Skip to main content
Article thumbnail
Location of Repository

Trainable COSFIRE filters for keypoint detection and pattern recognition

By George Azzopardi and Nicolai Petkov


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.\ud \ud 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.\ud \ud 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).\ud \ud 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.peer-reviewe

Topics: Medical innovations, Medical technology, Optical character recognition, Tracking and trailing
Publisher: IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2013
DOI identifier: 10.1109/TPAMI.2012.106
OAI identifier:
Provided by: OAR@UM

Suggested articles


  1. (1988). A Combined Corner and Edge Detector,” doi
  2. (2007). A Comparative Study of Shape Representation doi
  3. (2011). A CORF Computational Model of a Simple Cell That Relies on doi
  4. (2005). A Performance Evaluation of Local Descriptors,” doi
  5. (2005). A Theory of Object Recognition: doi
  6. (1979). Arterial Bifurcations in the Human Retina,” doi
  7. (2007). Automatic Detection of Vascular Bifurcations and Crossovers from Color Retinal Fundus Images,” doi
  8. (1995). Biologically Motivated Computationally Intensive Approaches to Image Pattern-Recognition,” doi
  9. (1997). Computational Models of Visual Neurons Specialised in the Detection of Periodic and Aperiodic Oriented Visual Stimuli: Bar and Grating Cells,” doi
  10. (2004). Contour and Boundary Detection Improved by Surround Suppression of doi
  11. (2003). Contour Detection Based on Nonclassical Receptive Field Inhibition,” doi
  12. (2003). Distance Sets for Shape Filters and Shape Recognition,” doi
  13. (2004). Distinctive Image Features from Scale-Invariant Keypoints,” doi
  14. (1998). Feature Detection with Automatic Scale Selection,” doi
  15. (2002). Generalized Relevance Learning Vector Quantization,” doi
  16. (1998). Gradient-Based Learning Applied to Document Recognition,” doi
  17. (2003). Handwritten Digit Recognition: doi
  18. (1999). Hierarchical Models of Object Recognition in
  19. (2001). Indexing Based on Scale Invariant Interest Points,” doi
  20. (2010). Invariance Analysis of Modified C2 Features: Case Study-Handwritten Digit Recognition,” doi
  21. (2011). Learning Effective Color Features for Content Based Image Retrieval doi
  22. (1997). Local Grayvalue Invariants for Image Retrieval,” doi
  23. (2004). Moment Invariants for Recognition under Changing Viewpoint doi
  24. (2003). Multi-Scale Phase-Based Local Features,” doi
  25. (1999). Non-Linear Operator for Oriented Texture,” doi
  26. (1999). Object Recognition from Local Scale-Invariant Features,” doi
  27. (1981). On Connecting Large Vessels to Small—the Meaning of Murray Law,” doi
  28. (2004). PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,” doi
  29. (2002). Peripheral Vascular Disease Is Associated with Abnormal Arteriolar Diameter Relationships at Bifurcations in the Human Retina,” doi
  30. (2002). Population Coding of Shape doi
  31. (2000). Reliable Feature Matching Across Widely Separated Views,” doi
  32. (1999). Responses to Contour Features in
  33. (2006). Retinal Image Analysis: Concepts, doi
  34. (2004). Ridge-Based Vessel Segmentation in doi
  35. (2008). Robust Handwritten Character Recognition with Features Inspired by doi
  36. (2007). Robust Object Recognition with Cortex-Like Mechanisms,” doi
  37. (2002). Shape Matching and Object Recognition Using Shape Contexts,” doi
  38. (2001). Shape Representation in Area V4: Position-Specific Tuning for Boundary Conformation,” doi
  39. (2003). Suppression of Contour Perception by Band-Limited Noise and Its Relation to NonClassical Receptive Field Inhibition,” doi
  40. (1991). The Design and Use of Steerable Filters,” doi
  41. (1926). The Physiological Principle of Minimum Work Applied to the Angle of doi
  42. (1926). The Physiological Principle of Minimum Work: I. The Vascular System and the Cost of Blood Volume,” doi
  43. (2010). The Support Feature Machine for Classifying with the Least Number of Features,” doi
  44. (2007). Towards Scalable Representations of Object Categories: Learning a Hierarchy of Parts,” doi
  45. (2008). Unsupervised Shape Learning in a Neuromorphic Hierarchy,” doi
  46. (1988). Visuotopic Organization and Extent of V3

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.