A new approach for image segmentation with MATLAB

Image segmentation is the process of subdividing a digital image into its constitute regions such that pixels belong to the same region will be same based on some image property (such as grayscale value, color, texture) and pixels in the different group will be different based on the same image property. Till date, different researchers have taken image segmentation problem from a different point of view and developed several image segmentation algorithms. This paper is going to address an optimization-based approach in color image segmentation where optimized threshold value is chosen by maximizing the Kapur’s Entropy Function.

A knowledge-based approach for Landsat image segmentation is proposed. The image segmentation problem is solved by extracting kernel information from the input image to provide an initial interpretation of the image and by using a knowledge-based hierarchical classifier to discriminate between major land-cover types in the study area. The proposed method is designed in such a way that a Landsat image can be segmented and interpreted without any prior image-dependent information. The general spectral land-cover knowledge is constructed from the training land-cover data, and the road information of an image is obtained through a road-detection program. >

YoTransViT: A transformer and CNN method for predicting and classifying skin diseases using segmentation techniques

In day-to-day life, new technologies are emerging in the field of Image processing, especially in the domain of segmentation. Image segmentation is the most important part in digital image processing. Segmentation is nothing but a portion of any image and object. In image segmentation, the digital image is divided into multiple set of pixels. Image segmentation is generally required to cut out region of interest (ROI) from an image. Currently there are many different algorithms available for image segmentation. This chapter presents a brief outline of some of the most common segmentation techniques (e.g. Segmentation based on thresholding, Model based segmentation, Segmentation based on edge detection, Segmentation based on clustering, etc.,) mentioning its advantages as well as the drawbacks. The Matlab simulated results of different available image segmentation techniques are also given for better understanding of image segmentation. Simply, different image segmentation algorithms with their prospects are reviewed in this chapter to reduce the time of literature survey of the future researchers.

In this paper, an efficient evolutionary image segmentation algorithm (EISA) is proposed. The existing evolutionary approach of image segmentation has the advantages over the other approaches such as continuous contour, non-oversegmentation, and non-thresholds, but suffers from long computation time. EISA uses a K-means algorithm to split an image into many homogeneous regions and then merges the split regions automatically using an evolutionary algorithm. The image segmentation problem is formulated as an optimization problem and the objective function is also given. EISA using a novel chromosome encoding method and a novel intelligent genetic algorithm makes the segmentation results robust and the computation time much shorter than the existing evolutionary image segmentation algorithms. Design and analysis of EISA are also presented. Experimental results of natural images with various degrees of noise demonstrate the effectiveness of EISA.

There is a huge diversity of definitions of visually image segments, ranging from simple uniformly colored segments, textured segments, through symmetric patterns, and up to complex semantically meaningful objects. This diversity has led to a wide range of different approaches for image segmentation. In this paper we present a single unified framework for addressing this problem --- Segmentation by Composition. We define a good image segment as one which can be easily composed using its own pieces, but is difficult to compose using pieces from other parts of the image. This non-parametric approach captures a large diversity of segment types, yet requires no pre-definition or modelling of segment types, nor prior training. Based on this definition, we develop a segment extraction algorithm --- i.e., given a single point-of-interest, provide the best image segment containing that point. This induces a figure-ground image segmentation, which applies to a range of different segmentation tasks: single image segmentation, simultaneous co-segmentation of several images, and class-based segmentations.

The robot vision system is the critical component of the soccer robot, in football competition, robot perceive the most of the information from the vision system. Because of the variable illumination conditions, the traditional image segmentation method based on color information is not satisfactory. Based on the color information and shape information of the object, this paper proposes a object recognition algorithm that combine color image segmentation with edge detection. This algorithm implement image segmentation use color information in the HSV color space obtain the pixel of the object, then use this pixel implement edge detection to recognize the object. Experiments show that this algorithm can recognize the object exactly in the different illumination conditions, satisfy the requirement of the competition.

Clustering-based segmentation algorithm is one of the common methods in image segmentation. However, how to avoid the influence of the initialization and the singular value is a problem we have to face. For overcoming this problem, we proposed a new image segmentation technology which is based on the AIC criterion and not affected by the singular values. The simulation results show that the method has good performance on stability and accuracy. Image segmentation is the key step in image analysis of computer vision and image processing field. It has been widely applied in various areas such as in the process of automating production, sensing images and biomedical images. The main goal of image segmentation is to simplify an image into segments that have a strong correlation with objects in the real world. The existing segmentation algorithm can be generally divided into three classes: edge detection, region- based segmentation and data clustering. Edge detection method relies on the edges of the object with background and the other objects. For the images with noise, it is difficult to separate the boundary and the target background (1). Region- based segmentation method segments an image into several meaningful sub-domains that are non-overlapping and same- nature. But it may lead to the over-segmentation, and has to combine with other methods to use (2). Data clustering method is based on the whole image and considers the distance between each data. Advantages of data clustering method are low complexity and easy to implement. Disadvantages are sensitive to the noise and selection of the initial centroids (3). Data clustering is one of the common techniques in image segmentation. Its purpose is to cluster pixels into several parts (regions) according to the feature of image. Based on the properties of clustering algorithm, researchers have proposed various image segmentation algorithm, such as based on hierarchical clustering, based on K-means clustering, based on fuzzy clustering, based on mean shift, and so on (3-5). Clustering analysis is an unsupervised process of partitioning a data set into subsets of similar data objects. The elementary principle is as far as possible to increase the difference between categories and reduce the difference within the category. In the process of image segmentation based on data clustering, thus the objective is that the similar pixels are divided into the same regions and the non-similar pixels are divided into different regions. However, Two difficulties we must face are that how to determine the cluster centers and avoid influence of noise. In this paper, a new image segmentation algorithm is proposed based on the AIC criterion. The new segmentation technology can achieve an ideal performance and not affected by the initialization of centroids and the singular values. The simulation results verify the efficiency of proposed algorithm.

Traditional supervised learning methods have limitations in labeling speed, scene adaptability and accuracy.Unsupervised learning methods do not require labeling data and can automatically extract the laws, which provides a new idea for image segmentation, especially in medical diagnosis, automatic driving and other highprecision requirements of the scene has an important application value.This study explores unsupervised learningbased image segmentation methods in computer vision, focusing on the improved kernel fuzzy C-mean clustering algorithm (KFCM).The study constructs an image segmentation algorithm with noise robustness by introducing a kernel function instead of Euclidean distance and combining it with super-pixel segmentation technique.The experiments are validated on synthetic, natural and medical images and compared with various classical algorithms.The results show that when 30% Gaussian noise is added to the synthetic image, the segmentation accuracy of the KFCM algorithm reaches 99.8%, which is 12.6% higher than that of the traditional FCM; in the segmentation of the natural image with the addition of mixed noise, the average value of the segmentation coefficient of the KFCM reaches 96.45%, which is 17.47% higher than that of the FCM, and the segmentation entropy is reduced by 34.57%; and in the segmentation of the medical cell image, the KFCM algorithm shows good edge keeping ability in complex noise environment.The study shows that the improved KFCM algorithm significantly improves the image segmentation accuracy and anti-noise performance through the adaptive neighborhood information and kernel mapping, and provides an effective solution for unsupervised image segmentation, which is of practical application value for medical diagnosis, automatic driving and other fields.

The progress in the realm of image segmentation has helped farmers to use nominal inputs for higher production within limited time. Preliminary identification of diseases on fruits is limited to naked eyes since the majority of these symptoms can only be identified by microscopic visuals. Image segmentation plays a vital part in distinguishing their infected parts from the disinfected ones. In this paper, clustering is used as an approach in image segmentation to cautiously discover the affected parts of the fruits by segmenting the affected areas from the non-affected parts. Four clustering techniques—IS-KM, IS-FEKM, IS-MKM, and IS-FECA—were employed for this purpose. The quality of segmentation was evaluated using few performance measures like SC, RMSE, MSE, MAE, NAE, and PSNR. The result obtained using IS-FECA is more reasonable compared to the other methods. Roughly each value of performance parameters confers better results for IS-FECA-based image segmentation method, which means proper separation of diseased parts in fruits from their un-affected ones is attainable.

Image segmentation and image denoising are two important and fundamental topics in the field of image processing. Geometric active contour model based on level set method can deal with the problem of image segmentation, but it does not consider the problem of image denoising. In this paper, a new diffusion equation model for noisy image segmentation is proposed by incorporating some classical diffusion equation denoising models into the segmental process. An assumption about the connection between the image intensity and level set function is given firstly. Some classical denoising models are employed to describe the evolution of level set function secondly. The final nonlinear diffusion equation model for noisy image segmentation is built thirdly. Then image segmentation and image denoising are combined in a united framework. The segmental results can be presented by level set function. Experimental results show that the new model has the advantage of noise resistance and is superior to traditional segmentation model.

Image segmentation consists of dividing an image domain into disjoint regions according to a characterization of the image within or in-between the regions. Therefore, segmenting an image is to divide its domain into relevant components. The efficient solution of the key problems in image segmentation promises to enable a rich array of useful applications. The current major application areas include robotics, medical image analysis, remote sensing, scene understanding, and image database retrieval. The subject of this book is image segmentation by variational methods with a focus on formulations which use closed regular plane curves to define the segmentation regions and on a level set implementation of the corresponding active curve evolution algorithms. Each method is developed from an objective functional which embeds constraints on both the image domain partition of the segmentation and the image data within or in-between the partition regions. The necessary conditions to optimize the objective functional are then derived and solved numerically. The book covers, within the active curve and level set formalism, the basic two-region segmentation methods, multiregion extensions, region merging, image modeling, and motion based segmentation. To treat various important classes of images, modeling investigates several parametric distributions such as the Gaussian, Gamma, Weibull, and Wishart. It also investigates non-parametric models. In motion segmentation, both optical flow and the movement of real three-dimensional objects are studied.

Image segmentation has been considered as the first step in the image processing. An efficient segmentation result would make it easier for further analysis of image processing. However, there exits many algorithms and approaches for image segmentation. Clustering is one of the commonly used image segmentation techniques. In this paper, we have briefly describe some of the clustering techniques and discuss some of the recent works by researchers on these techniques.

Color Image Edge Detection Based on Fuzzy Rule Based Approach

The paper explores the use of evolutionary techniques in dealing with the image segmentation problem. An image is modeled as a weighted undirected graph, where nodes correspond to pixels, and edges connect similar pixels. A genetic algorithm that uses a fitness function based on an extension of the normalized cut criterion is proposed. The algorithm employs the locus-based representation of individuals, which allows for the partitioning of images without setting the number of segments beforehand. A new concept of nearest neighbor that takes into account not only the spatial location of a pixel, but also the affinity with the other pixels contained in the neighborhood, is also defined. Experimental results show that our approach is able to segment images in a number of regions that conform well to human visual perception. The visual perceptiveness is substantiated by objective evaluation methods based on uniformity of pixels inside a region, and comparison with ground-truth segmentations available for part of the used test images.