Issue No. 02 - Feb. (2013 vol. 35)
G. Azzopardi , Johann Bernoulli Inst. for Math. & Comput. Sci., Univ. of Groningen, Groningen, Netherlands
N. Petkov , Johann Bernoulli Inst. for Math. & Comput. Sci., Univ. of Groningen, Groningen, Netherlands
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.
Prototypes, Gabor filters, Shape, Detectors, Vectors, Handwriting recognition,shape, Feature detection, feature representation, medical information systems, object recognition, optical character recognition
G. Azzopardi, N. Petkov, "Trainable COSFIRE Filters for Keypoint Detection and Pattern Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. , pp. 490-503, Feb. 2013, doi:10.1109/TPAMI.2012.106