A Study on RGB Image Multi-Thresholding using Kapur/Tsallis Entropy and Moth-Flame Algorithm

In the literature, a considerable number of image processing and evaluation procedures are proposed and implemented in various domains due to their practical importance. Thresholding is one of the pre-processing techniques, widely implemented to enhance the information in a class of gray/RGB class pictures. The thresholding helps to enhance the image by grouping the similar pixels based on the chosen thresholds. In this research, an entropy assisted threshold is implemented for the benchmark RGB images. The aim of this work is to examine the thresholding performance of well-known entropy functions, such as Kapur’s and Tsallis for a chosen image threshold. This work employs a Moth-Flame-Optimization (MFO) algorithm to support the automatic identification of the finest threshold (Th) on the benchmark RGB image for a chosen threshold value (Th=2,3,4,5). After getting the threshold image, a comparison is performed against its original picture and the necessary Picture-Quality-Values (PQV) is computed to confirm the merit of the proposed work. The experimental investigation is demonstrated using benchmark images with various dimensions and the outcome of this study confirms that the MFO helps to get a satisfactory result compared to the other heuristic algorithms considered in this study

A Multilevel Image Thresholding Based on Hybrid Jaya Algorithm and Simulated Annealing

Thresholding is a method for region-based image segmentation, which is important in image processing applications such as object recognition Multilevel. Thresholding is used to find multiple threshold values. Image segmentation plays a significant role in image analysis and pattern recognition. While threshold techniques traditionally are quite well for bi-level thresholding algorithms, multilevel thresholding for color images may have too much processing complexity. Swarm intelligence methods are frequently employed to minimize the complexity of constrained optimization problems applicable to multilevel thresholding and segmentation of color (RGB) images; In this paper, the hybrid Jaya algorithm with the SA algorithm was proposed to solve the problem of computational complexity in multilevel thresholding. This work uses Otsu method, Kapur entropy and Tsallis method as techniques to find optimal values of thresholds at different levels of color images as the target Tasks Experiments were performed on 5 standardized color images and 3 grayscale images as far as optimal threshold values are concerned, Statistical methods were used to measure the performance of the threshold methods and to select the better threshold, namely, PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), SSIM (Structural Similarity Index), FSIM (Feature Similarity Index) and values of objective at many levels. The experimental results indicate that the presenter's Jaya and Simulated Annealing (JSA) method is better than other methods for segmenting color (RGB) images with multiple threshold levels. On the other hand, the Tsallis entropy of the cascade was found to be more robust and accurate in segmenting color images at multiple levels

Information theoretic thresholding techniques based on particle swarm optimization.

In this dissertation, we discuss multi-level image thresholding techniques based on information theoretic entropies. In order to apply the correlation information of neighboring pixels of an image to obtain better segmentation results, we propose several multi-level thresholding models by using Gray-Level & Local-Average histogram (GLLA) and Gray-Level & Local-Variance histogram (GLLV). Firstly, a RGB color image thresholding model based on GLLA histogram and Tsallis-Havrda-Charv\u27at entropy is discussed. We validate the multi-level thresholding criterion function by using mathematical induction. For each component image, we assign the mean value from each thresholded class to obtain three segmented component images independently. Then we obtain the segmented color image by combining the three segmented component images. Secondly, we use the GLLV histogram to propose three novel entropic multi-level thresholding models based on Shannon entropy, R\u27enyi entropy and Tsallis-Havrda-Charv\u27at entropy respectively. Then we apply these models on the three components of a RGB color image to complete the RGB color image segmentation. An entropic thresholding model is mostly about searching for the optimal threshold values by maximizing or minimizing a criterion function. We apply particle swarm optimization (PSO) algorithm to search the optimal threshold values for all the models. We conduct the experiments extensively on The Berkeley Segmentation Dataset and Benchmark (BSDS300) and calculate the average four performance indices (Probability Rand Index, PRI, Global Consistency Error, GCE, Variation of Information, VOI and Boundary Displacement Error, BDE) to show the effectiveness and reasonability of the proposed models

Mathematical Methods Applied to Digital Image Processing

Introduction: Digital image processing (DIP) is an important research area since it spans a variety of applications. Although over the past few decades there has been a rapid rise in this field, there still remain issues to address. Examples include image coding, image restoration, 3D image processing, feature extraction and analysis, moving object detection, and face recognition. To deal with these issues, the use of sophisticated and robust mathematical algorithms plays a crucial role. The aim of this special issue is to provide an opportunity for researchers to publish their latest theoretical and technological achievements in mathematical methods and their various applications related to DIP. This special issue covers topics related to the development of mathematical methods and their applications. It has a total of twenty-four high-quality papers covering various important topics in DIP, including image preprocessing, image encoding/decoding, stereo image reconstruction, dimensionality and data size reduction, and applications

Renyi’s entropy based multilevel thresholding using a novel meta-heuristics algorithm

Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively

Optimization Methods for Image Thresholding: A review

Setting a border with the proper gray level in processing images to separate objects from their backgrounds is crucial. One of the simplest and most popular methods of segmenting pictures is histogram-based thresholding. Thresholding is a common technique for image segmentation because of its simplicity. Thresholding is used to separate the Background of the image from the Foreground. There are many methods of thresholding. This paper aims to review many previous studies and mention the types of thresholding. It includes two types: the global and local thresholding methods and each type include a group of methods. The global thresholding method includes (the Otsu method, Kapur's entropy method, Tsallis entropy method, Hysteresis method, and Fuzzy entropy method), and the local thresholding method includes ( Ni-Black method and Bernsen method). The optimization algorithms(Genetic Algorithm, Particle Swarm Optimization, Bat Algorithm, Modified Grasshopper Optimization, Firefly Algorithm, Cuckoo Search, Tabu Search Algorithm, Simulated Annealing, and Jaya Algorithm) used along with thresholding methods are also illustrated