This paper presents a set of texture features which is based on morphological residues of opening and closing by reconstruction. In texture classification, this set of features is proven much more robust to noise than...
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This paper presents a set of texture features which is based on morphological residues of opening and closing by reconstruction. In texture classification, this set of features is proven much more robust to noise than the feature set derived from traditional morphological residues. An optimization algorithm is established to search for the optimum feature subset. The robustness to noise of our feature set is investigated in detail qualitatively and quantitatively. In various noise circumstances as well as in image deformation, it is found that this feature set bears quite high texture classification accuracy compared to other texture classification methods. (C) 1997 pattern Recognition Society.
A new method is presented for the texture analysis and segmentation of seismic images. The texture of a seismic image is described in terms either of seismic horizons' features (e.g. length, reflection strength, g...
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A new method is presented for the texture analysis and segmentation of seismic images. The texture of a seismic image is described in terms either of seismic horizons' features (e.g. length, reflection strength, geometrical appearance), or in terms of Hilbert transform features (magnitude, phase, instantaneous frequency) or in terms of features related to the generalized runs. Seismic image segmentation rules are derived from examples by using minimum entropy rule learning techniques. Two new methods are presented for using geometric proximity to reference points in region growing. The first one is based on Voronoi tessellation and mathematical morphology. The second one is based on the so-called "radiation model" for region growing and image segmentation.
Super-resolution problem is posed as an inverse deconvolution problem. Fast non-iterative super-resolution algorithm based on this approach is suggested. Different super-resolution problem statements for the cases of ...
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Graph-based methods have been widely used by the document imageanalysis and recognition community, as the different objects and the content in document images is best represented by this powerful structural represent...
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Graph-based methods have been widely used by the document imageanalysis and recognition community, as the different objects and the content in document images is best represented by this powerful structural representation. Designing of novel computation tools for processing these graph-based structural representations has always remained a hot topic of research. Recently, Graph Neural Network (GNN) have been used for solving different problems in the domain of document imageanalysis and recognition. In this article we take forward the state of the art by presenting a new approach to gather the symbolic and numeric information from the nodes and edges of a graph. We use this information to learn a Graph Neural Network (GNN). The experimentation on the recognition of handwritten letters and graphical symbols shows that the proposed approach is an interesting contribution to the growing set of GNN-based methods for document imageanalysis and recognition.
A new mathematical method is presented for processing and analyzing microscopic images of the epithelium posterius (endothelium) in the human eye cornea. As initial data, we use images of endothelial cells that are ob...
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We present an algorithm, called marching cores, that generates cores of 3D medical images and also generalizes to finding implicitly defined manifolds of codimension greater than one. As we march along the core, we us...
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ISBN:
(纸本)0818673672
We present an algorithm, called marching cores, that generates cores of 3D medical images and also generalizes to finding implicitly defined manifolds of codimension greater than one. As we march along the core, we use medialness kernels to generate new medialness values and then find ridges in the extended medial space using the geometric definition of height ridges and mathematical models of manifold intersections. Results from both a test image and a CT image illustrate the algorithm.
We present an up-to-date survey on the topic of adaptive mathematical morphology. A broad review of research performed within the field is provided, as well as an in-depth summary of the theoretical advances within th...
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We present an up-to-date survey on the topic of adaptive mathematical morphology. A broad review of research performed within the field is provided, as well as an in-depth summary of the theoretical advances within the field. Adaptivity can come in many different ways, based on different attributes, measures, and parameters. Similarities and differences between a few selected methods for adaptive structuring elements are considered, providing perspective on the consequences of different types of adaptivity. We also provide a brief analysis of perspectives and trends within the field, discussing possible directions for future studies. (C) 2014 Elsevier B.V. All rights reserved.
We introduce a class of mathematical algorithms with the aim of establishing a framework of finding a group average and extracting prominent features in a group of landmark represented shapes or image templates. A gro...
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We introduce a class of mathematical algorithms with the aim of establishing a framework of finding a group average and extracting prominent features in a group of landmark represented shapes or image templates. A group average is an estimator that is said to best represent the common features of the group being studied. The proposed algorithms, as a tool of feature extraction, extract information about momentum at each landmark through the process of template matching. Once the convergence criterion is satisfied numerically, the algorithms produce a group average and a local coordinate system for each member of the observing group, in terms of the residual momentum. We present several examples to illustrate the use of the proposed algorithms for finding a group average. Using the metrics computed between the group average and each member of the group, we successfully run a cluster analysis for datasets that contain a heavy percentage of outliers. Finally, we apply the collected residual momenta computed in the proposed algorithms in some statistical methods to demonstrate a potential application of the algorithms for detecting structure abnormality. (C) 2018 Elsevier Ltd. All rights reserved.
Pitting corrosion can lead to critical failures of infrastructure elements. Therefore, accurate detection of corroded areas is crucial during the phase of structural health monitoring. This study aims at developing a ...
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Pitting corrosion can lead to critical failures of infrastructure elements. Therefore, accurate detection of corroded areas is crucial during the phase of structural health monitoring. This study aims at developing a computer vision and data-driven method for automatic detection of pitting corrosion. The proposed method is an integration of the history-based adaptive differential evolution with linear population size reduction (LSHADE), image processing techniques, and the support vector machine (SVM). The implementation of the LSHADE metaheuristic in this research is multifold. This optimization algorithm is employed in the task of multilevel image thresholding to extract regions of interest from the metal surface. image texture analysismethods of statistical measurements of color channels, gray-level co-occurrence matrix, and local binary pattern are used to compute numerical features subsequently employed by the SVM-based pattern recognition phase. In addition, the LSHADE metaheuristic is also used to optimize the hyperparameters of the machine-learning approach. Experimental results supported by statistical test points out that the newly developed approach can attain a good predictive result with classification accurate rate = 91.80%, precision = 0.91, recall = 0.94, negative predictive value = 0.93, and F1 score = 0.92. Thus, the newly developed method can be a promising tool to be used in a periodic structural health survey.
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