Self-constrained inversion of potential fields through a 3D depth weighting

A new method for inversion of potential fields is developed using a depth-weighting function specifically designed for fields related to complex source distributions. Such a weighting function is determined from an analysis of the field that precedes the inversion itself. The algorithm is self-consistent, meaning that the weighting used in the inversion is directly deduced from the scaling properties of the field. Hence, the algorithm is based on two steps: (1) estimation of the locally homogeneous degree of the field in a 3D domain of the harmonic region and (2) inversion of the data using a specific weighting function with a 3D variable exponent. A multiscale data set is first formed by upward continuation of the original data. Local homogeneity and a multihomogeneous model are then assumed, and a system built on the scaling function is solved at each point of the multiscale data set, yielding a multiscale set of local-homogeneity degrees of the field. Then, the estimated homogeneity degree is associated to the model weighting function in the source volume. Tests on synthetic data show that the generalization of the depth weighting to a 3D function and the proposed two-step algorithm has great potential to improve the quality of the solution. The gravity field of a polyhedron is inverted yielding a realistic reconstruction of the whole body, including the bottom surface. The inversion of the aeromagnetic real data set, from the Mt. Vulture area, also yields a good and geologically consistent reconstruction of the complex source distribution.

Local Homogeneity Based Morphological Operators for Segmentation of Fabric Defect via LSE

At the cloth manufacturing industry, texture imperfection discovery turns into a vital and basic advance in good quality control. In this field, the interest is higher than conservative while a decrease in labor cost and related advantages are taken into consideration. In addition, the advancement of an entirely mechanized examination framework requires effective and strong calculations. To conquer this issue, in this paper, we present another texture imperfection location conspire which utilizes the neighborhood homogeneity. Its initial step comprises in processing another homogeneity picture designated as a H-image. Later the next step is the utilization of Morphological shutting to the H-image is ended by utilizing the Level Sets. Reproductions on various texture pictures and distinctive imperfection angles demonstrate that the proposed technique accomplishes a normal exactness of 99.35%.

Clustering knowledge and dispersing abilities enhances collective problem solving in a network

Diversity tends to generate more and better ideas in social settings, ranging in scale from small-deliberative groups to tech-clusters and cities. Implicit in this research is that there are knowledge-generating benefits from diversity that comes from mixing different individuals, ideas, and perspectives. Here, we utilize agent-based modeling to examine the emergent outcomes resulting from the manipulation of how diversity is distributed and how knowledge is generated within communicative social structures. In the context of problem solving, we focus on cognitive diversity and its two forms: ability and knowledge. For diversity of ability, we find that local diversity (intermixing of different agents) performs best at all time scales. However, for diversity of knowledge, we find that local homogeneity performs best in the long-run, because it maintains global diversity, and thus the knowledge-generating ability of the group, for a longer period.

Spatial Regularization and Adaptive Distance Metric Methods through DST for Tumor Segmentation

The process of segmentation in MRI pictures is turning into a significant assignment to be considered in clinical oncology applications, in view of the noise and blur that is available normally in MRI images. To minimize this natural disadvantages in the MRI images an imaging tool called belief theory is taken as the base alongside the proposed evidential clustering algorithm (ECM-MS) .This proposed technique joins the adaptive distance metric in so as to limit the clustering distortions and the comparability that happen between the voxels. The local homogeneity is measured by the spatial regularization dependent on the belief theory called Dempster Shafer Theory (DST). To get definite division the surface highlights are extricated from the data picture and is incorporated with the force of the voxels in the proposed strategy, thusly giving a decent presentation contrasted with different strategies.

Topology: A Theory of a Pseudometric-Based Clustering Model and Its Application in Content-Based Image Retrieval

The clustering problem has been extensively studied over the last 50 years; however, it still has the attention of researchers. This paper presents a topological basis of a pseudometric-based clustering model which takes into account the local and global topological properties of the data to be clustered, as per the definition of homogeneity measurement. The proposed approach takes into account the homogeneity effect produced when a new particle is added to a group. The additional element can be accumulated in the group if its local homogeneity is not altered and, therefore, it is not necessary to carry out tests in another group. A new group needs to be generated if the threshold of the local homogeneity of the group exceeds. Theoretical results, their implementation, and their application to the problem of Content Based Image Retrieval (CBIR) are presented. The tests were performed using three image databases widely used in the literature, which are “Vogel and Shiele,” “Oliva and Torralba,” and “L. Fei- Fei, R. Fergus and P. Perona.” The results are presented and compared with the most competitive methods available in the literature.