A Fastest Patchwise Histogram Construction Algorithm based on Cloud-Computing Architecture

A Fastest Patchwise Histogram Construction Algorithm based on Cloud-Computing Architecture

Chung-Chih Cheng (Department of Electronic Engineering, National Taipei University of Technology, Taipei City, Taiwan), Fan-Chieh Cheng (Department of Electronic Engineering, National Taipei University of Technology, Taipei City, Taiwan), Po-Hsiung Lin (Department of Electronic Engineering, National Taipei University of Technology, Taipei City, Taiwan), Wen-Tzeng Huang (Department of Electronic Engineering, National Taipei University of Technology, Taipei City, Taiwan) and Shih-Chia Huang (Department of Electronic Engineering, National Taipei University of Technology, Taipei City, Taiwan)
Copyright: © 2017 |Pages: 12
DOI: 10.4018/IJWSR.2017010101
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Abstract

The histogram in each patch of the input image is a useful feature applied for various development of image processing techniques. However, if the size of the input image is very large, the histogram construction of each patch in the image becomes very time-consuming. For applications involving the processing of several very large images, this paper proposes a superior patchwise histogram construction algorithm based on cloud-computing architecture that is faster than similar state-of-the-art approaches. Through the modern communication network, the computation cost can be easily shared to construct several patchwise histograms at the same time. The proposed algorithm is the fastest solution in the field as well as applicable to various data processing procedures related to probability distribution. Experimental results show that the proposed algorithm has the best performance compared to other related algorithms.
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1. Introduction

In the field of image processing, the intensity probability distribution of an input 2-D image is usually represented as a 1-D intensity histogram. As image resolution usually increases due to the significant improvement of digital imaging techniques, the local intensity probability distribution of each patch in the input image needs to be individually estimated. This, in turn, will result in improved effectiveness of the related image processing algorithms, such as data hiding and watermarking (Guo, 2007; Lee et al., 2013; Guo et al., 2009; Wang et al., 2014; Guo et al., 2008; Guo & Liu, 2010; Guo & Liu, 2010), video quality analysis (Huang, 2011), face detection (Guo et al., 2011), image classification (Liu et al., 2011), contrast enhancement (Huang et al., 2013), traffic monitoring (Huang et al., 2013), motion detection (Huang et al., 2014), and so on.

Object detection is a well-known feature generation task that generally is used to estimate the intensity probability distributions in video surveillance systems (Chen & Chen, 2008; Luo et al., 2010). For detection of specific objects or behaviors, such as humans and their actions, the patch histogram is vital to locating the detection region of the input image. In addition to object detection, the video surveillance system also incorporates visual tracking, by which one or more objects are located over time (Medeiros et al., 2010; Nejhum et al., 2010). Based on its generated features, an object can be tracked by measuring the maximum similarity of the local region of the image. Hence, image histograms are applicable as measurement features. As probability or other selective features are generated, target recognition functions use them to classify the specific objects, actions, et cetera (Ning et al., 2009; Ruta et al., 2010).

The amount of noise present in an image is used as the significant index for measuring its quality. Hence, noise reduction algorithms have been proposed for image improvement. For noise estimation or reduction, the patch histogram is often employed (Porikli, 2008; Wan et al., 2010). Image contrast affects visual perception when images are observed. Histogram equalization (HE) is the traditional contrast enhancement method, which maps the curve of the intensity probability density (Liu et al., 2011; Celik et al., 2011). For single image haze removal, the dark channel prior (DCP) method has been the most popular method in recent years (He et al., 2011). The DCP-based defogging algorithms must minimize the gray-level of each pixel to model the illumination and reflectance parameter. As the number of neighbor pixels increases, the process may incorporate the patch histogram construction procedure to reduce computation complexity.

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