A New Local Adaptive Thresholding Technique in Binarization

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.

In this paper, a color image binarization method based on isodata thresholding technique is developed. A threshold value is computed separately for each of the color component of the given input RGB image. The binary images of each color component image along with the binary image of the converted grayscale image are added together to produce the resultant binary image. The result of this proposed method was compared with the popular Otsu’s thresholding method. The proposed method found to produce better result than the Otsu’s thresholding technique.

In this paper, a new binarization algorithm for degraded document images is proposed. The method is based on positive and negative pixel energies using the Laplacian of an image. After a filtering step and morphological operations our local contrast segmentation method is able to detecting connected components. The given approach is applied to cases of sophisticated, challenging documents and other application scenarios like whiteboard and chalk images.

Computer Vision and Graphics, 2016

This paper describes a simple analytic method for determining an optimal threshold position during low-contrast grayscale image binarization. The described method uses no tunable or user-set parameters and is invariant to both image size and pixel intensity value range. It uses both the pixel intensity histogram of the analysed image and its first-order derivative to estimate the threshold position. There is an assumption that the histogram is bimodal by its nature, however, the method can cope with the histograms where the peaks are very close or even overlapping due to low contrast. The method performance is comparable with the traditionally used Otsu's method [1] on unproblematic images, however, it significantly outperforms Otsu's on specific images, like e.g. electron microscopy of composite material fibres or x-ray mammography, where the bimodal histogram is almost (but not entirely) collapsed into unimodal.

Pattern Recognition Letters, 2011

Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.

Image binarization is an important step in many applications. With the existence of new imaging devices such as smart phones, the types of application become wider. Image binarization is the process of finding the proper image pixels threshold. Threshold methods can be grouped into two main categories: global and local. Local thresholding produces good binary images but is time consuming, which may not be a ppropriate for some imaging applications. On the other hand, global thresholding is fast but does not produce the target accuracy. In this article, a simple yet efficient method is presented. It is a mixture of global and local thresholding. It can be classified as local thresholding. However, it does not require calculations based on each pixel in the given image. It also automates the choice of thresholding free parameter. The resulting performance is comparable to the best of the existing methods and is faster than many of them.

International Journal of Research in Engineering and Technology, 2016

This paper presents a hybrid approach for binarization as well as a review on Binarization of images. Binarization is a process by which an image is converted into bits. It is an important step in most document image analysis systems. An image is a set of pixels. Most of the binarization techniques associate a certain intensity value for each and every pixel. A gray image or gray-scale image is just an image which has each pixel of same intensity. That means there is not much difference in color or value information of pixels. Usually, in common terms, a picture in black and white is considered as gray image in which black has least intensity and white have highest.

Pomiary Automatyka Robotyka, 2014

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In digital image processing, binarization (two-level thresholding) is a commonly used technique for image segmentation. It is the process of converting a gray scale image to a binary image. Furthermore, binarization methods are divided into two groups as global binarization and locally adaptive binarization. A number of binarization techniques have been proposed over the years. Bernsen's method is one of locally adaptive binarization methods developed for image segmentation. In this study, Bernsen's locally adaptive binarization method is implemented and then tested for different gray scale images.

2006

The problem of binarization of gray level images acquired under nonuniform illumination is reconsidered. Yanowitz and Bruckstein proposed to use for image binarization an adaptive threshold surface, determined by interpolation of the image gray levels at points where the image gradient is high. The rationale is that high image gradient indicates probable object edges, and there the image values are between the object and the background gray levels. The threshold surface was determined by successive overrelaxation as the solution of the Laplace equation. This work proposes a different method to determine an adaptive threshold surface. In this new method, inspired by multiresolution approximation, the threshold surface is constructed with considerably lower computational complexity and is smooth, yielding faster image binarizations and better visual performance.