【作者】 刘张桥；

【导师】 王成良；

【作者基本信息】 重庆大学 ， 计算机软件与理论， 2011， 硕士

【摘要】 随着图像采集技术的发展,人们可获得分辨率越来越高的图像,高效地提取高分辨率图像中大量可辨识信息对图像工程应用有重要意义。传统的多分辨率图像锥采用低通滤波技术,分割高分辨率图像时造成分割区域数量不等于图像中实际物体数量;串行的模糊C-均值(即FCM)算法分割图像时可能忽略空间上下文信息,且时间和空间复杂度很大。如何提高大幅图像的分割效率是图像处理领域的一个难题。本文提出基于正交小波分解的多分辨率图像锥结合FCM的图像分割方法,该方法综合分析正交小波分解法、多分辨率图像锥分割算法和FCM算法的多核并行性,设计出两者相结合的P-FCM多核并行模型,采用并行化语言OpenMP实现该模型。实验验证该算法在保证分割质量的前提下,较好地提高图像分割效率。本文主要工作包括:①对图像分割技术的研究背景、实际应用意义、国内外的研究现状和图像处理基础理论知识进行分析,并对多核并行发展及相关技术进行介绍。②针对传统图像锥设计过程计算量大和分割图像效率低的缺点,采用多核并行的正交小波分解法设计锥结构,并提出基于多分辨率图像锥的多核并行图像分割模型(P-多核并行锥模型)。③针对多分辨率图像锥分割图像可能存在过或低分割的问题,引入FCM算法,设计其多核并行模型,并与P-多核并行锥模型相结合,提出P-FCM多核并行模型。另外,对原始图像数据预处理时,采用矩形块并行分割方法来划分原始图像数据。④采用OpenMP对P-FCM多核并行模型编程,并统计分析该模型算法在分割不同大小和不同分辨率的图像时P-FCM多核并行模型的加速比。⑤通过实验验证该模型在处理高分辨率的大幅图像(大于1MB)时,随着CPU数量的增加,加速比呈接近线性方式递增。本文提出的P-FCM多核并行模型能很好地降低计算复杂度,提高图像分割效率,满足图像工程应用中实时性强和准确性高的要求。更多还原

【Abstract】 With the development of image acquisition technology, people can gain the higher and higher resolution image. It is useful to efficiently extract a large number of identifying information in the high resolution images for the application of Image Engineering. The traditional multiresolution image pyramid utilizes the low-pass filter technique, which easily causes the number of segmentation regions not equal to the true number of objects which really exist in the high resolution image; when the algorithm of fuzzy c-mean (FCM) is applied to segment the image, it may ignore the spatial context and also has the large complexity of time and space. How to improve the segmentation efficiency of large image is becoming a difficult problem.In this paper, a novel technique is proposed, which combines the multiresolution image pyramid based on the orthogonal wavelet decomposition with the FCM algorithm. This paper integrally analyses the muti-core parallel feasibility of the orthogonal wavelet decomposition, the multiresolution image pyramid segmentation algorithm and FCM algorithm, designs the multi-core parallel model of P-FCM which is the combination of the multiresolution image pyramid amd FCM, and the parallel language of OpenMP is adopted to achieve this model. Experiment testifies that this model preferably improves the efficiency of image segmentation under the promise of segmentation quality.The main contents of this paper can be summarized as follows:①Discusses and analyzes the image segmentation technique research background, practical application of significance, research status and the basic knowledge of image processing, then introduces the development of multi-core parallelism and the related technologies.②Aiming at the design method of traditional image pyramid is high computational complexity and the low efficiency of segmentation, a novel algorithm of multi-core parallel orthogonal wavelet decomposition is applied to design the pyramidal structure, and proposes the multi-core parallel model of image segmentation using multiresolution image pyramid (P- Multi-core Parallel Pyramidal Model).③Aiming at the problem of over - or under -segmentation using the multiresolution image pyramid, this paper introduces the algorithm of FCM, designs its multi-core parallel model, and presents the multi-core parallel model of P-FCM, which combines the multi-core parallel model of P with the multi-core parallel model of FCM. In addition, for the original image data preprocessing, the parallel method of rectangular block is used to divide the original image data.④The language of OpenMP is adopted to program the multi-core parallel model of P-FCM, and analyses the Speedup of the P-FCM multi-core parallel model when this algorithm is dealing with the images of different size and different resolution.⑤Experiments testify that the algorithm of P-FCM could achieve almost linear SpeedUP with the increase of CPU’s number, especially for the high resolution large image (over 1MB) segmentation.The multi-core parallel model of P-FCM proposed by this paper could reduce the computational complexity, improve the efficiency of image segmentation and meet the requirements of strong real-time and high accuracy in the applications of Image Engineering.更多还原