Image Binarization using Otsu Thresholding Algorithm

2012

is a process of separation of pixel values of an input image into two pixel values like white as background and black as foreground. It is an important part in image processing and it is the first step in many document analysis and OCR processes. Most of the binarization techniques associate a certain intensity value called threshold which separate the pixel values of the concerned input grayscale image into two classes like background and foreground. Each and every pixel should be compared with the threshold and transformed to its respective class according to the threshold value. Thus threshold takes a major role in binarization. Hence determination of proper threshold value in binarization is a major factor of being a good binarised image and it can be approached in two categories like global thresholding and local thresholding techniques. In uniform contrast distribution of background and foreground documents, global thesholding is more suitable than that of local thresholding o...

Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics Service, 2008

2012

Image binarization is the process of separation of pixel values into two groups, white as background and black as foreground. Thresholding plays a major in binarization of images. Thresholding can be categorized into global thresholding and local thresholding. In images with uniform contrast distribution of background and foreground like document images, global thresholding is more appropriate. In degraded document images, where considerable background noise or variation in contrast and illumination exists, there exists many pixels that cannot be easily classified as foreground or background. In such cases, binarization with local thresholding is more appropriate. This paper describes a locally adaptive thresholding technique that removes background by using local mean and mean deviation. Normally the local mean computational time depends on the window size. Our technique uses integral sum image as a prior processing to calculate local mean. It does not involve calculations of standard deviations as in other local adaptive techniques. This along with the fact that calculations of mean is independent of window size speed up the process as compared to other local thresholding techniques.

A fresh and new algorithm for retrieval of image is accessible using improved Otsu thresholding techniques in this chapter. The fundamental design work of the new algorithm is that the basic segments of the image are used to retrieve images within a digital library. the histogram derived from threshold. These methods include some knowledge of the distribution of an image, & will result in the less miss-classification. Isodata algorithm is the iterative process for finding a threshold value. It first segments the image into two regions according to a temporary threshold value chosen .It then calculates the mean value of an image corresponding to the two segmented regions. Calculate the new threshold value & repeat until the threshold value does not change any more. Finally, choose this value for the threshold segmentation. One of the advantages of the algorithm is that, for a given retrieval image, the user can select a query segment with which to perform retrieval, thus it can satisfy different needs from different users.

Image segmentation is often used to distinguish the foreground from the background. Image segmentation is one of the difficult research problems in the machine vision industry and pattern recognition. Thresholding is a simple but effective method to separate objects from the background. A commonly used method, the Otsu method, improves the image segmentation effect obviously. It can be implemented by two different approaches: Iteration approach and Custom approach. In this paper both approaches has been implemented on MATLAB and give the comparison of them and show that both has given almost the same threshold value for segmenting image but the custom approach requires less computations. So if this method will be implemented on hardware in an optimized way then custom approach is the best option.

In this report two binary image thresholding methods are briefly described. Both methods assume that the image consists only two major parts: object (foreground) and background. First method is Otsu method. Threshold value in this method is calculated by minimizing the within-group variance. This method, however, has a possibility to falsely detect the background as foreground. Second method reported in this paper is Kittler-Illingworth (KI) method that attempts to overcome the shortcoming of Otsu method by assuming the histogram of the image consists two Gaussian distribution modes and the best threshold value is calculated by minimizing the Kullback-Lieber information distance. Both methods are tested to several images to visually check their performance in determining the threshold value. Other testing to these methods is a quantitative evaluation by measuring the uniformity measure of results is also presented.

Communications in Computer and Information Science, 2011

A good reference image is important for relative performance analysis of different image thresholding techniques in a quantitative manner. There exist standard methods for building reference images for document image binarization. However, a gap is found for graphic images referencing. This paper offers six different techniques for building reference images. These may be used for comparing different image thresholding techniques. Experimental results illustrate the relative performance of five different image thresholding methods for the six reference image building methods on a set of ten images taken from the USC-SIPI database. The results would help picking up the right reference image for evaluating binarization techniques.

Przeglad Elektrotechniczny

In the paper an algorithm for binarization of grayscale images representing text documents together with its optimization is presented. In the proposed approach two classical global thresholding algorithms proposed by Otsu and Kapur are combined with their local versions applied for blocks. The experimental results have been obtained for the H-DIBCO test dataset containing handwritten text images together with their "ground- truth" binary equivalents.

Applying binarization technique on a colored image can yield an image that is quite different from the original one if multi-thresholding is used. The solution proposed is an adaptive multi-thresholding technique (AMTT) that describes an adaptive multi-thresholding technique (AMTT) for binarization of color images depending upon the nature of the image. The technique improves the perception of the binarized images based on color hue and hence the dissimilarity between original and binarized image is reduced. This technique compensates for differences in illumination and shade by including information content in the thresholding calculation. AMTT calculates information loss in the image with respect to human perception on the basis of color hue. Experimental results show that the proposed AMTT reduces the color content by keeping the information loss minimum.

International Journal of Computer Applications, 2012

Binarization is a process of separation of pixel values of an input image into two pixel values like white as background and black as foreground. It is an important part in image processing and it is the first step in many document analysis and OCR processes. Most of the binarization techniques associate a certain intensity value called threshold which separate the pixel values of the concerned input grayscale image into two classes like background and foreground. Each and every pixel should be compared with the threshold and transformed to its respective class according to the threshold value. Thus threshold takes a major role in binarization. Hence determination of proper threshold value in binarization is a major factor of being a good binarised image and it can be approached in two categories like global thresholding and local thresholding techniques. In uniform contrast distribution of background and foreground documents, global thesholding is more suitable than that of local thresholding one. In degraded documents, where considerable background noise or variation in contrast and illumination exists, local technique is more suitable than that of global one. In this paper a local thresholding technique using local contrast and mean is described. Local adaptation is carried out with the local contrast and mean.