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Structure graphs are often used in image structural representation by organizing the units of image (such as feature points). However, due to "noise" or non-rigid deformations, the graphs generated from images are usually not stab...
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Structure graphs are often used in image structural representation by organizing the units of image (such as feature points). However, due to "noise" or non-rigid deformations, the graphs generated from images are usually not stable. To overcome this problem, image matching and recognition can usually be achieved by inexact graph matching means. There has been recent much work on inexact graph matching, but not much on robust graph modeling itself. In this paper we develop a new robust structure graph model for image representation and matching. We believe that a robust structure graph model should adapt to the noise or perturbation of the image units. Here, we explore random graphs instead of traditional graph models and propose a novel random structure graph, called Geometric-Edge random graphs (G-E graphs), for image representation and matching. The main idea of G-E graphs is that the probabilities of edges between node pairs are explored to indicate the uncertainty or variations of edges in the geometric graph generated under some noise or perturbation of the image units. Promising experimental results on both image matching and pattern space embedding show that the proposed G-E graphs are effective and robust to structural variations and significantly outperform traditional graph models. (C) 2016 Elsevier B.V. All rights reserved.
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For segmentation method to be useful it must be fast, easy to use, and produce high quality segmentations, but few algorithms can offer this in various conditions and applications. In this paper, we propose a context dependent gra...
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For segmentation method to be useful it must be fast, easy to use, and produce high quality segmentations, but few algorithms can offer this in various conditions and applications. In this paper, we propose a context dependent graph-based method for transition region extraction and thresholding. The graphbased approach is introduced into image thresholding, and context dependent graph is constructed from a given image, which can adaptively extract the pixel context and shape information because of the scalable neighborhood. Then an edge weight function is defined as the measure of possible transition pixels, and a robust fully automatic scheme for the optimal threshold is also presented. The proposed approach is validated both quantitatively and qualitatively. Compared with the traditional state-of-art algorithms on synthetic and real images, as well as laser cladding images, the experimental results suggest that the new proposal is efficient and effective.
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? 2024 Elsevier LtdFusion of high spatial resolution multispectral (HR MS) and low spatial resolution hyperspectral (LR HS) images has become a significant way to produce high spatial resolution hyperspectral (HR HS) images. Thoug...
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? 2024 Elsevier LtdFusion of high spatial resolution multispectral (HR MS) and low spatial resolution hyperspectral (LR HS) images has become a significant way to produce high spatial resolution hyperspectral (HR HS) images. Though many methods have exploited the spatial nonlocal similarity (SNS) and spectral band correlation (SBC) in the HR HS image, it is difficult to model the priors explicitly because the HR HS image is unavailable in real scenes. As the low-dimensional degradation versions, HR MS and LR HS images inherit the SNS and SBC in the HR HS image, respectively. But these methods seldom consider the inheritance of SNS and SBC between the two source images and the HR HS image. In this paper, we propose a spatial–spectral dual adaptive graph embedding model (SDAGE) to exploit the SNS and SBC in HR MS and LR HS images for the regularization of their fusion. Specifically, spatial and spectral graphs are constructed adaptively to describe the SNS in the HR MS image and the SBC in the LR HS image. Then, the two graphs are embedded into the features for the reconstruction of the HR HS image. In this way, it is explicit to ensure the consistency of SNS and SBC between the source images and the HR HS image. Experiments on three benchmark datasets demonstrate the effectiveness of our SDAGE method. The code can be downloaded from https://github.com/RSMagneto/SDAGE.
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The infrared image (RI) and visible image (VI) fusion method merges complementary information from the infrared and visible imaging sensors to provide an effective way for understanding the scene. The graph filter bank-based graph...
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The infrared image (RI) and visible image (VI) fusion method merges complementary information from the infrared and visible imaging sensors to provide an effective way for understanding the scene. The graph filter bank-based graph wavelet transform possesses the advantages of the classic wavelet filter bank and graph representation of a signal. Therefore, we propose an RI and VI fusion method based on oversampled graph filter banks. Specifically, we consider the source images as signals on the regular graph and decompose them into the multi-scale representations with M-channel oversampled graph filter banks. Then, the fusion rule for the low-frequency subband is constructed using the modified local coefficient of variation and the bilateral filter. The fusion maps of detail subbands are formed using the standard deviationbased local properties. Finally, the fusion image is obtained by applying the inverse transform on the fusion subband coefficients. The experimental results on benchmark images show the potential of the proposed method in the image fusion applications. (C) 2020 SPIE and IS&T
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Under suitable conditions on the range of the Gauss map of a complete submanifold of Euclidean space with parallel mean curvature, we construct a strongly subharmonic function and derive a-priori estimates for the harmonic Gauss m...
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Under suitable conditions on the range of the Gauss map of a complete submanifold of Euclidean space with parallel mean curvature, we construct a strongly subharmonic function and derive a-priori estimates for the harmonic Gauss map. The required conditions here are more general than in previous work and they therefore enable us to improve substantially previous results for the Lawson-Osseman problem concerning the regularity of minimal submanifolds in higher codimension and to derive Bernstein type results.
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This paper deals with the mathematical properties of watersheds in weighted graphs linked to region merging methods, as used in image analysis. In a graph, a cleft (or a binary watershed) is a set of vertices that cannot be reduce...
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This paper deals with the mathematical properties of watersheds in weighted graphs linked to region merging methods, as used in image analysis. In a graph, a cleft (or a binary watershed) is a set of vertices that cannot be reduced, by point removal, without changing the number of regions (connected components) of its complement. To obtain a watershed adapted to morphological region merging, it has been shown that one has to use the topological thinnings introduced by M. Couprie and G. Bertrand. Unfortunately, topological thinnings do not always produce thin clefts. Therefore, we introduce a new transformation on vertex weighted graphs, called C-watershed, that always produces a cleft. We present the class of perfect fusion graphs, for which any two neighboring regions can be merged, while preserving all other regions, by removing from the cleft the points adjacent to both. An important theorem of this paper states that, on these graphs, the C-watersheds are topological thinnings and the corresponding divides are thin clefts. We propose a linear-time immersion-like algorithm to compute C-watersheds on perfect fusion graphs, whereas, in general, a linear-time topological thinning algorithm does not exist. Furthermore, we prove that this algorithm is monotone in the sense that the vertices are processed in increasing order of weight. Finally, we derive some characterizations of perfect fusion graphs based on the thinness properties of both C-watersheds and topological watersheds. (C) 2008 Elsevier B.V. All rights reserved.
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Many cell-based research studies require the counting of cells in order to understand and validate experiments through statistical analyses. Although progress in imaging technology has enabled the automation of cell counting for m...
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Many cell-based research studies require the counting of cells in order to understand and validate experiments through statistical analyses. Although progress in imaging technology has enabled the automation of cell counting for many different cell types, this process still has to be done manually in the case of images of embryonic stem cells. In this paper, we present a new automatic algorithm to detect and count embryonic stem cells in fluorescence microscopy images that identifies pluripotent stem cells cultured in vitro. Our approach uses luminance information to generate a graph-based image representation. The cell pattern is defined as a subgraph, and a graph-mining process is applied to detect the cells. The method is tolerant to variations in cell size and shape, Moreover, it can easily be parameterized to handle different image groups resulting from distinct differentiation protocols. The paper presents numerical results from tests made on a database with more than two hundred images, including EB cryosection, embryoid body cell migration, murine embryonic stem cell colonies under murine embryonic fibroblast, and neurosphere images. The results from our algorithm were validated by expert biologists, and provide good precision, recall and F-measure. Finally, a comparative study with the widely used watershed algorithm is presented.
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Searching for relevant images given a query term is an important task in nowadays large-scale community databases. The image ranking approach presented in this work represents an image collection as a graph that is built using a m...
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Searching for relevant images given a query term is an important task in nowadays large-scale community databases. The image ranking approach presented in this work represents an image collection as a graph that is built using a multimodal similarity measure based on visual features and user tags. We perform a random walk on this graph to find the most common images. Further we discuss several scalability issues of the proposed approach and show how in this framework queries can be answered fast. Experimental results validate the effectiveness of the presented algorithm.
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Purpose - The purpose of this paper is to present an efficient, interactive foreground/background image segmentation method using mean shift (MS) and graph cuts, in order to improve the segmentation performance with little user in...
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Purpose - The purpose of this paper is to present an efficient, interactive foreground/background image segmentation method using mean shift (MS) and graph cuts, in order to improve the segmentation performance with little user interaction. Design/methodology/approach - By incorporating the advantages of the mean shift method and the graph cut algorithm, the proposed approach ensures the accuracy of segmentation results. First, the user marks certain pixels as foreground or background. Then the graph is constructed and the cost function composed of the boundary properties and the region properties is defined. To obtain the hidden information of user interaction, the foreground and background marks are clustered separately by the mean shift method. The region properties are determined by the minimum distances from the unmarked pixels to the foreground and background clusters. The boundary properties are determined by the relationship between the unmarked pixels and its neighbor pixels. Finally, using the graph cuts method solves the energy minimization problem to get the foreground which is of interest. Findings - The paper presents experimental results and compares the results to other methods. It can be seen from the comparison that this method can obtain a better segmentation performance in many cases. Originality/value - The paper incorporates the advantages of the mean shift method and the graph cut algorithm to obtain better segmentation results, even though the scene is complex.
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Structural representation of drawings and their fragments in the form of loaded graphs with spatial relations as well as an original tool of their analysis are discussed. Several levels of representation of graphic design-engineer...
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Structural representation of drawings and their fragments in the form of loaded graphs with spatial relations as well as an original tool of their analysis are discussed. Several levels of representation of graphic design-engineering data are proposed. Examples of analyzing 3D images of products, which are reconstructed by drawings, are given.
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