Image thresholding is a very common image processing operation, since all image processing schemes need some sort of operation of the pixels into different classes. In order to determine thresholds, most methods analyze the histogram of the image. The optimal thresholds are often found by either minimizing or maximizing an objective function with respect to the values of the thresholds. In this...

In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. 3-16). We attempt to evaluate the...

In this paper, the RD-OPT algorithm for optimizing Discrete Cosine Transform quantization tables RL95] is extended to incorporate global thresholding. Performance gains are possible by zeroing oo some DCT coeecients in DCT-based image compression. We describe a global thresholding scheme, in which the zeroing thresholds for coeecients can be arbitrarily diierent from those determined by the qua...

Image thresholding plays an important role in image segmentation. This paper presents a novel fuzzy clustering based image thresholding technique, which incorporates the spatial neighborhood information into the standard fuzzy c-means (FCM) clustering algorithm. The prior spatial constraint, which is defined as weight in this paper, is inspired by the k-nearest neighbor (k-NN) algorithm and is ...

This study is focused on histogram thresholding methods automatically. In computer vision, Image segmentation is an initial and vital step in a series of processes aimed at overall image understanding. In other words Segmentation refers to the process of partitioning a digital image into the multiple segments (set of pixels as known as super pixels). Two very simple image segmentation technique...

Thresholding is one of the most widely used image segmentation operations; one application is foreground-background separation. Multilevel thresholding is the extension to segmentation into more than two classes. In order to find the thresholds, which separate the classes, the histogram of the image is analyzed. In most cases, the optimal thresholds are found by the minimazing or maximazing an ...

The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the literature has focused on developing the best uniform threshold or best basis selection. However, not much has been done to make the threshold values adaptive to the spatially changing statistics of images. Such adaptivity can improve the wave...

Thresholding is a simple but effective technique for image segmentation. In this paper, a general locally adaptive thresholding method using neighborhood processing is presented. The method makes use of local image statistics of mean and variance within a variable neighborhood and two thresholds obtained from the global intensity distribution. It can thus take advantage of local thresholding an...

This paper presents a selective local thresholding method of a camera based document image for applying character segmentation. It is very difficult to binarize a camera based document image due to inconsistent lighting condition. It makes difficulties of character extraction and recognition. A selective local thresholding method in character region can reduce illumination effect and extract pr...