Soham Sarkar

Multilevel Image Thresholding Based on 2D Histogram and Maximum Tsallis Entropy— A Differential Evolution Approach

Multilevel thresholding amounts to segmenting a gray-level image into several distinct regions. This paper presents a 2D histogram based multilevel thresholding approach to improve the separation between objects. Recent studies indicate that the results obtained with 2D histogram oriented approaches are superior to those obtained with 1D histogram based techniques in the context of bi-level thresholding. Here, a method to incorporate 2D histogram related information for generalized multilevel thresholding is proposed using the maximum Tsallis entropy. Differential evolution (DE), a simple yet efficient evolutionary algorithm of current interest, is employed to improve the computational efficiency of the proposed method. The performance of DE is investigated extensively through comparison with other well-known nature inspired global optimization techniques such as genetic algorithm, particle swarm optimization, artificial bee colony, and simulated annealing. In addition, the outcome of the proposed method is evaluated using a well known benchmark-the Berkley segmentation data set (BSDS300) with 300 distinct images.

A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution

Abstract We propose a novel multi-level thresholding method for unsupervised separation between objects and background from a natural color image using the concept of the minimum cross entropy (MCE). MCE based thresholding techniques are widely popular for segmenting grayscale images. Color image segmentation is still a challenging field as it involves 3-D histogram unlike the 1-D histogram of grayscale images. Effectiveness of entropy based multi-level thresholding for color image is yet to be explored and this paper presents a humble contribution in this context. We have used differential evolution (DE), a simple yet efficient evolutionary algorithm of current interest, to improve the computation time and robustness of the proposed algorithm. The performance of DE is also investigated extensively through comparison with other well-known nature inspired global optimization techniques like genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The proposed method is evaluated by comparing it with seven other prominent algorithms both qualitatively and quantitatively using a well known benchmark suite – the Barkley Segmentation Dataset (BSDS300) with 300 distinct images. Such comparison reflects the efficiency of our algorithm

Hyper-spectral image segmentation using Rényi entropy based multi-level thresholding aided with differential evolution

Unsupervised classification of land cover study of hyper-spectral satellite images.A multi-level Renyi entropy based image thresholding scheme is presented.Multi-level thresholding is formulated as optimization problem and solved with DE.Composite kernel based classification approach using Support Vector Machine (SVM).Very competitive performance on popular hyper-spectral imagery like ROSIS and AVRIS. This article presents a novel approach for unsupervised classification of land cover study of hyper-spectral satellite images to improve separation between objects and background by using multi-level thresholding based on the maximum Renyi entropy (MRE). Multi-level thresholding, which partitions a gray-level image into several distinct homogeneous regions, is a widely popular tool for segmentation. However, utility of multi-level thresholding is yet to be investigated in challenging applications like hyper-spectral image analysis. Differential Evolution (DE), a simple yet efficient evolutionary algorithm of current interest, is employed to improve the computation time and robustness of the proposed algorithm. The performance of DE is also investigated extensively through comparison with other well-known nature inspired global optimization techniques. In addition, the outcomes of the MRE-based thresholding are employed to train a Support Vector Machine (SVM) classifier via the composite kernel approach to improve the classification accuracy. The final outcomes are tested on popular hyper-spectral imagery like ROSIS and AVRIS sensors. The effectiveness of the proposed algorithm is evaluated through qualitative and quantitative comparison with other state-of-the-art global optimizers.

A Fuzzy Entropy Based Multi-Level Image Thresholding Using Differential Evolution

This paper presents a multi-level image thresholding approach based on fuzzy partition of the image histogram and entropy theory. Here a fuzzy entropy based approach is adopted in context to the multi-level image segmentation scenario. This entropy measure is then optimized to obtain the thresholds of the image. In order to solve the optimization problem, a meta-heuristic, Differential Evolution (DE) is used, which leads to a faster and accurate convergence towards the optima. The performance of DE is also measured with respect to some popular global optimization techniques like Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs).The outcomes are compared with Shannon entropy, both visually and statistically in order to establish the perceptible difference in image.

Multilevel image thresholding based on tsallis entropy and differential evolution

Image segmentation is known as one of the most critical task in image processing and pattern recognition in contemporary time, for this purpose Multi Level Thresholding based approach has been an acclaimed way out. Endeavor of this paper is to focus on obtaining the optimal threshold points by using Tsallis Entropy. In this paper, we have incorporated a Differential Evolution (DE) based technique to acquire optimal threshold values. Furthermore, results are compared with two state-of-art algorithms- a. Particle Swarm Optimization (PSO), and b. Genetic Algorithm (GA). Several image quality assessment indices are applied for the performance analysis of the outcome derived by applying the proposed algorithm.

A Differential Evolutionary Multilevel Segmentation of Near Infra-Red Images Using Renyi’s Entropy

In recent years remote sensing image processing has got some intense attention of the researchers for its utility in land cover study, natural calamity detection, object tracking etc. In case of remote sensing image processing, the primal objective is to sub divide the image into more than one segment. In doing so, Multi-level thresholding based image segmentation techniques play an useful role in accomplishing this critical task. Endeavor of this paper is to focus on obtaining the optimal multiple threshold points from a LISS III Near Infra-Red (NIR) band by employing Renyi’s Entropy. Moreover, a state-of-art meta-heuristics like Differential Evolution (DE) is incorporated to acquire optimal threshold values in reduced computational time with precision.

A hybrid ARIMA-DENFIS method for wind speed forecasting

This paper proposes a hybrid autoregressive integrated moving average - dynamic evolving neural-fuzzy inference system (ARIMA-DENFIS) model for wind speed forecasting. The theory of ARIMA, DENFIS and the hybrid of the two are discussed. The proposed model is evaluated with NDBC wind speed data and the results show that the proposed hybrid ARIMA-DENFIS model outperforms DENFIS model in most of the cases. It has comparable or better error measures than ARIMA model. In addition, when the forecasting horizon increases, the advantage of the proposed ARIMA-DENFIS model becomes more significant.

Multi-level image segmentation based on fuzzy - Tsallis entropy and differential evolution

This paper presents a fuzzy partition and Tsallis entropy based thresholding approach for multi-level image segmentation. Image segmentation is considered as one of the most critical tasks in image processing and pattern recognition area. However, discriminating many objects present in an image automatically is the most challenging one. As a result, multilevel thresholding based methods gain importance in recent times, because of its ability to split the image into more than one segments. Efficiency of these algorithms still remains a matter of concern. Over the years, fuzzy partition of 1-D histogram has been employed successfully in bi-level image segmentation to improve the separation between object and the background. Here a fuzzy based technique is adopted in multi-level image segmentation scenario using Tsallis entropy based thresholding. Differential Evolution, a widely used meta-heuristic in recent times, is used for lesser computation time of the proposed algorithm. Both visual and statistical comparison of outcomes between Tsallis and Fuzzy - Tsallis entropy based methods are given in this paper to establish the superiority of the technique.

Multi-level thresholding with a decomposition-based multi-objective evolutionary algorithm for segmenting natural and medical images

Display Omitted A novel bi-objective formulation of multi-level thresholding entropy functions.Application of evolutionary multi-objective optimization for threshoding.Extensive comparative study among MOEAs on the segmentation problem.Well known dataset and performance evolutions are used for performance assessments. The objective of multilevel thresholding is to segmenting a gray-level image into several distinct homogeneous regions. This paper presents an alternative approach for unsupervised segmentation of natural and medical images to improve the separation between objects in the framework of multi-objective optimization. In contrast to the existing single-objective optimization and entropy-based methods, a multi-objective framework is adopted by combining two objectives based on the Minimum Cross Entropy (MCE) and Renyi Entropy (RE). One of the most competitive Multi-Objective Evolutionary Algorithms (MOEAs) of current interest, called MOEA/D-DE (Decomposition based MOEA with Differential Evolution) is then applied to determine the set of Pareto optimal solutions for these two objectives. The threshold values for multi-level segmentations are obtained from the approximated Pareto Fronts (PFs) generated by MOEA/D-DE. The performance of MOEA/D-DE is also investigated extensively through comparison with other popular nature-inspired single-objective and multi-objective optimizers. Moreover, outcome of the proposed method is evaluated by comparing against the results of other well cited algorithms both qualitatively and quantitatively on test-suites comprising well-known natural and medical test images in order to showcase the efficiency of the proposed algorithm.

A Hybrid Particle Swarm with Differential Evolution Operator Approach (DEPSO) for Linear Array Synthesis

In recent years particle swarm optimization emerges as one of the most efficient global optimization tools. In this paper, a hybrid particle swarm with differential evolution operator, termed DEPSO, is applied for the synthesis of linear array geometry. Here, the minimum side lobe level and null control, both are obtained by optimizing the spacing between the array elements by this technique. Moreover, a statistical comparison is also provided to establish its performance against the results obtained by Genetic Algorithm (GA), classical Particle Swarm Optimization (PSO), Tabu Search Algorithm (TSA), Differential Evolution (DE) and Memetic Algorithm (MA).