The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.02 - Feb. (2013 vol.35)
pp: 490-503
N. Petkov , Johann Bernoulli Inst. for Math. & Comput. Sci., Univ. of Groningen, Groningen, Netherlands
ABSTRACT
Background: Keypoint detection is important for many computer vision applications. Existing methods suffer from insufficient selectivity regarding the shape properties of features and are vulnerable to contrast variations and to the presence of noise or texture. Methods: We propose a trainable filter which we call Combination Of Shifted FIlter REsponses (COSFIRE) and use for keypoint detection and pattern recognition. It is automatically configured to be selective for a local contour pattern specified by an example. The configuration comprises selecting given channels of a bank of Gabor filters and determining certain blur and shift parameters. A COSFIRE filter response is computed as the weighted geometric mean of the blurred and shifted responses of the selected Gabor filters. It shares similar properties with some shape-selective neurons in visual cortex, which provided inspiration for this work. Results: We demonstrate the effectiveness of the proposed filters in three applications: the detection of retinal vascular bifurcations (DRIVE dataset: 98.50 percent recall, 96.09 percent precision), the recognition of handwritten digits (MNIST dataset: 99.48 percent correct classification), and the detection and recognition of traffic signs in complex scenes (100 percent recall and precision). Conclusions: The proposed COSFIRE filters are conceptually simple and easy to implement. They are versatile keypoint detectors and are highly effective in practical computer vision applications.
INDEX TERMS
Prototypes, Gabor filters, Shape, Detectors, Vectors, Handwriting recognition,shape, Feature detection, feature representation, medical information systems, object recognition, optical character recognition
CITATION
N. Petkov, "Trainable COSFIRE Filters for Keypoint Detection and Pattern Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 2, pp. 490-503, Feb. 2013, doi:10.1109/TPAMI.2012.106
REFERENCES
[1] C. Harris and M. Stephens, "A Combined Corner and Edge Detector," Proc. Fourth Alvey Vision Conf., pp. 147-151, 1988,
[2] C. Schmid and R. Mohr, "Local Grayvalue Invariants for Image Retrieval," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 5, pp. 530-535, May 1997.
[3] T. Lindeberg, "Feature Detection with Automatic Scale Selection," Int'l J. Computer Vision, vol. 30, no. 2, pp. 79-116, 1998.
[4] D.G. Lowe, "Object Recognition from Local Scale-Invariant Features," Proc. Seventh IEEE Int'l Conf. Computer Vision, vol. 2, pp. 1150-1157, 1999.
[5] K. Mikolajczyk and C. Schmid, "Indexing Based on Scale Invariant Interest Points," Proc. Eighth IEEE Int'l Conf. Computer Vision, vol. 1, pp. 525-531, 2001.
[6] K. Mikolajczyk and C. Schmid, "A Performance Evaluation of Local Descriptors," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615-1630, Oct. 2005.
[7] L. Florack, B. ter Haar Romeny, J. Koenderink, and M. Viergever, "General Intensity Transformations and Differential Invariants," J. Math. Imaging and Vision, pp. 171-187, 1994.
[8] F. Mindru, T. Tuytelaars, L. Van Gool, and T. Moons, "Moment Invariants for Recognition under Changing Viewpoint and Illumination," Computer Vision and Understanding, vol. 94, nos. 1-3, pp. 3-27, 2004.
[9] A. Baumberg, "Reliable Feature Matching Across Widely Separated Views," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 774-781, 2000.
[10] W. Freeman and E. Adelson, "The Design and Use of Steerable Filters," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 9, pp. 891-906, Sept. 1991.
[11] G. Carneiro and A. Jepson, "Multi-Scale Phase-Based Local Features," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. I-736-I-743, 2003.
[12] D. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," Int'l J. Computer Vision, vol. 60, pp. 91-110, 2004.
[13] Y. Ke and R. Sukthankar, "PCA-SIFT: A More Distinctive Representation for Local Image Descriptors," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. II-506-II-513, 2004.
[14] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, "Speeded-Up Robust Features (SURF)," Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, 2008.
[15] A. Pasupathy and C.E. Connor, "Responses to Contour Features in Macaque Area V4," J. Neurophysiology, vol. 82, no. 5, pp. 2490-2502, Nov. 1999.
[16] A. Pasupathy and C.E. Connor, "Population Coding of Shape in Area V4," Nature Neuroscience, vol. 5, no. 12, pp. 1332-1338, 2002.
[17] E. Gheorghiu and F.A.A. Kingdom, "Multiplication in Curvature Processing," J. Vision, vol. 9, no. 2, pp. 23:1-23:17, 2009.
[18] N. Petkov, "Biologically Motivated Computationally Intensive Approaches to Image Pattern-Recognition," Future Generation Computer Systems, vol. 11, nos. 4/5, pp. 451-465, 1995.
[19] N. Petkov and P. Kruizinga, "Computational Models of Visual Neurons Specialised in the Detection of Periodic and Aperiodic Oriented Visual Stimuli: Bar and Grating Cells," Biological Cybernetics, vol. 76, no. 2, pp. 83-96, 1997.
[20] P. Kruizinga and N. Petkov, "Non-Linear Operator for Oriented Texture," IEEE Trans. Image Processing, vol. 8, no. 10, pp. 1395-1407, Oct. 1999.
[21] S.E. Grigorescu, N. Petkov, and P. Kruizinga, "Comparison of Texture Features Based on Gabor Filters," IEEE Trans. Image Processing, vol. 11, no. 10, pp. 1160-1167, Oct. 2002.
[22] N. Petkov and M.A. Westenberg, "Suppression of Contour Perception by Band-Limited Noise and Its Relation to Non-Classical Receptive Field Inhibition," Biological Cybernetics, vol. 88, no. 10, pp. 236-246, 2003.
[23] C. Grigorescu, N. Petkov, and M.A. Westenberg, "The Role of Non-CRF Inhibition in Contour Detection," J. Computer Graphics, Visualization, and Computer Vision, vol. 11, no. 2, pp. 197-204, 2003.
[24] C. Grigorescu, N. Petkov, and M.A. Westenberg, "Contour Detection Based on Nonclassical Receptive Field Inhibition," IEEE Trans. Image Processing, vol. 12, no. 7, pp. 729-739, July 2003.
[25] C.D. Murray, "The Physiological Principle of Minimum Work: I. The Vascular System and the Cost of Blood Volume," Proc. Nat'l Academy of Sciences USA, vol. 12, pp. 207-214, 1926.
[26] C.D. Murray, "The Physiological Principle of Minimum Work Applied to the Angle of Branching of Arteries," J. General Physiology, vol. 9, pp. 835-841, 1926.
[27] T. Sherman, "On Connecting Large Vessels to Small—the Meaning of Murray Law," J. General Physiology, vol. 78, no. 4, pp. 431-453, 1981.
[28] M. Zamir, J. Medeiros, and T. Cunningham, "Arterial Bifurcations in the Human Retina," J. General Physiology, vol. 74, no. 4, pp. 537-548, 1979.
[29] M. Tso and L. Jampol, "Path-Physiology of Hypertensive Retinopathy," Opthalmology, vol. 89, no. 10, pp. 1132-1145, 1982.
[30] N. Chapman, G. Dell'omo, M.S. Sartini, N. Witt, A. Hughes, S. Thom, and R. Pedrinelli, "Peripheral Vascular Disease Is Associated with Abnormal Arteriolar Diameter Relationships at Bifurcations in the Human Retina," Clinical Science, vol. 103, no. 2, pp. 111-116, Aug. 2002.
[31] N. Patton, T.M. Aslam, T. MacGillivray, I.J. Deary, B. Dhillon, R.H. Eikelboom, K. Yogesan, and I.J. Constable, "Retinal Image Analysis: Concepts, Applications and Potential," Progress in Retinal and Eye Research, vol. 25, no. 1, pp. 99-127, Jan. 2006.
[32] J. Staal, M. Abramoff, M. Niemeijer, M. Viergever, and B. van Ginneken, "Ridge-Based Vessel Segmentation in Color Images of the Retina," IEEE Trans. Medical Imaging, vol. 23, no. 4, pp. 501-509, Apr. 2004.
[33] A. Bhuiyan, B. Nath, J. Chua, and K. Ramamohanarao, "Automatic Detection of Vascular Bifurcations and Crossovers from Color Retinal Fundus Images," Proc. Third IEEE Int'l Conf. Signal-Image Technologies and Internet-Based System, pp. 711-718. 2007,
[34] C. Liu, K. Nakashima, H. Sako, and H. Fujisawa, "Handwritten Digit Recognition: Benchmarking of State-of-the-Art Techniques," Pattern Recognition, vol. 36, no. 10, pp. 2271-2285, 2003.
[35] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-Based Learning Applied to Document Recognition," Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
[36] S. Belongie, J. Malik, and J. Puzicha, "Shape Matching and Object Recognition Using Shape Contexts," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 509-522, Apr. 2002.
[37] D. Oberhoff and M. Kolesnik, "Unsupervised Shape Learning in a Neuromorphic Hierarchy," Pattern Recognition and Image Analysis, vol. 18, pp. 314-322, 2008.
[38] A. Borji, M. Hamidi, and F. Mahmoudi, "Robust Handwritten Character Recognition with Features Inspired by Visual Ventral Stream," Neural Processing Letters, vol. 28, no. 2, pp. 97-111, 2008.
[39] M. Hamidi and A. Borji, "Invariance Analysis of Modified C2 Features: Case Study-Handwritten Digit Recognition," Machine Vision and Applications, vol. 21, no. 6, pp. 969-979, 2010.
[40] M. Ranzato, C. Poultney, S. Chopra, and Y. LeCun, "Efficient Learning of Sparse Representations with an Energy-Based Model," Advances in Neural Information Processing Systems, J. Platt et al., eds., MIT Press, 2006.
[41] C. Grigorescu and N. Petkov, "Distance Sets for Shape Filters and Shape Recognition," IEEE Trans. Image Processing, vol. 12, no. 10, pp. 1274-1286, Oct. 2003.
[42] C. Grigorescu, N. Petkov, and M.A. Westenberg, "Contour and Boundary Detection Improved by Surround Suppression of Texture Edges," Image and Vision Computing, vol. 22, no. 8, pp. 609-622, Aug. 2004.
[43] K. Bunte, M. Biehl, M.F. Jonkman, and N. Petkov, "Learning Effective Color Features for Content Based Image Retrieval in Dermatology," Pattern Recognition, vol. 44, no. 9, pp. 1892-1902, 2011.
[44] B. Hammer and T. Villmann, "Generalized Relevance Learning Vector Quantization," Neural Networks, vol. 15, nos. 8/9, pp. 1059-1068, 2002.
[45] S. Klement and T. Martinetz, "The Support Feature Machine for Classifying with the Least Number of Features," Proc. 20th Int'l Conf. Artificial Neural Networks: Part II, pp. 88-93, 2010.
[46] A. Pasupathy and C.E. Connor, "Shape Representation in Area V4: Position-Specific Tuning for Boundary Conformation," J. Neurophysiology, vol. 86, no. 5, pp. 2505-2519, Nov. 2001.
[47] J. Hegde and D.C. Van Essen, "A Comparative Study of Shape Representation in Macaque Visual Areas V2 and V4," Cerebral Cortex, vol. 17, no. 5, pp. 1100-1116, 2007.
[48] R. Gattass, A.P. Sousa, and C.G. Gross, "Visuotopic Organization and Extent of V3 and V4 of the Macaque," J. Neuroscience, vol. 8, no. 6, pp. 1831-1845, 1988.
[49] M. Riesenhuber and T. Poggio, "Hierarchical Models of Object Recognition in Cortex," Nature Neuroscience, vol. 2, no. 11, pp. 1019-1025, Nov. 1999.
[50] T. Serre, M. Kouh, C. Cadieu, U. Knoblich, G. Kreiman, and T. Poggio, "A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex," AI Memo 2005-036/CBCL Memo 259, Massachusetts Inst. of Tech nology, 2005.
[51] T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio, "Robust Object Recognition with Cortex-Like Mechanisms," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 3, pp. 411-426, Mar. 2007.
[52] C. Cadieu, M. Kouh, A. Pasupathy, C.E. Connor, M. Riesenhuber, and T. Poggio, "A Model of V4 Shape Selectivity and Invariance," J. Neurophysiology, vol. 98, no. 3, pp. 1733-1750, Sept. 2007.
[53] S. Fidler and A. Leonardis, "Towards Scalable Representations of Object Categories: Learning a Hierarchy of Parts," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2007.
[54] G. Azzopardi and N. Petkov, "A CORF Computational Model of a Simple Cell That Relies on LGN Input Outperforms the Gabor Function Model," Biological Cybernetics, vol. 106, pp. 177-189, 2011, doi: 10.1007/s00422-012-0486-6.
34 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool