مقاله باینری کردن تصاویر و آستانه سازی تصاویر درجه خاکستری

A double-threshold image binarization method based on edge detector

Introduction

Image binarization is an important preprocessing technology for the recognition of handwritten literal amounts of checks [1,2], document image processing [3 7], recognition of fingerprint images [8], etc. Currently, many binarization methods have been presented, which can be categorized mainly into two classes: global thresholding methods [9 12] and adaptive thresholding methods [13 16]. The global thresholding methods are effective for images with an obviously bimodal histogram. Due to its poor robustness, however, global thresholding methods are not suitable for images with low contrast or non-uniform illumination. Otsu’s algorithm [9], a classical global thresholding method, reflects the intensity distribution of an image, but its property of a single threshold results in poor robustness. Although adaptive thresholding methods can deal with some complex images, they often ignore the edge property and lead to a fake shadow. Bernsen’s algorithm [13], a classical adaptive thresholding algorithm, computes a ∗ Corresponding author.

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A Theory Based on Conversion of RGB image to Gray

ABSTRACT

The use of color in image processing is motivated by two principal factors; First color is a powerful descriptor that often simplifies object identification and extraction from a scene. Second, human can discern thousands of color shades and intensities, compared to about only two dozen shades of gray. In RGB model, each color appears in its primary spectral components of red, green and blue. This model is based on Cartesian coordinate system. Images represented in RGB color model consist of three component images. One for each primary, when fed into an RGB monitor, these three images combines on the phosphor screen to produce a composite color image. The number of bits used to represent each pixel in RGB space is called the pixel depth. Consider an RGB image in which each of the red, green and blue images is an 8-bit image. Under these conditions each RGB color pixel is said to have a depth of 24 bit. MATLAB 7.0 2007b was used for the implementation of all results.

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A threshold selection method from gray level histogram

INTRODUCTION

It is important in picture processing to select an adequate threshold of gray level for extracting objects from their background. A variety of techniques have been proposed in this regard. In an ideal case, the histogram has a deep and sharp valley between two peaks representing objects and background, respectively, so that the threshold can be chosen at the bottom of this valley [1]. However, for most real pictures, it is often difficult to detect the valley bottom precisely, especially in such cases as when the valley is flat and broad, imbued with noise, or when the two peaks are extremely unequal in height, often producing no traceable valley.There have been some techniques proposed in order to overcome these difficulties. They are, for example, the valley sharpening technique [2], which restricts the histogram to the pixels with large absolute values of derivative (Laplacian or gradient), and the difference histogram method [3], which selects the threshold at the gray level with the maximal amount of difference.

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