The Experts below are selected from a list of 36 Experts worldwide ranked by ideXlab platform
John Goutsias - One of the best experts on this subject based on the ideXlab platform.
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Grayscale Level connectivity: theory and applications
IEEE Transactions on Image Processing, 2004Co-Authors: Ulisses Braga-neto, John GoutsiasAbstract:A novel notion of connectivity for Grayscale images is introduced, defined by means of a binary connectivity assigned at image-Level sets. In this framework, a Grayscale image is connected if all Level sets below a prespecified threshold are connected. The proposed notion is referred to as Grayscale Level connectivity and includes, as special cases, other well-known notions of Grayscale connectivity, such as fuzzy Grayscale connectivity and Grayscale blobs. In contrast to those approaches, the present framework does not require all image-Level sets to be connected. Moreover, a connected Grayscale object may contain more than one regional maximum. Grayscale Level connectivity is studied in the rigorous framework of connectivity classes. The use of Grayscale Level connectivity in image analysis applications, such as object extraction, image segmentation, object-based filtering, and hierarchical image representation, is discussed and illustrated.
Tang Shuo - One of the best experts on this subject based on the ideXlab platform.
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a segmentation approach based on Grayscale Level connectivity in ir image
Computer Science, 2007Co-Authors: Tang ShuoAbstract:A new segmentation approach is proposed based on Grayscale Level connectivity in IR image. In this connectivity, a Grayscale image is connected if all Level sets below a prespecified threshold are connected. First, the Level-k characteristic opening using appropriate thresholding "keeps" only the regional maxima of image that are at or above Level k, and flattens the rest. Second, Level-k connected component decomposing based on target size can extract connected component that contain the objects of interest. The end step can segment target combining with simple thresholding. Experimental results in real IR images demonstrate the effectiveness and robustness of the proposed approach to enhance signal noise ratio, extract target and segment image.
Ulisses M Braganeto - One of the best experts on this subject based on the ideXlab platform.
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Grayscale Level multiconnectivity
International Symposium on Memory Management, 2005Co-Authors: Ulisses M BraganetoAbstract:In [5], a novel concept of connectivity for Grayscale images was introduced, which is called Grayscale Level connectivity. In that framework, a Grayscale image is connected if all its threshold sets below a given Level are connected. It was shown that Grayscale Level connectivity defines a connection, in the sense introduced by Jean Serra in [10]. In the present paper, we extend Grayscale Level connectivity to the case where different connectivities are used for different threshold sets, a concept we call Grayscale Level multiconnectivity. In particular, this leads to the definition of a new operator, called the multiconnected Grayscale reconstruction operator. We show that Grayscale Level multiconnectivity defines a connection, provided that the connectivities used for the threshold sets obey a nesting condition. Multiconnected Grayscale reconstruction is illustrated with an example of scale-space representation.
Ulisses Braga-neto - One of the best experts on this subject based on the ideXlab platform.
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Grayscale Level connectivity: theory and applications
IEEE Transactions on Image Processing, 2004Co-Authors: Ulisses Braga-neto, John GoutsiasAbstract:A novel notion of connectivity for Grayscale images is introduced, defined by means of a binary connectivity assigned at image-Level sets. In this framework, a Grayscale image is connected if all Level sets below a prespecified threshold are connected. The proposed notion is referred to as Grayscale Level connectivity and includes, as special cases, other well-known notions of Grayscale connectivity, such as fuzzy Grayscale connectivity and Grayscale blobs. In contrast to those approaches, the present framework does not require all image-Level sets to be connected. Moreover, a connected Grayscale object may contain more than one regional maximum. Grayscale Level connectivity is studied in the rigorous framework of connectivity classes. The use of Grayscale Level connectivity in image analysis applications, such as object extraction, image segmentation, object-based filtering, and hierarchical image representation, is discussed and illustrated.
Hu Xin - One of the best experts on this subject based on the ideXlab platform.
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image segment based on Grayscale Level connectivity
International Conference Signal Processing Systems, 2010Co-Authors: Hu XinAbstract:The Level-k connectivity was only mentioned by Braga-Neto et al. We extract the Level-k connectivity component of target in FLIR image sequences to segment image. At first, we explain the new concept about the Level-k connectivity. In this abstract, we just add some pertinent remarks to listing the topic of explanation. The first topic is: the Grayscale Level connectivity on complete lattice. In the first topic, we discuss three questions: (1) the connectivity based on complete lattice, (2) the Level-k connectivity and (3) the operator based on the Level-k connectivity. The second topic is: the experiment in FLIR image. In the second topic, we discuss FLIR image processing. Experimental results are given in a table in the full paper. These results indicate that using new operator raises the value of SNR. These results demonstrate the effectiveness and robustness of the operator for dim target detection in the clutter and noise background.
