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THRESHOLD BASED COLOR IMAGE SEGMENTATION FOR GROUNDNUT PLANT IMAGESGowrishankar. K1, Kalaiselvi. N2, Dhanalakshmi.K3Research Scholars, Periyar University, Salem-11ABSTRACTImage segmentation is used widely in many applications.This paper gives a study of the threshold technique in color image segmentation.Image segmentation is fundamental approaches of image processing . Several general purpose algorithms and techniques have been developed for image segmentation. Segmentation applications are involving detection, recognition and measurement of features. The purpose of image segmentation is to partition an image into meaningful regions with respect to a particular application. Segmentation techniques can be classified as either contextual or non-contextual. Thresholding is a Non-Contextual Approach. This paper enumerates performance of threshold technique in color image segmentation for groundnut images.Key words: Image Segmentation, Threshold, Image Processing, Groundnut ImagesI INTRODUCTIONImage segmentation is a mechanism used to divide an image into multiple segments. It will make image smooth and easy to evaluate. Segmentation process also helps to find region of interest in a particular image1. One of the most important problems in color image analysis is that of segmentation. The fundamental idea in color image segmentation is to consider color uniformity as a relevant criterion to partition an image into significant regions. People are only interested in certain parts of the image. These parts are frequently referred as foreground or target and other is called background. Image segmentation is a technique and process which divide the image into different feature of region and extract out the interested target. It divides an image into a number of discrete regions such that the pixels have high similarity in each region and high contrast between regions. Properties like intensity, texture, depth, gray-level, color help to recognize similar regions, such properties are used to form groups of regions having a similar meaning. Segmentation is a valuable tool in many fields including health care, industry, remote sensing, image processing, content based image, pattern recognition, traffic image, video and computer vision. A particular type of image segmentation method can be found in application involving the recognition, measurement of objects and detecting objects in an image. Many researches have focused on gray-level image segmentation, whereas the color images carry most of the information. Segmentation techniques can be classified into the following categories: Edge-based, Cluster-based, Threshold based, Neural Network based, Region-based and Hybrid. Image segmentation based on thresholding is one of the oldest and powerful technique, since the threshold value divides the pixels in such a way that pixels having intensity value less than threshold belongs to one class while pixels whose intensity value is greater than threshold belongs to another class. Segmentation based on edge detection attempts to resolve image by detecting the edges between different regions that have sudden change in intensity value are extracted and linked to form closed region boundaries. Region based methods, divides an image into different regions that are similar according to a set of some predefined conditions.2 .The simplest method of image segmentation is called the thresholding method. This method is based on a clip-level (or a threshold value) to turn a gray-scale image into a binary image. There is also a balanced histogram thresholding.The key of this method is to select the threshold value (or values when multiple-levels are selected). Several popular methods are used in industry including the maximum entropy method, Otsu’s method (maximum variance), and k-means clustering.Recently, methods have been developed for thresholding computed tomography (CT) images. The key idea is that, unlike Otsu’s method, the thresholds are derived from the radiographs instead of the (reconstructed) image.910New methods suggested the usage of multi-dimensional fuzzy rule-based non-linear thresholds. In these works decision over each pixel’s membership to a segment is based on multi-dimensional rules derived from fuzzy logic and evolutionary algorithms based on image lighting environment and application.11The Neural Network based image segmentation techniques reported in the literature can mainly be classified into two categories: supervised and unsupervised methods. Supervised methods require expert human input for segmentation. Usually this means that human experts are carefully selecting the training data that is then used to segment the images. Unsupervised methods are semi or fully automatic. User intervention might be necessary at some point in the process to improve performance of the methods, but the results should be more or less human independentII RELATED WORKSThe segmentation is used to separate the image in parts that represents an interest object. There are several methods in that intend to perform such task that can adapt to different types of images that are very complex and specific. The goal of segmentation is to simplify and change the representation of an image into something that is easier to analyze and more meaningful. In the computer vision field to understanding images the information extracted from them can be used for other tasks for example identification of an airport from remote sensing data detection of cancerous cells, extracting malign tissues from body scans, navigation of robots. Now there is a need of a method, to understand images and extract information or objects, image segmentation fulfill above requirements. Practical application of image segmentation range from medical applications are Treatment planning, filtering of noisy images, Locate tumors, Measure tissue volumes, Diagnosis, Computer guided surgery, study of anatomical structure, Locate objects in satellite images like forests and roads, Face Recognition and Finger print Recognition. Many segmentation methods have been proposed in this literature survey. Segmentation technique are chooses over the level of segmentation are decided by the particular type of image and characteristics of the problem being considered.Liju Dong et al (2008) proposed an iterative algorithm for finding optimal thresholds that minimize a weighted sum of squared error objective function. This method is mathematically equivalent to the well known Otsu’s method. The computational complexity is linear with respect to the number of thresholds to be calculated as against the exponential complexity of the Otsu’s algorithm. K-Means method is compared to that of classical Otsu’s method in multilevel thresholding by Dongji Liu et al (2009). This both method are based on a same criterion that minimizes within class variance. Otsu’s method is an exhaustive algorithm of searching the global optimal threshold k-means is a local optimal method. K-means does not require computing a gray level histogram firstly. K-means can be more efficiently extended to multilevel thresholding method than Otsu’s method. K-means method performs well with less computing time than Otsu’s method does on three dimensional image thresholding. Numbers of methods are proposed for image segmentation on color and gray scale images that involves features extraction and image segmentation. The problems in image segmentation are slower processing time, long latency, and large memory storage. The Otsu’s Thresholding algorithm is widely used in image segmentation that encounters the problem of computational time. Triclass thresholding technique is proposed for color and complex images also that reduces the computation time.III THRESHOLDING TECHNIQUE FOR COLOR IMAGE SEGMENTATIONThresholding is the simplest method of image segmentation. From a gray scale image, thresholding can be used to create binary images. Binary images are produced from color images by segmentation. Segmentation is the process of assigning each pixel in the source image to two or more classes. If there are more than two classes then the usual result is several binary images. In image processing, thresholding is used to split an image into smaller segments, or junks, using at least one color or gray scale value to define their boundary. The advantage of obtaining first a binary image is that it reduces the complexityof the data and simplifies the process of recognition and classification2.The most common way to convert a gray level image to a binary image is to select a single threshold value (T)2 .The input to a thresholding operation is typically a gray scale or color image. In the simplest implementation, the output is a binary image representing the segmentation. Black pixels correspond to background and white pixels correspond to foreground (or vice versa). This method of segmentation applies a single fixed criterion to all pixels in the image simultaneously 8.Image Segmentation = divide image into (continuous) regions or sets of pixels. The pixels are partitioned depending on their intensity value.Segment image into foreground and background.g(x, y) = 1 if f(x,y) is foreground pixel = 0 if f(x, y) is background pixel In real applications histograms are more complex,with many peaks and not clear valleys and it is not always easy to select the value of T.This technique can be expressed as:T=Tx, y, p(x, y), f(x, yWhere f(x, y) is the gray level and p (x, y) is some local property.F(x, y) >T called an object point otherwise the point is called a background point.1.Global thresholding:The global threshold applicable when the intensity distribution of objects and background pixels are sufficiently distinct. In the global threshold, a single threshold value is used in the whole image. The global threshold has been a popular technique in many years 678. . When the pixel values of the components and that of background are fairly consistent in their respective values over the entire image, global thresholding could be used.Global Thresholding = Choose threshold T that separates object from background. If g(x, y) is a threshold version of f(x, y) at some global threshold T,There are a number of global thresholding techniques such as: Otsu, optimal thresholding, histogram analysis, iterative thresholding, maximum correlation thresholding, clustering, Multispectral and Multithresholding.IV PROPOSED METHOD FOR COLOR IMAGE SEGMENTATIONThe basic steps for  methodology of thresholding color images.The images were taken Groundnut farm by high resolution camera. Each image containing a rush of number of leaves and dissoved with infected leaves and healthy leaves. Here the basic steps for color image thresholding is given below.Step 1: The color image is taken as an input I.Step 2: Find the global threshold or determine the optimal threshold.Step 3: Based on the input image intensity levels similarities between the intensities are grouped.Step 4: Using the excitatory and inhibitory functions, the input I produces the output vectors J which construct from the global threshold value.AlgorithmThe algorithm contains the methodology for color image segmentationStep1: Intialize value for TStep2: Separate RGB, Planes. µ1, µ2, µ3.Step 3: Separate high intensity pixels from original image  µ(i,j).Using,                     µs = µ1,- µ1,? TStep 4: Reconstruct segmented image µT = (µi, µj) Step 5: Repeat steps for all Pixels µs(i,j) i=rowsj=columns V. RESULTS AND DISCUSSIONThe following images contains original images and segmented images                Fig. 1: 001.jpg Fig.2: 002.jpg                Fig.3: 003.jpg Fig.4: 004.jpg            Fig.4: 005.jpg Fig.6: 006.jpgA number of color image segmentation experiments are performed on rush images. The images are collected from a groundnut farm Using high resolution camera, containing 6 set of color images each one represented with real world pictures. Figure 1- 6 contains the different threshold values obtained using various segmentation methods for complex, real and low intensity images.Where (a) represents the segmented image and (b) represents the original images (001.jpg – 006.jpg). where the segmented images are clearly shown the defected area from rush image of ground nut leaves it shown in fig.(1- 6) a.VI. FUTURE WORK Supervised methods require expert human input for segmentation. Usually this means that human experts are carefully selecting the training data that is then used to segment the images. The future method has to be faster and achieve a better results in all kind of rush image for color segmentation. VII. CONCLUSIONIn this color image segmentation a new approach has been presented that is based on the R, G, and B channels, these channels will produce some kind of noise and to remove this kind of noise a median filtering process was proposed. It shows the fast to reach satisfactory results. In order to decrease the computation time the threshold values are initialized. The threshold values are calculated based on the type of image used. VIII. REFERENCES 1 Waseem Khan,Image Segmentation Techniques: A Survey, Journal of Image and Graphics Vol. 1, No. 4, December 20132. K. Bhargavi, “A Survey on Threshold Based Segmentation Technique in Image Processing”, International journal of innovative research and development, November, 2014 Vol 3 Issue 12, ISSN 2278 – 0211.3.Saleh Al-Amri1, N.V. Kalyankar2 And KhamitkarS.D”Image Segmentation By Using Threshold Techniques”, Journal Of Computing, Vol.2issue 5, Pp. 83-86, 20104C. H. Bindu and K. S. Prasad (2012), “An efficient medical image segmentation using conventional OTSU method”, Int. J. Adv. Sci. Technol., vol. 38, pp. 67– 74.5 R. F. Moghaddam and M. Cheriet (2012), “AdOtsu: An adaptive and parameterless generalization of Otsu’s method for document image binarization”, Pattern Recognit., vol. 45, no. 6, pp. 2419–2431.6 Dr.VipulSingh,Digital Image Processing With Matlab And Lab View; Elsevier 2013.7Khang Siang Tan, ” Color Image Segmentation Using Histogram Thresholding-Fuzzy C-Means Hybrid Approach”, Pattern Recognition, Vol44,Pp 1-15, 2011.8G. Parthasarathy, “Thresholding Technique for Color Image Segmentation”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volume 3 Issue VI, June 2015, ISSN: 2321-9653 9 Batenburg, K J.; Sijbers, J. (2009). “Adaptive thresholding of tomograms by projection distance minimization”. Pattern Recognition. 42 (10): 2297–2305.  200810Batenburg, K J.; Sijbers, J. (June 2009). “Optimal Threshold Selection for Tomogram Segmentation by Projection Distance Minimization” (PDF). IEEE Transactions on Medical Imaging. 28 (5): 676–686. 11 Kashanipour, A.; Milani, N; Kashanipour, A.; Eghrary, H. (May 2008). “Robust Color Classification Using Fuzzy Rule-Based Particle Swarm Optimization” (PDF). IEEE Congress on Image and Signal Processing. 2: 110–114.

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