This paper describes a system developed for segmenting multiband grayscale images into n-class labeled images at high-throughput rates. This system, which we refer to as the segmentation engine, performs supervised im...
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(纸本)0819414786
This paper describes a system developed for segmenting multiband grayscale images into n-class labeled images at high-throughput rates. This system, which we refer to as the segmentation engine, performs supervised image segmentation using algorithms based on the statistical pattern recognition paradigm. So-called 'features' are computed for each pixel and the feature vector thus formed is presented to a statistical classifier, which uses feature information to determine the most probable class of the pixel. algorithms are described for the following: features, automatic feature selection, classification and classifier training. While this paper describes the entire system, the algorithmic approach will be emphasized.
This paper presents an efficient technique for linking edge points in order to generate a closed-contour representation. It is based on the consecutive use of global and local schemes. In both cases it is assumed that...
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This paper presents an efficient technique for linking edge points in order to generate a closed-contour representation. It is based on the consecutive use of global and local schemes. In both cases it is assumed that the original intensity image, as well as its corresponding edge map, are given as inputs to the algorithm. The global scheme computes an initial representation by connecting edge points minimizing a global measure based on spatial information (3D space). It relies on the use of graph theory and exploits the edge points' distribution through the given edge map, as well as their corresponding intensity values. At the same time spurious edge points are removed by a morphological filter The local scheme finally generates closed contours, linking open boundaries, by using a local cost function that takes into account both spatial and topological information. Experimental results with different images, together with comparisons with a previous technique, are presented. (C) 2007 SPIE and IS&T.
image segmentation and its performance evaluation are very difficult but important problems in computer vision. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and o...
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image segmentation and its performance evaluation are very difficult but important problems in computer vision. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity: For general-purpose segmentation, the ground truth and segmentation accuracy may not be well defined, while embedding the evaluation in a specific application, the evaluation results may not be extensible to other applications. We present a new benchmark to evaluate five different image segmentation methods according to their capability to separate a perceptually salient structure from the background with a relatively small number of segments. This way, we not only find a large variety of images that satisfy the requirement of good generality, but also construct ground-truth segmentations to achieve good objectivity. We also present a special strategy to address two important issues underlying this benchmark: (1) most image-segmentation methods are not developed to directly extract a single salient structure;(2) many real images have multiple salient structures. We apply this benchmark to evaluate and compare the performance of several state-of-the-art image segmentation methods, including the normalized-cut method, the watershed method, the efficient graph-based method, the mean-shift method, and the ratio-cut method. (c) 2007 SPIE and IS&T.
The human visual system is capable of detecting and following the course of striated periodic patterns, even under adverse conditions of poor contrast and low signal-to-noise ratio. Sections of a striated pattern of s...
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The human visual system is capable of detecting and following the course of striated periodic patterns, even under adverse conditions of poor contrast and low signal-to-noise ratio. Sections of a striated pattern of subthreshold contrast may be detected easily if other parts of the same pattern have suprathreshold contrast. To simulate these capabilities of the visual system, an imageprocessing algorithm was developed using basic "cells" that are well localized in both the space and spatial frequency domains. These band-limiting, orientation-sensitive "fan filters" are similar in their point spread functions to the two-dimensional Gabor functions commonly used to describe responses of visual cortical cells. These filters are used both to detect the orientation of the striated pattern in a small window and to enhance the image in that orientation. The search for local orientation is limited to a small range based on orientations found in neighboring, overlapping windows. The orientation of the maximally responding cell is used for the enhancement. Results of applying the adaptive directional enhancement to nerve fiber layer photographs, finger-prints, and seismic data are presented.
images whose properties are spatially variant must often be processed locally. Statistical techniques may be required to do this if the image is noisy. These may be difficult to apply when local regions are so small t...
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images whose properties are spatially variant must often be processed locally. Statistical techniques may be required to do this if the image is noisy. These may be difficult to apply when local regions are so small that means, variances, and similar quantities are unstable. We demonstrate how a practical statistical segmentation algorithm may be constructed which operates locally and gives satisfactory global results. The size of the local area over which computations are made has an important effect on the segmentation quality.
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