2017
1.
Gecer, Baris; Azzopardi, George; Petkov, Nicolai
Color-blob-based COSFIRE filters for object recognition Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, image classification, pattern recognition, trainable filters
@article{gecer2017color,
title = {Color-blob-based COSFIRE filters for object recognition},
author = {Baris Gecer and George Azzopardi and Nicolai Petkov},
doi = {https://doi.org/10.1016/j.imavis.2016.10.006},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Image and Vision Computing},
volume = {57},
pages = {165--174},
publisher = {Elsevier},
abstract = {Most object recognition methods rely on contour-defined features obtained by edge detection or region segmentation. They are not robust to diffuse region boundaries. Furthermore, such methods do not exploit region color information. We propose color-blob-based COSFIRE (Combination of Shifted Filter Responses) filters to be selective for combinations of diffuse circular regions (blobs) in specific mutual spatial arrangements. Such a filter combines the responses of a certain selection of Difference-of-Gaussians filters, essentially blob detectors, of different scales, in certain channels of a color space, and at certain relative positions to each other. Its parameters are determined/learned in an automatic configuration process that analyzes the properties of a given prototype object of interest. We use these filters to compute features that are effective for the recognition of the prototype objects. We form feature vectors that we use with an SVM classifier. We evaluate the proposed method on a traffic sign (GTSRB) and a butterfly data sets. For the GTSRB data set we achieve a recognition rate of 98.94%, which is slightly higher than human performance and for the butterfly data set we achieve 89.02%. The proposed color-blob-based COSFIRE filters are very effective and outperform the contour-based COSFIRE filters. A COSFIRE filter is trainable, it can be configured with a single prototype pattern and it does not require domain knowledge.},
keywords = {brain-inspired, image classification, pattern recognition, trainable filters},
pubstate = {published},
tppubtype = {article}
}
Most object recognition methods rely on contour-defined features obtained by edge detection or region segmentation. They are not robust to diffuse region boundaries. Furthermore, such methods do not exploit region color information. We propose color-blob-based COSFIRE (Combination of Shifted Filter Responses) filters to be selective for combinations of diffuse circular regions (blobs) in specific mutual spatial arrangements. Such a filter combines the responses of a certain selection of Difference-of-Gaussians filters, essentially blob detectors, of different scales, in certain channels of a color space, and at certain relative positions to each other. Its parameters are determined/learned in an automatic configuration process that analyzes the properties of a given prototype object of interest. We use these filters to compute features that are effective for the recognition of the prototype objects. We form feature vectors that we use with an SVM classifier. We evaluate the proposed method on a traffic sign (GTSRB) and a butterfly data sets. For the GTSRB data set we achieve a recognition rate of 98.94%, which is slightly higher than human performance and for the butterfly data set we achieve 89.02%. The proposed color-blob-based COSFIRE filters are very effective and outperform the contour-based COSFIRE filters. A COSFIRE filter is trainable, it can be configured with a single prototype pattern and it does not require domain knowledge.
2012
2.
Azzopardi, George; Petkov, Nicolai
Trainable COSFIRE filters for keypoint detection and pattern recognition Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, image classification, keypoint detection, object detection, segmentation, trainable filters
@article{azzopardi2013trainable,
title = {Trainable COSFIRE filters for keypoint detection and pattern recognition},
author = {George Azzopardi and Nicolai Petkov},
doi = {10.1109/TPAMI.2012.106},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
journal = { IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {35},
issue = {2},
pages = {490 - 503},
publisher = {IEEE},
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.},
keywords = {brain-inspired, image classification, keypoint detection, object detection, segmentation, trainable filters},
pubstate = {published},
tppubtype = {article}
}
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.