Current Issue : July - September Volume : 2015 Issue Number : 3 Articles : 5 Articles
An image analysis procedure based on a two dimensional Gaussian fitting is\npresented and applied to satellite maps describing the surface urban heat island (SUHI).\nThe application of this fitting technique allows us to parameterize the SUHI pattern in\norder to better understand its intensity trend and also to perform quantitative comparisons\namong different images in time and space. The proposed procedure is computationally\nrapid and stable, executing an initial guess parameter estimation by a multiple regression\nbefore the iterative nonlinear fitting. The Gaussian fit was applied to both low and high\nresolution images (1 km and 30 m pixel size) and the results of the SUHI parameterization\nshown. As expected, a reduction of the correlation coefficient between the map values and\nthe Gaussian surface was observed for the image with the higher spatial resolution due to\nthe greater variability of the SUHI values. Since the fitting procedure provides a smoothed\nGaussian surface, it has better performance when applied to low resolution images, even if\nthe reliability of the SUHI pattern representation can be preserved also for high resolution\nimages....
Synthetic aperture radar (SAR) image segmentation usually involves two\ncrucial issues: suitable speckle noise removing technique and effective image segmentation\nmethodology. Here, an efficient SAR image segmentation method considering both of the\ntwo aspects is presented. As for the first issue, the famous nonlocal mean (NLM) filter\nis introduced in this study to suppress the multiplicative speckle noise in SAR image.\nFurthermore, to achieve a higher denoising accuracy, the local neighboring pixels in the\nsearching window are projected into a lower dimensional subspace by principal component\nanalysis (PCA). Thus, the nonlocal mean filter is implemented in the subspace. Afterwards,\na multi-objective clustering algorithm is proposed using the principals of artificial immune\nsystem (AIS) and kernel-induced distance measures. The multi-objective clustering has\nbeen shown to discover the data distribution with different characteristics and the kernel\nmethods can improve its robustness to noise and outliers. Experiments demonstrate that the\nproposed method is able to partition the SAR image robustly and accurately than the\nconventional approaches....
A graph is unipolar if it can be partitioned into a clique and a disjoint union of\ncliques, and a graph is a generalised split graph if it or its complement is unipolar. A unipolar\npartition of a graph can be used to find efficiently the clique number, the stability number, the\nchromatic number, and to solve other problems that are hard for general graphs. We present\nan O(n2)-time algorithm for recognition of n-vertex generalised split graphs, improving on\nprevious O(n3)-time algorithms....
The properties of 1172 protein complexes (downloaded from the Protein Data\nBank (PDB)) have been studied based on the concept of circular variance as a buriedness\nindicator and the concept of mutual proximity as a parameter-free definition of contact.\nThe propensities of residues to be in the protein, on the surface or form contact, as well as\nresidue pairs to form contact were calculated. In addition, the concept of circular variance\nhas been used to compare the ruggedness and shape of the contact surface with the overall\nsurface....
The NP-hard RAINBOW SUBGRAPH problem, motivated from bioinformatics, is\nto find in an edge-colored graph a subgraph that contains each edge color exactly once and\nhas at most k vertices. We examine the parameterized complexity of RAINBOW SUBGRAPH\nfor paths, trees, and general graphs. We show that RAINBOW SUBGRAPH is W[1]-hard with\nrespect to the parameter k and also with respect to the dual parameter := n ? k where n is\nthe number of vertices. Hence, we examine parameter combinations and show, for example,\na polynomial-size problem kernel for the combined parameter and ââ?¬Å?maximum number of\ncolors incident with any vertexââ?¬Â. Additionally, we show APX-hardness even if the input\ngraph is a properly edge-colored path in which every color occurs at most twice....
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