Journal paper

Jiapan Guo, Chenyu Shi, George Azzopardi, Nicolai Petkov, “Inhibition-augmented trainable COSFIRE filters for keypoint detection and object recognition” Machine Vision and Applications, doi: 10.1007/s00138-016-0777-3, 2016.
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Abstract

The shape and meaning of an object can radically change with the addition of one or more contour parts. For instance, a T-junction can become a crossover. We extend the COSFIRE trainable filter approach which uses a positive prototype pattern for configuration by adding a set of negative prototype patterns. The configured filter responds to patterns that are similar to the positive prototype but not to any of the negative prototypes. The configuration of such a filter comprises selecting given channels of a bank of Gabor filters that provide excitatory or inhibitory input and determining certain blur and shift parameters. We compute the response of such a filter as the excitatory input minus a fraction of the maximum of inhibitory inputs. We use three applications to demonstrate the effectiveness of inhibition: the exclusive detection of vascular bifurcations (i.e., without crossovers) in retinal fundus images (DRIVE data set), the recognition of architectural and electrical symbols (GREC’11 data set) and the recognition of handwritten digits (MNIST data set).

Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, the Netherlands