隨機寬線檢測方法

曲智國; 譚賢四; 林強; 王紅; 費太勇

doi:10.11999/JEIT170296

隨機寬線檢測方法

doi: 10.11999/JEIT170296 cstr: 32379.14.JEIT170296

基金項目:

國家自然科學基金 (61401504)，博士后科學基金(2014M 562562)

計量
- 文章訪問數(shù): 985
- HTML全文瀏覽量: 156
- PDF下載量: 155
- 被引次數(shù): 0
出版歷程
- 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: 為消除基本寬線檢測算子中的冗余計算量，提高算法的運算速度，該文提出一種寬線算子的快速實現(xiàn)方法隨機移動寬線算子。基本寬線算子采取逐像素移動模板的方式檢測圖像中的寬線特征，與之不同，隨機移動寬線算子在檢測時，隨機地在圖像中放置檢測模板，并根據(jù)當前像素類型采用啟發(fā)式的準則確定模板移動的策略，從而加快了模板移動速度，較好地消除了基本寬線檢測算法中的冗余運算；在此基礎上，提出了兩種提前結束條件，可根據(jù)檢測情況提前結束循環(huán)，進一步節(jié)省了運算量。利用測試圖像對快速算子進行了實驗分析，結果表明，隨機移動寬線算子在取得相當檢測性能的同時，提高了基本寬線算子的運算速度。
- 圖像處理 /
- 寬線特征 /
- 寬線算子 /
- 隨機移動 /
- 冗余計算
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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參考文獻(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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