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论文摘要

基于非采样高斯差分金字塔的多尺度融合边缘检测

MultiscaleFusedEdgeDetectionAlgorithmBasedonNon-sampling DifferenceofGaussianPyramid

作者:穆柯楠(长安大学信息工程学院);赵祥模(长安大学信息工程学院);惠飞(长安大学信息工程学院)

Author:MuKenan(SchoolofInfo.Eng.,Chang’anUniv.);ZhaoXiangmo(SchoolofInfo.Eng.,Chang’anUniv.);HuiFei(SchoolofInfo.Eng.,Chang’anUniv.)

收稿日期:2014-12-31          年卷(期)页码:2015,47(5):130-138

期刊名称:工程科学与技术

Journal Name:Advanced Engineering Sciences

关键字:非采样高斯差分金字塔;多尺度分解;边缘检测;车辆检测

Key words:non-samplingDifferenceofGaussianPyramid;multiscaledecomposition;edgedetection;vehicledetection

基金项目:国家自然科学基金资助项目(51278058);教育部博士点基金新教师资助项目(20120205120002)

中文摘要

针对传统边缘检测算法抗噪性差、边缘连续度低、细节边缘冗余,对运动目标检测应用领域的适用性差等缺点,基于图像多尺度的思想,结合小尺度图像边缘信息准确,大尺度图像抗噪性强、边缘冗余度低的优点,提出一种基于非采样高斯差分金字塔的多尺度融合边缘检测算法。算法首先对图像进行非采样高斯金字塔分解得到多尺度图像,同时在分解过程实现基于高斯差分算子的边缘检测,得到多尺度边缘图像。最后采用多尺度图像边缘融合策略实现多尺度边缘融合。通过实验对算法的有效性进行验证:通过对边缘融合结果进行Abdou-Pratt品质因数分析,表明该算法抗噪性强,边缘定位准确;连续度分析结果表明该算法在降低边缘冗余度的同时保留了主要边缘,且边缘连续度较高;车辆检测实验结果表明基于该算法得到的车辆检测结果准确度较高。

英文摘要

Traditional edge detection algorithms are low in noise immunity, high in edge continuity,redundant in edge details,and poor in the applicability for moving target detection.In order to treat these problems,a multiscale fused edge detection algorithm was proposed based on non-sampling Difference of Gaussian (DoG) Pyramid.With the concept of multiscale image,the algorithm combined the accurate edge information in small scale image and strong noise immunity and low edge redundancy in large scale image.Experiments verified the effectiveness of the proposed algorithm.Analysis of Abdou-Pratt quality factor on the fused edge image showed that the proposed algorithm is strong in noise immunity and accurate in edge location.Analysis of edge continuity showed that the proposed algorithm decreases edge continuity while keeps main edges,and obtains edges with higher continuity.Experiment result of vehicle detection showed that the vehicle detection result based on the proposed algorithm has higher accuracy.

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