Region-growing segmentationalgorithms are useful for remote sensing imagesegmentation. These algorithms need the user to supply control parameters, which control the quality of the resulting segmentation. An objecti...
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Region-growing segmentationalgorithms are useful for remote sensing imagesegmentation. These algorithms need the user to supply control parameters, which control the quality of the resulting segmentation. An objective function is proposed for selecting suitable parameters for region-growing algorithms to ensure best quality results. It considers that a segmentation has two desirable properties: each of the resulting segments should be internally homogeneous and should be distinguishable from its neighbourhood. The measure combines a spatial autocorrelation indicator that detects separability between regions and a variance indicator that expresses the overall homogeneity of the regions.
This paper proposes an automatic image segmentation algorithm. Our hierarchical algorithm uses recursive segmentation that consists of two major steps. First, local thresholding is carried out by the fuzzy hit-or-miss...
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This paper proposes an automatic image segmentation algorithm. Our hierarchical algorithm uses recursive segmentation that consists of two major steps. First, local thresholding is carried out by the fuzzy hit-or-miss operator, which allows dynamic separation of a grey-scale image into two classes, based on local intensity distributions. The fuzzy hit-or-miss, being an operator of fuzzy mathematical morphology, plays an important role in performing the dynamic local segmentation. This operator gives a better shape description than global thresholding methods. It also retains small but significant regions in satellite images. Second, the homogeneity index is measured in each class based on the quality of normalized intra-region uniformity. The proposed method has been tested using both synthetic and satellite images successfully;moreover, the algorithm can estimate the number of classes automatically.
Medical imaging often involves the injection of contrast agents and the subsequent analysis of tissue enhancement patterns. Many important types of tissue have characteristic enhancement patterns;for example, in MR ma...
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Medical imaging often involves the injection of contrast agents and the subsequent analysis of tissue enhancement patterns. Many important types of tissue have characteristic enhancement patterns;for example, in MR mammography, malignancies exhibit a characteristic "wash out" temporal pattern, while in MR angiography, arteries, veins and parenchyma each have their own distinctive temporal signature. In such time resolved image series, there are substantial changes in intensities;however, this change is due primarily to the contrast agent, rather than to motion. As a result, the task of automatically segmenting contrast-enhanced images poses interesting new challenges. In this paper, we propose a new image segmentation algorithm for time resolved image series with contrast enhancement, using a model-based time series analysis of individual pixels. We take an energy minimization approach to ensure spatial coherence. The energy is minimized in an expectation-maximization fashion that alternates between segmenting the image into a number of non-overlapping regions and finding the temporal profile parameters which describe the behavior of each region. Preliminary experiments on MR angiography and MR mammography studies show the algorithm's ability to find an accurate segmentation.
We present here art implementation of NeTra, a prototype image retrieval system that uses color, texture, shape and spatial location information in segmented image regions to search and retrieve similar regions from t...
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ISBN:
(纸本)0818681837
We present here art implementation of NeTra, a prototype image retrieval system that uses color, texture, shape and spatial location information in segmented image regions to search and retrieve similar regions from the database. A distinguishing aspect of this system is its incorporation of a robust automated image segmentation algorithm that allows object or region based search. imagesegmentation significantly improves the quality of image retrieval when images contain multiple complex objects. Another important components of the system include an efficient color representation, and indexing of color, texture, and shape features for fast search and retrieval. This representation allows the user to compose interesting queries such as ''retrieve all images that contain regions that have the color of object A, texture of object B, shape of object C, and lie in the upper one-third of the image'' where the individual objects could be regions belonging to different images.
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