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题名: 面向探测识别的图像优化方法研究
其他题名: Research on Image Optimization for Detection and Recognition
作者: 刘勋
导师: 朱枫
分类号: TP391.4
关键词: 图像优化 ; 探测识别 ; 人眼视觉系统 ; 临界可见偏差
索取号: TP391.4/L75/2011
学位专业: 模式识别与智能系统
学位类别: 硕士
答辩日期: 2011-05-27
授予单位: 中国科学院沈阳自动化研究所
学位授予地点: 中国科学院沈阳自动化研究所
作者部门: 光电信息技术研究室
中文摘要: 目标探测与识别包括目标探测和目标识别两个阶段,前者是当目标在图像中成像较小时探测出目标的位置,后者是当目标在图像中成像较大时识别出目标的类型。目标探测与识别在现代国防、工业、医学和空间等领域有着广泛的应用需求,已成为各领域的热点研究问题之一。 目标探测与识别的输入是图像,图像质量的好坏直接影响到探测与识别的结果,因此,对原始图像进行面向探测识别的优化处理是提高目标探测识别能力的一个有效手段。本文以人眼对图像的探测识别为背景,开展图像优化方法研究,具体内容包括: 首先,进行了现有典型图像优化方法的比较研究:面向探测识别,在现有图像优化方法中寻找优化效果较好的典型方法并实现;在此基础上,从优化效果、时间复杂度和空间复杂度三个方面对典型方法进行了比较分析与实验,得到如下结论:现有的图像优化方法没有确定的优化目标,不适用于探测识别任务。 其次,结合探测识别的特殊任务需求和人眼的视觉感知特性,提出了一种面向探测识别的图像优化方法。该方法的特点是能够在保持图像原有信息的前提下,使图像中相邻区域间的灰度差异最大限度地被人眼感知。其实现过程为:首先根据图像区域间的邻接关系,提出一种灰度合并策略,实现用最少的灰度来表示一幅图像;再基于人眼JND特性建立一种灰度映射关系,通过灰度映射的方式实现图像增强。实验结果表明:该方法的图像优化效果明显优于目前典型的图像优化方法。 最后,进行了图像优化方法工程实现技术研究:根据探测识别任务的工程化需求对前面提出的新算法进行了一系列优化,并在DSP C6455-850硬件平台上实现了该算法。实验结果表明:该算法能在限定的内存空间内可以满足处理实时性的要求。 综上所述,本文在对目前典型图像优化方法比较分析的基础上,提出了一种面向人眼探测识别的图像优化方法并在DSP硬件平台上进行了工程实现。该方法可以在保持图像原有信息的前提下,最大限度地提高人眼对图像信息的感知程度,从而提高人眼对图像中目标的探测识别能力。实验结果表明:该方法具有优化效果好、占内存空间小,处理速度快点等特点,具有很好的应用前景。
英文摘要: Target detection and recognition included two stage: target detection and target recognition. The former was to detect target location when small targets appeared in image, and the latter was to identify the target type when the target was large in the image. It was widely used in modern national defense, industry, medical and space fields and had become one of the hot research in various fields. The import of target detection and recognition was image. The quality of image had a direct impact on the results of detection and recognition. Therefore, image optimization was an effective means to raise the target detection and recognition. Based on the characteristics of human eyes detection and recognition, this article carried out the research of image optimization, including: First, we analyzed characteristics of tradition image optimization methods, implemented typical algorithms, complied the statistical tab of the time and space complexity of these algorithms and made some test. At this part, we obtained the following conclusion: The existing image optimization methods weren’t the task-oriented and applied to detection and recognition. Then, combined the special task-oriented of detection and recognition and the perceptual characteristics of human eyes, we proposed an image optimization method for detection and recognition. It could ensure that the gray differences between adjacent regions were maximumly perceived by human eyes under the premise of keeping information. In this method, based on the adjacency relation of of image regions, a gray consolidation strategy was proposed to represent image using the least gray. Then according to the JND curve,we signed a gray mapping relation for maximum perception of human eyes to enhance image. The experiments showed that this algorithm was better than current image enhancement methods in evidence. At end, we simplified the algorithm to the needs of engineering, implemented the algorithm at the DSP C6455-850 hardware platform. The experimental results showed that: This method could implement the real-time processing at the limited memory space of DSP.
语种: 中文
产权排序: 1
内容类型: 学位论文
URI标识: http://ir.sia.cn/handle/173321/9244
Appears in Collections:光电信息技术研究室_学位论文

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Recommended Citation:
刘勋.面向探测识别的图像优化方法研究.[硕士学位论文].中国科学院沈阳自动化研究所.2011
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