to a particular task. A group of projects in agriculture and forestry for which spatial and general domain knowledge were used to guide and improve image segmentation and patternrecognition is reviewed. The nature of the knowledge used in these projects is found to be either spatial information which can be coded into a GIS (geographic information system) or metaknowledge about processes and methodologies at the image-processing or cartographic levels. A trimodal diagram is shown for evaluating explicitly the different levels of knowledge and the flow of control between them. A trimodal scheme is presented that shows the relationships between the iconic processes, the cartographic (or symbolic) processes and the knowledge processes. It is shown that these processes form a hierarchy of levels moving from more concrete to more abstract processes. The flow of control in a typical task begins with the lower levels and migrates to the higher levels as the task proceeds. A software system for uniting the various components is outlined, and the present state of its implementation is discussed. It is noted that an integrated database which directly supports all three levels is needed for the system to work efficiently.
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