publication . Conference object . Part of book or chapter of book . 2017

Detection of curved lines with B-COSFIRE filters : a case study on crack delineation

Nicola Strisciuglio; George Azzopardi; Nicolai Petkov;
Open Access English
  • Published: 27 Jul 2017
  • Publisher: Cornell University
  • Country: Malta
Abstract
The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis thresholding and morph...
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Subjects
free text keywords: Computer vision, Image processing, Pattern recognition systems, Ranging, Robotics, Curvilinear coordinates, Computer science, Computer vision, Detector, Image processing, Segmentation, Artificial intelligence, business.industry, business, Biometrics, Closing (morphology)
Communities
Rural Digital Europe
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http://arxiv.org/pdf/1707.0774...
Part of book or chapter of book
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OAR@UM
Conference object . 2017
Provider: OAR@UM
http://link.springer.com/conte...
Part of book or chapter of book . 2017
Provider: Crossref
27 references, page 1 of 2

1. Fosa: F* seed-growing approach for crack-line detection from pavement images. Image and Vision Computing 29(12), 861 { 872 (2011)

2. Azzopardi, G., Petkov, N.: A CORF computational model of a simple cell that relies on lgn input outperforms the gabor function model. Biological Cybernetics 106, 177{189 (2012) [OpenAIRE]

3. Azzopardi, G., Petkov, N.: Trainable COSFIRE lters for keypoint detection and pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 490{503 (2013) [OpenAIRE]

4. Azzopardi, G., Strisciuglio, N., Vento, M., Petkov, N.: Trainable COSFIRE lters for vessel delineation with application to retinal images. Medical Image Analysis 19(1), 46 { 57 (2015)

5. Bibiloni, P., Gonzalez-Hidalgo, M., Massanet, S.: A survey on curvilinear object segmentation in multiple applications. Pattern Recognition 60, 949 { 970 (2016)

6. Chai, D., Forstner, W., Lafarge, F.: Recovering Line-networks in Images by Junction-Point Processes. In: CVPR (2013)

7. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement ltering, pp. 130{137 (1998)

8. Gecer, B., Azzopardi, G., Petkov, N.: Color-blob-based COSFIRE lters for object recognition. Image and Vision Computing 57, 165 174 (2017)

9. Grigorescu, C., Petkov, N., Westenberg, M.A.: Contour and boundary detection improved by surround suppression of texture edges. Image and Vision Computing 22(8), 609{622 (2004) [OpenAIRE]

10. Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched lter response. IEEE Trans. Med. Imag. 19(3), 203{210 (2000)

11. Lacoste, C., Descombes, X., Zerubia, J.: Point processes for unsupervised line network extraction in remote sensing. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1568{1579 (2005)

12. Lafarge, F., Gimel'farb, G., Descombes, X.: Geometric feature extraction by a multimarked point process. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1597{ 1609 (2010) [OpenAIRE]

13. Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Transactions on Medical Imaging 35(11), 2369{2380 (2016)

14. Martinez-Perez, M.E., Hughes, A.D., Thom, S.A., Bharath, A.A., Parker, K.H.: Segmentation of blood vessels from red-free and uorescein retinal images. Medical Image Analysis 11(1), 47{61 (2007)

15. Mendonca, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Transactions on Medical Imaging 25(9), 1200{1213 (2006)

27 references, page 1 of 2
Abstract
The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis thresholding and morph...
Persistent Identifiers
Subjects
free text keywords: Computer vision, Image processing, Pattern recognition systems, Ranging, Robotics, Curvilinear coordinates, Computer science, Computer vision, Detector, Image processing, Segmentation, Artificial intelligence, business.industry, business, Biometrics, Closing (morphology)
Communities
Rural Digital Europe
Download fromView all 2 versions
http://arxiv.org/pdf/1707.0774...
Part of book or chapter of book
Provider: UnpayWall
OAR@UM
Conference object . 2017
Provider: OAR@UM
http://link.springer.com/conte...
Part of book or chapter of book . 2017
Provider: Crossref
27 references, page 1 of 2

1. Fosa: F* seed-growing approach for crack-line detection from pavement images. Image and Vision Computing 29(12), 861 { 872 (2011)

2. Azzopardi, G., Petkov, N.: A CORF computational model of a simple cell that relies on lgn input outperforms the gabor function model. Biological Cybernetics 106, 177{189 (2012) [OpenAIRE]

3. Azzopardi, G., Petkov, N.: Trainable COSFIRE lters for keypoint detection and pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 490{503 (2013) [OpenAIRE]

4. Azzopardi, G., Strisciuglio, N., Vento, M., Petkov, N.: Trainable COSFIRE lters for vessel delineation with application to retinal images. Medical Image Analysis 19(1), 46 { 57 (2015)

5. Bibiloni, P., Gonzalez-Hidalgo, M., Massanet, S.: A survey on curvilinear object segmentation in multiple applications. Pattern Recognition 60, 949 { 970 (2016)

6. Chai, D., Forstner, W., Lafarge, F.: Recovering Line-networks in Images by Junction-Point Processes. In: CVPR (2013)

7. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement ltering, pp. 130{137 (1998)

8. Gecer, B., Azzopardi, G., Petkov, N.: Color-blob-based COSFIRE lters for object recognition. Image and Vision Computing 57, 165 174 (2017)

9. Grigorescu, C., Petkov, N., Westenberg, M.A.: Contour and boundary detection improved by surround suppression of texture edges. Image and Vision Computing 22(8), 609{622 (2004) [OpenAIRE]

10. Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched lter response. IEEE Trans. Med. Imag. 19(3), 203{210 (2000)

11. Lacoste, C., Descombes, X., Zerubia, J.: Point processes for unsupervised line network extraction in remote sensing. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1568{1579 (2005)

12. Lafarge, F., Gimel'farb, G., Descombes, X.: Geometric feature extraction by a multimarked point process. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1597{ 1609 (2010) [OpenAIRE]

13. Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Transactions on Medical Imaging 35(11), 2369{2380 (2016)

14. Martinez-Perez, M.E., Hughes, A.D., Thom, S.A., Bharath, A.A., Parker, K.H.: Segmentation of blood vessels from red-free and uorescein retinal images. Medical Image Analysis 11(1), 47{61 (2007)

15. Mendonca, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Transactions on Medical Imaging 25(9), 1200{1213 (2006)

27 references, page 1 of 2
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