随机宽线检测方法

曲智国; 谭贤四; 林强; 王红; 费太勇

doi:10.11999/JEIT170296

随机宽线检测方法

doi: 10.11999/JEIT170296

基金项目:

国家自然科学基金 (61401504)，博士后科学基金(2014M 562562)

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- 文章访问数: 938
- HTML全文浏览量: 145
- PDF下载量: 155
- 被引次数: 0
出版历程
- 收稿日期: 2017-04-05
- 修回日期: 2017-11-06
- 刊出日期: 2018-01-19

Randomized Wide Line Detector

Funds:

The National Natural Science Foundation of China (61401504), China Postdoctoral Science Foundation (2014M 562562)

摘要

摘要: 为消除基本宽线检测算子中的冗余计算量，提高算法的运算速度，该文提出一种宽线算子的快速实现方法随机移动宽线算子。基本宽线算子采取逐像素移动模板的方式检测图像中的宽线特征，与之不同，随机移动宽线算子在检测时，随机地在图像中放置检测模板，并根据当前像素类型采用启发式的准则确定模板移动的策略，从而加快了模板移动速度，较好地消除了基本宽线检测算法中的冗余运算；在此基础上，提出了两种提前结束条件，可根据检测情况提前结束循环，进一步节省了运算量。利用测试图像对快速算子进行了实验分析，结果表明，随机移动宽线算子在取得相当检测性能的同时，提高了基本宽线算子的运算速度。
- 图像处理 /
- 宽线特征 /
- 宽线算子 /
- 随机移动 /
- 冗余计算
Abstract: To eliminate computation redundancy and improve speed of the basic wide line detector, a fast implementation, named randomized moving wide line detector, is proposed. Instead of moving the mask pixel by pixel to detect wide lines as did in the basic implementation, the randomized moving wide line detector places the mask in the image randomly, and then determines the mask moving strategy heuristically according to the current pixel. In this way, the mask moving is accelerated, leading to obvious decrease of computational redundancy in the basic detector. Furthermore, two early termination conditions are proposed to break out of the detecting loop based on the detection situation of wide lines. Testing images are adopted for performance evaluation of the randomized moving wide line detector. Experimental results demonstrate that the proposed detector accelerates the basic wide line detector significantly while keeping its detection performance unaffected.
- Image processing /
- Wide line feature /
- Wide line detector /
- Randomized moving /
- Computational redundancy

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参考文献(19)

LINDEBERG T. Edge detection and ridge detection with automatic scale selection[J]. International Journal of Computer Vision, 1998, 30(2): 117-154. doi: 10.1023/A: 1008097225773.

JACOB M and UNSER M. Design of steerable filters for feature detection using Canny-Like criteria[J]. IEEE Transaction on Pattern Analysis Machine Intelligence, 2004, 26(8): 1007-1019. doi: 10.1109/TPAMI.2004.44.

EBERLY D, GARDNER R, MORSE B, et al. Ridges for image analysis[J]. Journal of Mathematical Imaging and Vision, 1994, 4(4): 353-373. doi: 10.1007/BF01262402.

AGGARWAL N and KARL W C. Line detection in images through regularized Hough transform[J]. IEEE Transactions on Image Processing, 2006, 15(3): 582-591. doi: 10.1109/TIP. 2005.863021.

XU Zezhong, SHIN Bok Suk, and KLETTE Reinhard. Closed form line-segment extraction using the Hough transform[J]. Pattern Recognition, 2015, 48(12): 4012-4023. doi: 10.1016/j. patcog.2015.06.008.

LOPEZ-MOLINA C, VIDAL-DIEZ DE ULZURRUN G, BAETENS J M, et al. Unsupervised ridge detection using second order anisotropic Gaussian kernels[J]. Signal Processing, 2015, 116: 55-67. doi: 10.1016/j.sigpro.2015.03. 024.

HU Yangyang, ZHANG Wenqiang, LU Hong, et al. Wide line detection with water flow[C]. 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, 2016: 1353-1355.

PANDEY D, YIN Xiaoxia, WANG Hua, et al. Accurate vessel segmentation using maximum entropy incorporating line detection and phase-preserving denoising[J]. Computer Vision and Image Understanding, 2017, 155: 162-172. doi: 10.1016/j.cviu.2016.12.005.

SUDESHNA S K and SANTI P M. Retinal blood vessel extraction using tunable bandpass filter and fuzzy conditional entropy[J]. Computer Methods and Programs in Biomedicine, 2016, 133: 111-132. doi: 10.1016/j.cmpb.2016. 05.015.

LI Shuxiao, CHANG Hongxing, and ZHU Chengfei. Fast curvilinear structure extraction and delineation using density estimation[J]. Computer Vision and Image Understanding, 2009, 113(6): 763-775. doi: 10.1016/j.cviu.2009.01.003.

LIU L, ZHANG D, and YOU J. Detecting wide lines using isotropic nonlinear filtering [J]. IEEE Transactions on Image Processing, 2007, 16(6): 1584-1595. doi: 10.1109/TIP.2007. 894288.

LIU L, NGADI M O, PRASHER S O, et al. Objective determination of pork marbling scores using the wide line detector[J]. Journal of Food Engineering, 2012, 110: 497-504. doi: 10.1016/j.jfoodeng.2011.11.008.

CRISTIAN V and SERGIU N. Detecting curvilinear featuresusing structure tensors[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3874-3887. doi: 10.1109/TIP. 2015.2447451.

SMITH S M and BRADY J M. SUSANA new approach to low level image processing[J]. International Journal of Computer Vision, 1997, 23(1): 45-78. doi: 10.1023/A: 1007963824710.

KNUTH D E, MORRIS J H, and PRATT V R. Fast pattern matching in strings[J]. SIAM Journal of Computing, 1977, 6: 323-350. doi: 10.1137/0206024.

BOYER R S and MOORE J S. A fast string searching algorithm[J]. Communications of the ACM, 1977, 20(10): 762-772. doi: 10.1145/359842.359859.

ABDOU I E and PRATT W K. Quantitative design and evaluation of enhancement/thresholding edge detectors[J]. Proceedings of the IEEE, 1979, 67(5): 753-763. doi: 0.1109/ PROC.1979.11325.

IGOR S and ALEKSEJ A. Convolutional neural network based automatic object detection on aerial images[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 14(5): 740-744. doi: 10.1109/LGRS.2016.2542358.

RODRIGO F N, ROBERTO A L, and RUBENS C M. Fingerprint liveness detection using convolutional neural networks[J]. IEEE Transactions on Information Forensics and Security, 2016, 11(6): 1206-1213. doi: 10.1109/TIFS. 2016.2520880.

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