Segmentation of Natural Images with K-Means and Hierarchical Algorithm based on Mixture of Pearson Distributions

In this paper, an attempt has been made to analyze the performance of the image segmented algorithms with the addition of the Pearsonian Type III mixture model. By using the Type III Pearsonian system of distributions the image segmentation process was carried out in the current article which is a novel technique. With the help of K-component combination of Pearsonian Type III distribution, it is considered that the whole input images are characterized. The performance parameters PRI (Probabilistic Rand Index), GCE (Global Consistency Error) and VOI (Volume of Interest) for the currently considered model are estimated with the help of EM (Expectation Maximization) algorithm. For analyzing the proposed model’s performance, four random images are selected as input for the current model from Berkeley image database. The performance metric parameters PRI, GCE and VOI values given the results as the currently proposed method is providing more précise results for the input images where the regions of the input images selected are with tiles having long upper model and the left skewed images. By the help of image quality measures, the proposed method is performing well for the purpose of retrieving the images with respect to the picture segmenting process which is based on GMM (Gaussian Mixture Model). The current model performance was compared with the other existing models like the k-means hierarchical clustering model and the 3-paprameter regression models

Segmentation of Natural Images with K-Means and Hierarchical Algorithm based on Mixture of Pearson Distributions

707-715In this paper, an attempt has been made to analyze the performance of the image segmented algorithms with the addition of the Pearsonian Type III mixture model. By using the Type III Pearsonian system of distributions the image segmentation process was carried out in the current article which is a novel technique. With the help of K-component combination of Pearsonian Type III distribution, it is considered that the whole input images are characterized. The performance parameters PRI (Probabilistic Rand Index), GCE (Global Consistency Error) and VOI (Volume of Interest) for the currently considered model are estimated with the help of EM (Expectation Maximization) algorithm. For analyzing the proposed model’s performance, four random images are selected as input for the current model from Berkeley image database. The performance metric parameters PRI, GCE and VOI values given the results as the currently proposed method is providing more précise results for the input images where the regions of the input images selected are with tiles having long upper model and the left skewed images. By the help of image quality measures, the proposed method is performing well for the purpose of retrieving the images with respect to the picture segmenting process which is based on GMM (Gaussian Mixture Model). The current model performance was compared with the other existing models like the k-means hierarchical clustering model and the 3-paprameter regression models

Globular Cluster UVIT legacy Survey (GlobUleS) III. Omega Centauri in Far-Ultraviolet

We present the first comprehensive study of the most massive globular cluster Omega Centauri in the far-ultraviolet (FUV) extending from the center to ~ 28% of the tidal radius using the Ultraviolet Imaging Telescope aboard AstroSat. A comparison of the FUV-optical color-magnitude diagrams with available canonical models reveals that the horizontal branch (HB) stars bluer than the knee (hHBs) and the white dwarfs (WDs) are fainter in the FUV by ~ 0.5 mag than model predictions. They are also fainter than their counterparts in M13, another massive cluster. We simulated HB with at least five subpopulations, including three He-rich populations with a substantial He enrichment of Y up to 0.43 dex, to reproduce the observed FUV distribution. We find the He-rich younger subpopulations to be radially more segregated than the He-normal older ones, suggesting an in-situ enrichment from older generations. The Omega Cen hHBs span the same effective temperature range as their M13 counterparts, but some have smaller radii and lower luminosities. This may suggest that a fraction of Omega Cen hHBs are less massive than those of M13, similar to the result derived from earlier spectroscopic studies of outer extreme HB stars. The WDs in Omega Cen and M13 have similar luminosity-radius-effective temperature parameters, and 0.44 - 0.46 M$_\odot$ He-core WD model tracks evolving from progenitors with Y = 0.4 dex are found to fit the majority of these. This study provides constraints on the formation models of Omega Cen based on the estimated range in age, [Fe/H] and Y (in particular), for the HB stars.Comment: Accepted for publication in ApJL; 13 pages, 5 figures, 1 tabl

Hybrid CNN-SVM Classifier Approaches to Process Semi-Structured Data in Sugarcane Yield Forecasting Production

Information communication technology (ICT) breakthroughs have boosted global social and economic progress. Most rural Indians rely on agriculture for income. The growing population requires modern agricultural practices. ICT is crucial for educating farmers on how to be environmentally friendly. It helps them create more food by solving a variety of challenges. India’s sugarcane crop is popular and lucrative. Long-term crops that require water do not need specific soil. They need water; the ground should always have adequate water due to the link between cane growth and evaporation. This research focuses on forecasting soil moisture and classifying sugarcane output; sugarcane has so many applications that it must be categorized. This research examines these claims: The first phase model predicts soil moisture using two-level ensemble classifiers. Secondly, to boost performance, the proposed ensemble model integrates the Gaussian probabilistic method (GPM), the convolutional neural network (CNN), and support vector machines (SVM). The suggested approach aims to correctly anticipate future soil moisture measurements affecting crop growth and cultivation. The proposed model is 89.53% more accurate than conventional neural network classifiers. The recommended models’ outcomes will assist farmers and agricultural authorities in boosting production