Color Image Segmentation Technique Research Articles

Sign Language Recognition and Response via Virtual Reality

The main aim of this project is to provide a voice to voiceless people with the help of Python and Unity prototype systems. Aforesaid project work focuses on the problem of gesture recognition in real time that sign language is used by the community of deaf people. The problem addressed is based on Digital image processing using color segmentation, Skin detection, Image Segmentation, Image Filtering, and Template Matching techniques. This system recognizes gestures of ISL(Indian Sign Language) including the alphabet and a subset of its words.

A novel multilevel color image segmentation technique based on an improved firefly algorithm and energy curve

A novel multilevel color image segmentation technique based on an improved firefly algorithm and energy curve

ANALISIS PERBANDINGAN KLASIFIKASI CITRA MYCROBACTERIUM TUBERCULOSIS

Tuberculosis (TB) is an infectious disease that remains a challenging health problem worldwide. This disease is caused by rod-shaped bacteria called Mycobacterium tuberculosis. These bacteria usually affect the lungs but can also spread to other parts of the body such as the eyes, bones and blood vessels. It was reported that around. 4.74 million new TB cases were identified and around eight hundred thousand people died from TB, during 2015 in Southeast Asia. In this study, color image segmentation techniques were carried out using classification methods by comparing several methods including Logistic Regression, Linear Discriminant Analysis, K- Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier, Gaussian NaiveBayes, and Support Vector Machine. In the final stage it is known that the validation test on the Random Forest Classifier produces the highest value of 86.04%.

MENENTUKAN LUAS OBJEK CITRA DENGAN TEKNIK SEGMENTASI BERDASARKAN WARNA PADA RUANG WARNA HSV

The color image segmentation technique is one of many approaches used to extract information from an image. In this study, the calculation of the image object area was carried out using the color image segmentation technique in HSV color space. There are three objects that the area has calculated those are triangle, square, and circle. The object area calculated by multiply the image background original area with the proportion difference between the total number of object pixels and the sum of background pixels. The threshold value for the HSV filter was determined using two different approaches, which are the trial-and-error method and the multi-otsu thresholding method. The result of this study shows that the object area calculation system works pretty well. Using the trial-and-error method, the difference percentage mean of all objects is 1,35%, meanwhile, using the multi-otsu thresholding method is 1,31%. Keywords: Color segmentation, hsv, multi-otsu, object area.

Variational Mode Decomposition-Based Multilevel Threshold Selection Scheme for Color Image Segmentation

Image segmentation is a method of subdividing an image into numerous meaningful regions or objects, which makes the image more informative and easy to analyze. Thresholding-based approaches are extensively employed for segmenting the image due to their low computational cost and are easy implementation. However, histogram-based thresholding schemes suffer from high variation that leads to abnormalities and sharp specifics. In this paper, we propose a technique for multilevel color image segmentation through variational mode decomposition (VMD) and Kapur’s entropy. Initially, the VMD is employed in order to decompose the histogram into corresponding submodes of analysis and attributes extraction, which leads to the removal of the unfavorable effects. Then, Kapur’s entropy is incorporated in order to generate accurate and optimal thresholds for segmentation. For the performance evaluation of the presented VMD–Kapur algorithm, various qualitative metrics have been used such as probability rand index, mean-square error, peak signal-to-noise ratio, variation of information, structural similarity index, dice error, feature similarity index, entropy, Jaccard/Tanimoto error, and normalized absolute error. The experimental results show that the proposed technique produces best-segmented images compared to Kapur’s, Tsallis, Masi, and fuzzy entropies.

Oppositional symbiotic organisms search optimization for multilevel thresholding of color image

Selection of optimal threshold is the most crucial issue in threshold-based segmentation. In case of color image, this task is become challenging, because conventional color image segmentation has computational complexity and also it suffers from lack of accuracy. Various techniques such as threshold based, region growing, edge detection, graph cut, pixel classification, neural network, active contour, gray level co-occurrence matrix are proposed so far for image segmentation in the literature. Out of them, threshold-based segmentation is popular for its simplicity. To address the problem of color image segmentation, we propose an enhanced version of metaheuristic optimization algorithm called Opposition based Symbiotic Organisms Search (OSOS) to solve multilevel image thresholding technique for color image segmentation by introducing opposition based learning concepts to accelerate the convergence rate and enhance the performance of standard symbiotic organisms search (SOS). The performance of the proposed OSOS based algorithm is investigated thoroughly and compared with some existing techniques like Cuckoo Search (CS), BAT algorithm (BAT), artificial bee colony (ABC) and particle swarm optimization (PSO). The comparison is made by applying the algorithm to a set of color images taken from a well-known benchmark dataset (Berkeley Segmentation Dataset (BSDS)) and some of the color images collected for the COCO dataset. It is observed from the results that the performance of the OSOS based algorithm is promising with respect to standards SOS and others in terms of the values of objective functions as well as the values of some well-defined quality metrics such as peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The results of the proposed algorithm may encourage the scientists and engineers to apply it into pattern recognition problems.

Performance Evaluation of ACO based Metaheuristic Technique for Color Image Segmentation

Performance Evaluation of ACO based Metaheuristic Technique for Color Image Segmentation

Improved active contour modelling for isolating different hues in infrared thermograms

Thermograms are widely used in industries and medicine for quality assessment and diagnostics. Thermogram analysis involves isolating desirable Regions of Interest (RoIs) and characterizing them in terms of physical parameters. Conventionally, image segmentation techniques are developed to extract a specific Region of Interest. However, an efficient algorithm that could segment any desired Region of Interest is yet to be developed. In this study, an efficient color image segmentation technique for isolating RoIs (in infrared thermograms) is developed by using Improved Active Contour Modeling (I-ACM). Two different techniques namely thresholding and region growing, have been used for mask selection in order to increase the segmentation ability of the algorithm. The performance of the proposed techniques is measured in terms of error and computational complexity. A user friendly GUI has been also developed for computer-aided analysis.

Ant Colony based Metaheuristic Technique for Color Image Segmentation

age Segmentation is an essential procedure of image processing as well as knowing it. Mainly it's known due to the method of splitting the image into various elements of homogeneity. The goal of image segmentation is to make simpler presentation of an image into incredible which is meaningful as well as easy to understand. It is mainly utilized to know the location of objects, boundaries, lines etc in the digital images. Clustering technique is a method which shows the data set or pixels are replaced by cluster, pixels might be together because of the same color, texture etc. This paper represents the effect of mixed regions on image segmentation and color images contain maximum information for efficient image segmentation. KeywordsProcessing,Image Segmentation Techniques,

Color image segmentation using feedforward neural networks with FCM

This paper proposes a hybrid technique for color image segmentation. First an input image is converted to the image of CIE L*a*b* color space. The color features "a" and "b" of CIE L*a*b* are then fed into fuzzy C-means (FCM) clustering which is an unsupervised method. The labels obtained from the clustering method FCM are used as a target of the supervised feed forward neural network. The network is trained by the Levenberg-Marquardt back-propagation algorithm, and evaluates its performance using mean square error and regression analysis. The main issues of clustering methods are determining the number of clusters and cluster validity measures. This paper presents a method namely co-occurrence matrix based algorithm for finding the number of clusters and silhouette index values that are used for cluster validation. The proposed method is tested on various color images obtained from the Berkeley database. The segmentation results from the proposed method are validated and the classification accuracy is evaluated by the parameters sensitivity, specificity, and accuracy.

A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve

Amongst all the multilevel thresholding techniques, standard histogram based thresholding approaches are very impressive for bi-level thresholding. But, it is not effective to select spatial contextual information of the image for choosing optimal thresholds. In this paper, a new color image thresholding technique is presented by using an energy function to generate the energy curve of an image by considering spatial contextual information of the image. The property of this energy curve is very much similar to histogram of the image. To estimate the spatial contextual information for thresholding practice, in place of histogram, the energy curve function is used as an input. A new energy curve based color image segmentation approach using three well known objective functions named Kapur’s entropy, between-class-variance, and Tsalli’s entropy is proposed. In this paper, cuckoo search (CS) and egg lying radius-cuckoo search (ELR-CS) optimization algorithms with different parameter analysis have been used for solving the color image multilevel thresholding problem. The experimental results demonstrate that the proposed CS-Kapur’s energy curve based segmentation can powerfully and accurately search the multilevel thresholds.

Color Image Segmentation using Genetic Algorithm

This paper proposes color image segmentation approach and applying corresponding genetic algorithm under human vision limitations and capabilities. Most of the color image segmentation techniques initially use any clustering techniques to segment color images and then genetic algorithm (GA) is used only as optimization tool. Images are directly applied on 4D-color image histogram table using JND thresholds. The proposed algorithms are applied on Berkeley segmentation database in addition to general images. The segmentation performance of the proposed algorithms is estimated using Probabilistic Rand Index (PRI). The modified algorithm is proposed to improve the results and then compared with the proposed algorithm.

Color calibration and correction applying linear interpolation technique for color fringe projection system

We presents an efficient color calibration approach by establishing color correspondence between source (design) color and measured color, and color correspondence is used to correct the recorded color obtained by camera. The proposed technique is applied to improve the accuracy of color fringe projection system, which combines color image segmentation and color intensity linear interpolation technique. In our approach, only 24 color patterns are projected and are recorded to set up color correspondence, the total number of projected patterns is fewer than many previous reported methods; we deduce that the relation between source color and measured color meets a linear expression within a small local color space, and the linear expression is regarded as the base of succedent color correction. That means: for a given measured color, the initial source color is first searched based on the rule of a minimum color distance, and then linear interpolation is applied to the local adjacent color space of the initial source color point to implement color correction. Experiment results validate the correctness of the proposed approach.

Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms

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.

Color Image Segmentation By Cuckoo Search

In this paper, a clustering based color image segmentation technique is proposed and the clustering technique is optimized by the cuckoo search method. The proposed approach consists of two phase segmentation processes. In the first phase, cluster centres are optimized by using the cuckoo search algorithm and in the second phase, empty and frequent clutters are removed and merged according to pre-defined rules. This cluster centre based clustering technique is then used to find the optimum centre within a cluster, while cuckoo search is applied to find the optimum cluster centre for each segment in the image. Comparison of the proposed method is performed with the genetic algorithm (GA), dynamic control particle swarm optimization (DCPSO) algorithm and firefly algorithm based color image segmentation methods over five benchmark color images. The parameters of the proposed method are tuned through empirical testing. Results demonstrated that the proposed method can be an effective tool for image segmentation.

Vivid-DIBR Based 2D–3D Image Conversion System for 3D Display

The multi-view perspectives are captured around a scene by camera array at the same time, and multi-view images cannot only keep two dimensions information but also has the depth architecture of a scene. Most of 3D displays support the multi-view contents, while there is a bottleneck of the 3D TV system to broadcast the larger storage and bandwidth for enduser. For saving bandwidth, a new TV broadcasting channel transfers third dimensional information, i.e., depth map has been utilized. The 3D display renders intermediate image and a depth map to form different views, as well as synthesizes these images into one image; this technology is the renowned Depth Image Based Rendering (DIBR). In other words, any 2D content can be transformed into multi-view images according to their corresponding depth map. Our system processes 2D images to capture a depth map of the intermediate view image during 3D contents capturing. In this paper, we utilize the vanishing point detection and color image segmentation technique to find the objects and deepest point (i.e., vanishing point) in the image, and then we assign depth value by comparing the vanishing point of object with image. After depth map generation, we propose the Vivid-DIBR system to imitate how human eyes see things. On the other hand, this system solves the holes (warping error points) problem by redistributing corresponding depth map. The design aims to convert 2D images into multi-view images which will be suitable for any interlacing 3D display by adjusting the focal plane location.

SURVEY ON CLUSTERING BASED COLOR IMAGE SEGMENTATION AND NOVEL APPROACHES TO FCM ALGORITHM

Segmentation is an important image processing techn ique that helps to analyze an image automatically. Applications involving detection or recognition of objects in images often include segmentation process. This paper describes two unsupervised clustering based color image segmentation technique s namely K-means clustering and Fuzzy C-means (FCM) clustering. The advantages and disadvantages of both K-means and Fu zzy C-means algorithm are also presented in this pa per. K-means algorithm takes less computation time as compared t o Fuzzy C-means algorithm which produces result clo se to that of K-means. On the other hand in FCM algorithm each pixel of an image can have membership to more than one cluster which is not in case of K-means algorithm, an advantage to FCM method. C olor images contain wide variety of information and are more complicated than gray scale images. In image proces sing, though color image segmentation is a challeng ing task but provides a path for image analysis in practical application fi elds. Secondly some novel approaches to FCM algorit hm for better image segmentation are also discussed such as SFCM (Spati al FCM) and THFCM (Thresholding FCM). Basic FCM alg orithm does not take into consideration the spatial information of the image. SFCM specially focus on spatial details and contribute towards image segmentation results for image analysis. It i ntroduces spatial function into FCM algorithm membe rship function and then operates with available spatial information. THFCM is another approach that focus on thresholding tech nique for image segmentation. It main task is to find a discerner c luster that will act as automatic threshold. These two approaches shows how better segmentation results can be obtained.

Review of Graph, Medical and Color Image base Segmentation Techniques

This literature review attempts to provide a brief overview of some of the most common segmentation techniques, and a comparison between them.It discusses Graph based methods, Medical image segmentation research papers and Color Image based Segmentation Techniques. With the growing research on image segmentation, it has become important to categorise the research outcomes and provide readers with an overview of the existing segmentation techniques in each category. In this paper, different image segmentation techniques starting from graph based approach to color image segmentation and medical image segmentation, which covers the application of both techniques, are reviewed.Information about open source software packages for image segmentation and standard databases are provided. Finally, summaries and review of research work for image segmentation techniques along with quantitative comparisons for assessing the segmentation results with different parameters are represented in tabular format, which are the extracts of many research papers. Index Terms—Graph based segmentation technique, medical image segmentation, color image segmentation, watershed (WS) method, F-measure, computerized tomography (CT) images I. Introduction Image segmentation is the process of separating or grouping an image into different parts. There are currently many different ways of performing image segmentation, ranging from the simple thresholding method to advanced color image segmentation methods. These parts normally correspond to something that humans can easily separate and view as individual objects. Computers have no means of intelligently recognizing objects, and so many different methods have been developed in order to segment images. The segmentation process in based on various features found in the image. This might be color information, boundaries or segment of an image. The aim of image segmentation is the domain-independent partition of the image into a set of regions, which are visually distinct and uniform with respect to some property, such as grey level, texture or colour. Segmentation can be considered the first step and key issue in object recognition, scene understanding and image understanding. Its application area varies from industrial quality control to medicine, robot navigation, geophysical exploration, military applications, etc. In all these areas, the quality of the final results depends largely on the quality of the segmentation. In this review paper we will discuss on graph based segmentation techniques, color image segmentation techniques and medical image segmentation, which is the real time application and very important field of research. The mathematical details are avoided for simplicity.

A Brief Survey of Color Image Preprocessing and Segmentation Techniques

Close The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1558-884X) immediately upon acceptance. Submit Manuscript A Brief Survey of Color Image Preprocessing and Segmentation Techniques Siddhartha Bhattacharyya JPRR Vol 6, No 1 (2011); doi:10.13176/11.191

Bootstrapping approach to feature-weight selection in fuzzy c-means algorithms with an application in color image segmentation

The fuzzy c-means (FCM) algorithm is a popular fuzzy clustering method. It is known that an appropriate assignment to feature weights can improve the performance of FCM. In this paper, we use the bootstrap method proposed by Efron [Efron, B., 1979. Bootstrap methods: Another look at the jackknife. Ann. Statist. 7, 1–26] to select feature weights based on statistical variations in the data. It is simple to compute and interpret for feature-weights selection. Compared with the feature weights proposed by Wang et al. [Wang, X.Z., Wang, Y.D., Wang, L.J., 2004. Improving fuzzy c-means clustering based on feature-weight learning. Pattern Recognition Lett. 25, 1123–1132], Modha and Spangler [Modha, D.S., Spangler, W.S., 2003. Feature weighting in k-means clustering. Machine Learn. 52, 217–237], Pal et al. [Pal, S.K., De, R.K., Basak, J., 2000. Unsupervised feature evaluation: A neuro-fuzzy approach. IEEE Trans. Neural Networks 11, 366–376] and Basak et al. [Basak, J., De, R.K., Pal, S.K., 1998. Unsupervised feature selection using a neuro-fuzzy approach. Pattern Recognition Lett. 19, 997–1006] we find that the proposed method provides a better clustering performance for Iris data and several simulated datasets based on error rate criterion and also performs well in color image segmentation according to Liu and Yang’s [Liu, J., Yang, Y.H., 1994. Multiresolution color image segmentation technique. IEEE Trans. Pattern Anal. Machine Intell. 16, 689–700] evaluation function.