Trainable COSFIRE Filters for Keypoint Detection and Pattern Recognition

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 filte...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 35; no. 2; pp. 490 - 503
Main Authors: Azzopardi, G., Petkov, N.
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.02.2013
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Biological and medical sciences
Computer science; control theory; systems
Computer vision
Detection, estimation, filtering, equalization, prediction
Detectors
Exact sciences and technology
Eye and associated structures. Visual pathways and centers. Vision
Feature detection
feature representation
Fundamental and applied biological sciences. Psychology
Gabor filters
Handwriting recognition
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Information, signal and communications theory
medical information systems
object recognition
optical character recognition
Pattern recognition
Pattern Recognition, Automated - methods
Pattern recognition. Digital image processing. Computational geometry
Prototypes
Recall
Recognition
Reproducibility of Results
Sensitivity and Specificity
Shape
Signal and communications theory
Signal, noise
Studies
Subtraction Technique
Surface layer
Telecommunications and information theory
Texture
Vectors
Vertebrates: nervous system and sense organs
Gabor filter
Computer vision
Visual cortex
Information system
feature representation
shape
Bifurcation
Retina
Selectivity
medical information systems
Pattern recognition
Character recognition
Object recognition
Texture
Feature detection
Efficiency
Optical character recognition
Scene analysis
Classification
Filter bank
Circulatory system
Manuscript character
Medical application
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Description
Summary: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.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2012.106