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Anil Kumar

Bio: Anil Kumar is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Filter bank & Filter design. The author has an hindex of 44, co-authored 1411 publications receiving 11378 citations. Previous affiliations of Anil Kumar include Gwangju Institute of Science and Technology & Malaviya National Institute of Technology, Jaipur.


Papers
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Journal ArticleDOI
TL;DR: The results based on Kapur's entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem.
Abstract: The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur's entropy has been employed. For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur's entropy for optimal multilevel thresholding. A new approach of CS and WDO algorithm is used for selection of optimal threshold value. This algorithm is used to obtain the best solution or best fitness value from the initial random threshold values, and to evaluate the quality of a solution, correlation function is used. Experimental results have been examined on standard set of satellite images using various numbers of thresholds. The results based on Kapur's entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem.

392 citations

Journal ArticleDOI
TL;DR: Compared to other thresholding methods, segmentation results of the proposed MABC algorithm is most promising, and the computational time is also minimized.
Abstract: A modified ABC algorithm based fast satellite image segmentation has been presented.ABC, PSO and GA methods are compared with this proposed method.The experimental results demonstrate better performance of MABC based technique.The proposed MABC based approach is much faster (CPU time is less).The validity of the proposed technique is reported both qualitatively and quantitatively. In this paper, a modified artificial bee colony (MABC) algorithm based satellite image segmentation using different objective function has been presented to find the optimal multilevel thresholds. Three different methods are compared with this proposed method such as ABC, particle swarm optimization (PSO) and genetic algorithm (GA) using Kapur's, Otsu and Tsallis objective function for optimal multilevel thresholding. The experimental results demonstrate that the proposed MABC algorithm based segmentation can efficiently and accurately search multilevel thresholds, which are very close to optimal ones examined by the exhaustive search method. In MABC algorithm, an improved solution search equation is used which is based on the bee's search only around the best solution of previous iteration to improve exploitation. In addition, to improve global convergence when generating initial population, both chaotic system and opposition-based learning method are employed. Compared to other thresholding methods, segmentation results of the proposed MABC algorithm is most promising, and the computational time is also minimized.

289 citations

Journal ArticleDOI
TL;DR: In this article, an improved method for the analysis of satellite image based on Normalized Difference Vegetation Index (NDVI) is presented. And the simulation results show that the NDVI is highly useful in detecting the surface features of the visible area which are extremely beneficial for municipal planning and management.

207 citations

Journal ArticleDOI
TL;DR: In this article, the effect of different integrated nutrient management practices on soil organic carbon (SOC) stocks and its fractions, SOC sequestration potential as well as the sustainability of the rice-wheat system were evaluated in long term experiments at different agro-climatic zones of IGP.

167 citations

Journal ArticleDOI
TL;DR: A new technique for color image segmentation using CS algorithm supported by Tsallis entropy for multilevel thresholding has been proposed toward the effective colored segmentation of satellite images and qualitative and quantitative results demonstrate that the proposed method selects the threshold values effectively and properly.
Abstract: Cuckoo search based multi-level thresholding is presented by maximizing the Tsallis entropy.Different optimization algorithms are exploited with Tsallis entropy method.Cuckoo based Tsallis entropy was found to be more accurate for colored satellite image segmentation.The feasibility of the proposed approach has been tested on 10 different colored satellite images. In this paper, a new technique for color image segmentation using CS algorithm supported by Tsallis entropy for multilevel thresholding has been proposed toward the effective colored segmentation of satellite images. The nonextensive entropy is a new expansion in statistical mechanics, and it is a recent formalism in which a real quantity q was introduced as parameter for physical systems that presents the long range interactions, long time memories and fractal-type structures. The feasibility of the proposed cuckoo search and Tsallis entropy based approach was tested on 10 different satellite images and benchmarked with differential evolution, wind driven optimization, particle swarm optimization and artificial bee colony algorithm for solving the multilevel colored image thresholding problems. Experiments have been conducted on a variety of satellite images. Several measurements are used to evaluate the performance of proposed method which clearly illustrates the effectiveness and robustness of the proposed algorithm. The experimental results qualitative and quantitative both demonstrate that the proposed method selects the threshold values effectively and properly.

164 citations


Cited by
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Journal ArticleDOI
TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

14,171 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal Article
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

9,847 citations

01 Jan 2002

9,314 citations