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实时图像增强算法改进及FPGA实现 总被引:2,自引:0,他引:2
针对复杂背景的多目标图像,提出了一种基于直方图的实时自适应图像增强方法.该方法根据自适应直方图窗口选择高低阈值,通过灰度线性变换及灰度级等间距密度均衡进行图像增强.利用该算法增强图像视频时,采用FPGA,通过并行处理结构及流水线技术,可实时处理每秒50帧780×582×12bits的可见光图像.在处理视频的过程中,由前一帧图像的直方图信息,来增强后一帧图像.理论分析和实验结果均表明,该算法克服了直方图均衡及平台直方图均衡增强图像引起的灰度断层现象,有效地增强了图像的对比度,提高了图像质量. 相似文献
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Mutation testing has traditionally been used as a defect injection technique to assess the effectiveness of a test suite as
represented by a “mutation score.” Recently, mutation testing tools have become more efficient, and industrial usage of mutation
analysis is experiencing growth. Mutation analysis entails adding or modifying test cases until the test suite is sufficient
to detect as many mutants as possible and the mutation score is satisfactory. The augmented test suite resulting from mutation
analysis may reveal latent faults and provides a stronger test suite to detect future errors which might be injected. Software
engineers often look for guidance on how to augment their test suite using information provided by line and/or branch coverage
tools. As the use of mutation analysis grows, software engineers will want to know how the emerging technique compares with
and/or complements coverage analysis for guiding the augmentation of an automated test suite. Additionally, software engineers
can benefit from an enhanced understanding of efficient mutation analysis techniques. To address these needs for additional
information about mutation analysis, we conducted an empirical study of the use of mutation analysis on two open source projects.
Our results indicate that a focused effort on increasing mutation score leads to a corresponding increase in line and branch
coverage to the point that line coverage, branch coverage and mutation score reach a maximum but leave some types of code
structures uncovered. Mutation analysis guides the creation of additional “common programmer error” tests beyond those written
to increase line and branch coverage. We also found that 74% of our chosen set of mutation operators is useful, on average,
for producing new tests. The remaining 26% of mutation operators did not produce new test cases because their mutants were
immediately detected by the initial test suite, indirectly detected by test suites we added to detect other mutants, or were
not able to be detected by any test.
Ben Smith is a second year Ph.D. student in Computer Science at North Carolina State University working as an RA under Dr. Laurie Williams. He received his Bachelor’s degree in Computer Science in May of 2007 and he hopes to receive his doctorate in 2012. He has begun work on developing SQL Coverage Metrics as a predictive measure of the security of a web application. This fall, he will be beginning the doctoral preliminary exam and working as a Testing Manager for the NCSU CSC Senior Design Center: North Carolina State’s capstone course for Computer Science. Finally, he has designed and maintained the websites for the Center for Open Software Engineering and ESEM 2009. Laurie Williams is an Associate Professor in the Computer Science Department of the College of Engineering at North Carolina State University. She leads the Software Engineering Reasearch group and is also the Director of the North Carolina State University Laboratory for Collaborative System Development and the Center for Open Software Engineering. She is also technical co-director of the Center for Open Software Engineering (COSE) and the area technical director of the Secure Open Systems Initiative (SOSI) at North Carolina State University. Laurie received her Ph.D. in Computer Science from the University of Utah, her MBA from Duke University, and her BS in Industrial Engineering from Lehigh University. She worked for IBM for nine years in Raleigh, NC before returning to academia. Laurie’s research interests include agile software development methodologies and practices, collaborative/pair programming, software reliability and testing, and software engineering for secure systems development. 相似文献
Laurie WilliamsEmail: |
Ben Smith is a second year Ph.D. student in Computer Science at North Carolina State University working as an RA under Dr. Laurie Williams. He received his Bachelor’s degree in Computer Science in May of 2007 and he hopes to receive his doctorate in 2012. He has begun work on developing SQL Coverage Metrics as a predictive measure of the security of a web application. This fall, he will be beginning the doctoral preliminary exam and working as a Testing Manager for the NCSU CSC Senior Design Center: North Carolina State’s capstone course for Computer Science. Finally, he has designed and maintained the websites for the Center for Open Software Engineering and ESEM 2009. Laurie Williams is an Associate Professor in the Computer Science Department of the College of Engineering at North Carolina State University. She leads the Software Engineering Reasearch group and is also the Director of the North Carolina State University Laboratory for Collaborative System Development and the Center for Open Software Engineering. She is also technical co-director of the Center for Open Software Engineering (COSE) and the area technical director of the Secure Open Systems Initiative (SOSI) at North Carolina State University. Laurie received her Ph.D. in Computer Science from the University of Utah, her MBA from Duke University, and her BS in Industrial Engineering from Lehigh University. She worked for IBM for nine years in Raleigh, NC before returning to academia. Laurie’s research interests include agile software development methodologies and practices, collaborative/pair programming, software reliability and testing, and software engineering for secure systems development. 相似文献
56.
《Displays》2023
Aiming at the poor adaptability and robustness of existing pseudo-color methods, most of which can only deal with an 8-bit grayscale, an adaptive enhancement algorithm of high grayscale images based on priori knowledge was proposed. Firstly, aiming at the problem that the original RGB color space is not easy to adjust dynamically, the power adjustment was integrated into the constructed high-bit chromatogram, and a power adaptive adjustment function based on the brightness priority over the original grayscale image was designed. Secondly, aiming at the problems of over-exposure, under-exposure, and poor gradients in RGB space, an adaptive grayscale correction algorithm was designed according to the priori knowledge distribution of RGB perceived brightness. Finally, to guarantee the color balance of the enhanced image, a color balance correction algorithm based on CMY space was designed. To verify the effectiveness of this method, it was applied to the pseudo-color enhancement of 16-bit pipeline CR images, 14-bit infrared images, 24-bit pipeline DR weld images, 8-bit or 24-bit rail crack images, and 16-bit remote sensing images respectively. The subjective and objective experimental results show that the design method has stronger adaptability, which has obvious advantages compared with the existing advanced high grayscale image enhancement methods. The enhancement effect is more coordinated, the processing result is more in line with human visual perception, and the details and texture information of the original image can be better preserved. 相似文献
57.
《Displays》2023
As a comprehensive integration of many new-generation information technologies, the metaverse has become a research hotspot that has attracted much attention. As a part of the metaverse, the industrial metaverse is expected to break through the constraints of space and time and promote high-quality industrial development. The industrial metaverse is human-centric, so its quality of experience (QoE) is a key topic. As one of the enabling technologies of the industrial metaverse, Mixed Reality (MR) can seamlessly integrate virtual information with the physical world and is widely regarded as an important window to the industrial metaverse. In close integration with other enabling technologies, industrial MR applications can be seen as a path toward the realization of the industrial metaverse; thus, the optimization of industrial MR applications can effectively achieve the QoE enhancement of the industrial metaverse. Based on the analysis of existing research and the characteristics of industrial scenarios, consistency, authenticity, smoothness, and comfort are identified as the factors that influence the user experience (UX) of industrial MR applications. Specific optimization methods for industrial MR applications are proposed to improve the UX with regard to these four factors. To verify the effectiveness of the proposed methods, a QoE evaluation model of the industrial metaverse based on the fuzzy analytic hierarchy process (FAHP) is established. Moreover, an industrial metaverse prototype for longwall mining that incorporates the proposed methods is developed and its QoE is evaluated. The results show that the proposed optimization methods for industrial MR applications significantly enhance the QoE in the industrial metaverse, and can provide better services for users in industrial systems, thus better serving these systems. 相似文献
58.
David A. Bader Joseph Jájá David Harwood Larry S. Davis 《The Journal of supercomputing》1996,10(2):141-168
This paper presents efficient and portable implementations of a powerful image enhancement process, the Symmetric Neighborhood Filter (SNF), and an image segmentation technique that makes use of the SNF and a variant of the conventional connected components algorithm which we call -Connected Components. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The image segmentation algorithm makes use of an efficient connected components algorithm based on a novel approach for parallel merging. The algorithms have been coded in Split-C and run on a variety of platforms, including the Thinking Machines CM-5, IBM SP-1 and SP-2, Cray Research T3D, Meiko Scientific CS-2, Intel Paragon, and workstation clusters. Our experimental results are consistent with the theoretical analysis (and provide the best known execution times for segmentation, even when compared with machine-specific implementations). Our test data include difficult images from the Landsat Thematic Mapper (TM) satellite data.Also affiliated with the Department of Electrical Engineering.Also affiliated with the Department of Computer Science and the Center for Automation Research. 相似文献
59.
潘启勇 《计算机测量与控制》2011,19(9)
提出一种柔性制造系统(FMS)的故障诊断和可用性评价方法;针对传统的随机Petri网在解决FMS故障诊断上极大受限于底层马尔科夫链规模而容易产生状态爆炸的问题,首先,将蚁群优化算法(ACO)融入随机有色网(SPN)中,提出并定义了一种能对柔性制造系统的故障进行诊断的诊断器;然后,通过马尔科夫链计算制造单元的可用性,得到FMS到诊断器的映射,从而可以得到FMS中所有可能生产过程;最后,在经典FMS可用性评价方法的基础上,引入覆盖因子,提出了一种新的对FMS生产过程进行可用性评价的方法;仿真实验显示了覆盖因子对系统可用性的影响,通过与传统方法进行比较,表明覆盖因子越大,FMS的可用性越高。 相似文献
60.
针对PCB图像的特点对图像进行增强处理,使得图像的边缘更加突出,灰度的对比度增加,同时滤除靠近边缘的噪声,为后续图像分割时更有效的选取阈值打下良好基础。使用二阶梯度算子获得图像的增强效果,实验表明,不仅解决了边缘附近噪声去除问题,而且有效地突出了边缘信息,图像的黑白对比分明,尤其是目标图像靠近边缘部分灰度值明显高于其他的非边缘区域。 相似文献