An examination is made of the fuzzy approach of E. H. Ruspini to the problem of pattern classification. The problem of classification as that of estimating a partition of the data to be classified is presented. An alg...
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An examination is made of the fuzzy approach of E. H. Ruspini to the problem of pattern classification. The problem of classification as that of estimating a partition of the data to be classified is presented. An algorithm is presented for classifying data issued from a Gaussian environment; the fundamental tool of this algorithm is the use of numerical filters for estimating a set of parameters which characterize each class. This algorithm has been applied to the recognition of the components of a mixture of Normal distributions.
Consideration is given to a class of patterns which are subject to the action of a group of transformations. The author is particularly concerned with the existence of measurements or features which are invariant with...
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Consideration is given to a class of patterns which are subject to the action of a group of transformations. The author is particularly concerned with the existence of measurements or features which are invariant with respect to transformation. A concept of relative invariance is also introduced and explored in depth. In a very general sense, it is shown that every invariant (and relative invariant) is a suitable average over the relevant group of transformations. Finally, invariant means of bounded functions are used to explore existence of pattern invariants. Suggestions for further research are also given.
Landuse classification is an important problem in the remote sensing field. It can be used in a wide range of applications. In this paper we propose a hybrid method fusing edges and regions information for the landuse...
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ISBN:
(纸本)0769523722
Landuse classification is an important problem in the remote sensing field. It can be used in a wide range of applications. In this paper we propose a hybrid method fusing edges and regions information for the landuse classification of multispectral images. It mainly includes the steps of image pre-processing. initial segmentation and region merging. Especially, a novel spatial mean shift procedure is proposed so that some information can be extracted and used in the successive steps. Aiming at the multispectral images processing. we also design a band weighting strategy that give a proper weight to each band adaptively according to the region to be processed. Experimental results on the Landsat TM and ETM+ images validate the performance of the proposed method.
A method is described that generates 3D descriptions of arbitrarily-curved surfaces of physical objects by imageprocessing.and photogrammetric techniques. A surface that is to be described is illuminated by a pattern...
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A method is described that generates 3D descriptions of arbitrarily-curved surfaces of physical objects by imageprocessing.and photogrammetric techniques. A surface that is to be described is illuminated by a pattern of lines. The 3D shape of the surface causes the pattern to be distorted. The illuminated parts of the surface are photographed from two or more vantage points and the resulting images are digitized. The 2D perspective projection of the surface-distorted line pattern is extracted from each digitized image. Camera calibration marks are also extracted and the position of the camera is determined for each image. Two or more projections of the line pattern are then matched and the line pattern projected on the surface is reconstructed into 3D.
Let a vector of probabilities be associated with every node of a graph. These probabilities define a random variable representing the possible labels of the node. Probabilities at neighboring nodes are used iterativel...
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Let a vector of probabilities be associated with every node of a graph. These probabilities define a random variable representing the possible labels of the node. Probabilities at neighboring nodes are used iteratively to update the probabilities at a given node based on statistical relations among node labels. The results are compared with previous work on probabilistic relaxation labeling, and examples are given from the image segmentation domain. References are also given to applications of the new scheme in text processing.
The potential uses of Charge Transfer Devices (CTDs) in pattern classification operations are explored. The needs for a hardware-based pattern classifier are established, and a matrix multiplication subsystem based up...
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The potential uses of Charge Transfer Devices (CTDs) in pattern classification operations are explored. The needs for a hardware-based pattern classifier are established, and a matrix multiplication subsystem based upon a sum-of-products CTD is presented. Applications of the subsystem to the classification of multi-modal Gaussian distributions in general and to LANDSAT data processing.in particular are discussed. The potential impact of this technology on satellite data processing.methodologies is discussed.
This work explores a statistical basis for a process often described in computer vision: image segmentation by region merging following a particular order in the choice of regions. We exhibit a particular blend of alg...
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This work explores a statistical basis for a process often described in computer vision: image segmentation by region merging following a particular order in the choice of regions. We exhibit a particular blend of algorithmics and statistics whose error is, as we formally show, close to the best possible. This approach can be approximated in a very fast segmentation algorithm for processing.images described using most common numerical feature spaces. Simple modifications of the algorithm allow to cope with occlusions and/or hard noise levels. Experiments on grey-level and color images, obtained with a short C-code, display the quality of the segmentations obtained.
Many applications in picture processing.require accurate slope estimation. A brief survey of these applications is followed by discussion of the Sobel edge detection operator. The Sobel operator is very fast and simpl...
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Many applications in picture processing.require accurate slope estimation. A brief survey of these applications is followed by discussion of the Sobel edge detection operator. The Sobel operator is very fast and simple;however, it is inherently biased as a function of true slope value and displacement of the true edge from center of the processing.window. An iterative version of the operator is presented which reduces this bias with only a moderate increase in required processing.
When analyzing structural descriptions of pictures (e. g. strings of symbols) for recognition, some reduced data stored in a data base (e. g. names of objects) are often substituted for substrings. If substrings were ...
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When analyzing structural descriptions of pictures (e. g. strings of symbols) for recognition, some reduced data stored in a data base (e. g. names of objects) are often substituted for substrings. If substrings were contents addressable in the data base, each reduction step would just involve a memory access instead of lengthy retrieval processes. The data structures and algorithms required to first simulate and later build such an associative memory are presented.
Machine vision systems are often confronted with images of great complexity. Preprocessing. region growing, and edge extraction algorithms must rely on their underlying image models in order to make effective use of t...
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Machine vision systems are often confronted with images of great complexity. Preprocessing. region growing, and edge extraction algorithms must rely on their underlying image models in order to make effective use of the data. This study presents an alternate approach to the definition of an image model. It also discusses an algorithm, based on this model, which reduces noise in a digitized image without destroying the information contained within the image. This algorithm is based upon a neighbor-weighting function which incorporates spatial separation as well as intensity difference and is modulated by the variance in the local neighborhood.
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