2019
1.
Simanjuntak, Frans; Azzopardi, George
Fusion of CNN-and COSFIRE-Based Features with Application to Gender Recognition from Face Images Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, convnets, deep learning, face analysis, trainable filters
@inproceedings{simanjuntak2019fusion,
title = {Fusion of CNN-and COSFIRE-Based Features with Application to Gender Recognition from Face Images},
author = {Frans Simanjuntak and George Azzopardi},
doi = {https://doi.org/10.1007/978-3-030-17795-9_33},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {Science and Information Conference},
pages = {444--458},
organization = {Springer},
abstract = {Convolution neural networks (CNNs) have been demonstrated to be very eective in various computer vision tasks. The main strength of such networks is that features are learned from some training data. In cases where training data is not abundant, transfer learning can be used in order to adapt features that are pre-trained from other tasks. Similarly, the COSFIRE approach is also trainable as it configures lters to be selective for features selected from training data. In this study we propose a fusion method of these two approaches and evaluate their performance on the application of gender recognition from face images. In particular, we use the pre-trained VGGFace CNN, which when used as standalone, it achieved 97.45% on the GENDER-FERET data set. With one of the proposed fusion approaches the recognition rate on the same task is improved to 98.9%, that is reducing the error rate by more than 50%. Our experiments demonstrate that COSFIRE filters can provide complementary features to CNNs, which contribute to a better performance.},
keywords = {brain-inspired, convnets, deep learning, face analysis, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
Convolution neural networks (CNNs) have been demonstrated to be very eective in various computer vision tasks. The main strength of such networks is that features are learned from some training data. In cases where training data is not abundant, transfer learning can be used in order to adapt features that are pre-trained from other tasks. Similarly, the COSFIRE approach is also trainable as it configures lters to be selective for features selected from training data. In this study we propose a fusion method of these two approaches and evaluate their performance on the application of gender recognition from face images. In particular, we use the pre-trained VGGFace CNN, which when used as standalone, it achieved 97.45% on the GENDER-FERET data set. With one of the proposed fusion approaches the recognition rate on the same task is improved to 98.9%, that is reducing the error rate by more than 50%. Our experiments demonstrate that COSFIRE filters can provide complementary features to CNNs, which contribute to a better performance.
2.
Demajo, Lara Marie; Guillaumier, Kristian; Azzopardi, George
Age group recognition from face images using a fusion of CNN-and COSFIRE-based features Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, convnets, deep learning, face analysis, trainable filters
@inproceedings{demajo2019age,
title = {Age group recognition from face images using a fusion of CNN-and COSFIRE-based features},
author = {Lara Marie Demajo and Kristian Guillaumier and George Azzopardi},
doi = {https://doi.org/10.1145/3309772.3309784},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {Proceedings of the 2nd International Conference on Applications of Intelligent Systems},
pages = {1--6},
abstract = {Automatic age group classification is the ability of an algorithm to classify face images into predetermined age groups. It is an important task due to its numerous applications such as monitoring, biometrics and commercial profiling. In this work we propose a fusion technique that combines CNN- and COSFIRE-based features for the recognition of age groups from face images. Both CNN and COSFIRE are trainable approaches that have been demonstrated to be effective in various computer vision applications. As to CNN, we use the pre-trained VGG-Face architecture and for COSFIRE we configure new COSFIRE filters from training data. Since recent literature suggests that CNNs deliver the highest accuracy rates within such problems, the hypothesis which we want to investigate in this work is whether combining CNN and COSFIRE approaches together will improve results. The proposed fusion technique using stacked Support Vector Machine (SVM) classifiers, and trained and tested with the FERET data set images has shown that, indeed, CNN- and COSFIRE-based features are complimentary as their combination reduces the error rate by more than 25%.},
keywords = {brain-inspired, convnets, deep learning, face analysis, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
Automatic age group classification is the ability of an algorithm to classify face images into predetermined age groups. It is an important task due to its numerous applications such as monitoring, biometrics and commercial profiling. In this work we propose a fusion technique that combines CNN- and COSFIRE-based features for the recognition of age groups from face images. Both CNN and COSFIRE are trainable approaches that have been demonstrated to be effective in various computer vision applications. As to CNN, we use the pre-trained VGG-Face architecture and for COSFIRE we configure new COSFIRE filters from training data. Since recent literature suggests that CNNs deliver the highest accuracy rates within such problems, the hypothesis which we want to investigate in this work is whether combining CNN and COSFIRE approaches together will improve results. The proposed fusion technique using stacked Support Vector Machine (SVM) classifiers, and trained and tested with the FERET data set images has shown that, indeed, CNN- and COSFIRE-based features are complimentary as their combination reduces the error rate by more than 25%.
2018
3.
Bonnici, Alexandra; Bugeja, Dorian; Azzopardi, George
Vectorisation of sketches with shadows and shading using COSFIRE filters Inproceedings
Links | BibTeX | Altmetric | Tags: brain-inspired, pattern recognition, trainable filters
@inproceedings{bonnici2018vectorisation,
title = {Vectorisation of sketches with shadows and shading using COSFIRE filters},
author = {Alexandra Bonnici and Dorian Bugeja and George Azzopardi},
doi = {https://doi.org/10.1145/3209280.3209525},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Proceedings of the ACM Symposium on Document Engineering 2018},
pages = {1--10},
keywords = {brain-inspired, pattern recognition, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
4.
Bonnici, Alexandra; Abela, Julian; Zammit, Nicholas; Azzopardi, George
Automatic ornament localisation, recognition and expression from music sheets Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, pattern recognition, trainable filters
@inproceedings{bonnici2018automatic,
title = {Automatic ornament localisation, recognition and expression from music sheets},
author = {Alexandra Bonnici and Julian Abela and Nicholas Zammit and George Azzopardi},
doi = {10.1145/3209280.3209536},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Proceedings of the ACM Symposium on Document Engineering 2018},
pages = {1--11},
abstract = {Musical notation is a means of passing on performance instructions with fidelity to others. Composers, however, often introduced embellishments to the music they performed notating these embellishments with symbols next to the relevant notes. In time, these symbols, known as ornaments, and their interpretation became standardized such that there are acceptable ways of interpreting an ornament. Although music books may contain footnotes which express the ornament in full notation, these remain cumbersome to read. Ideally, a music student will have the possibility of selecting ornamented notes and express them as full notation. The student should also have the possibility to collapse the expressed ornament back to its symbolic representation, giving the student the possibility of also becoming familiar with playing from the ornamented score. In this paper, we propose a complete pipeline that achieves this goal. We compare the use of COSFIRE and template matching for optical music recognition to identify and extract musical content from the score. We then express the score using MusicXML and design a simple user interface which allows the user to select ornamented notes, view their expressed notation and decide whether they want to retain the expressed notation, modify it, or revert to the symbolic representation of the ornament. The performance results that we achieve indicate the effectiveness of our proposed approach.},
keywords = {brain-inspired, pattern recognition, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
Musical notation is a means of passing on performance instructions with fidelity to others. Composers, however, often introduced embellishments to the music they performed notating these embellishments with symbols next to the relevant notes. In time, these symbols, known as ornaments, and their interpretation became standardized such that there are acceptable ways of interpreting an ornament. Although music books may contain footnotes which express the ornament in full notation, these remain cumbersome to read. Ideally, a music student will have the possibility of selecting ornamented notes and express them as full notation. The student should also have the possibility to collapse the expressed ornament back to its symbolic representation, giving the student the possibility of also becoming familiar with playing from the ornamented score. In this paper, we propose a complete pipeline that achieves this goal. We compare the use of COSFIRE and template matching for optical music recognition to identify and extract musical content from the score. We then express the score using MusicXML and design a simple user interface which allows the user to select ornamented notes, view their expressed notation and decide whether they want to retain the expressed notation, modify it, or revert to the symbolic representation of the ornament. The performance results that we achieve indicate the effectiveness of our proposed approach.
5.
Azzopardi, George; Foggia, Pasquale; Greco, Antonio; Saggese, Alessia; Vento, Mario
Gender recognition from face images using trainable shape and color features Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, face analysis, trainable filters
@inproceedings{azzopardi2018gender,
title = {Gender recognition from face images using trainable shape and color features},
author = {George Azzopardi and Pasquale Foggia and Antonio Greco and Alessia Saggese and Mario Vento},
doi = {10.1109/ICPR.2018.8545771},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {2018 24th International Conference on Pattern Recognition (ICPR)},
pages = {1983-1988},
organization = {IEEE},
abstract = {Gender recognition from face images is an important application and it is still an open computer vision problem, even though it is something trivial from the human visual system. Variations in pose, lighting, and expression are few of the problems that make such an application challenging for a computer system. Neurophysiological studies demonstrate that the human brain is able to distinguish men and women also in absence of external cues, by analyzing the shape of specific parts of the face. In this paper, we describe an automatic procedure that combines trainable shape and color features for gender classification. In particular the proposed method fuses edge-based and color-blob-based features by means of trainable COSFIRE filters. The former types of feature are able to extract information about the shape of a face whereas the latter extract information about shades of colors in different parts of the face. We use these two sets of features to create a stacked classification SVM model and demonstrate its effectiveness on the GENDER-COLOR-FERET dataset, where we achieve an accuracy of 96.4%.},
keywords = {brain-inspired, face analysis, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
Gender recognition from face images is an important application and it is still an open computer vision problem, even though it is something trivial from the human visual system. Variations in pose, lighting, and expression are few of the problems that make such an application challenging for a computer system. Neurophysiological studies demonstrate that the human brain is able to distinguish men and women also in absence of external cues, by analyzing the shape of specific parts of the face. In this paper, we describe an automatic procedure that combines trainable shape and color features for gender classification. In particular the proposed method fuses edge-based and color-blob-based features by means of trainable COSFIRE filters. The former types of feature are able to extract information about the shape of a face whereas the latter extract information about shades of colors in different parts of the face. We use these two sets of features to create a stacked classification SVM model and demonstrate its effectiveness on the GENDER-COLOR-FERET dataset, where we achieve an accuracy of 96.4%.
6.
Apap, Adrian; Robles, Laura Fernandez; Azzopardi, George
Person Identification with Retinal Fundus Biometric Analysis Using COSFIRE Filters Inproceedings
Links | BibTeX | Altmetric | Tags: biometrics, brain-inspired, trainable filters
@inproceedings{apap2018retinal,
title = {Person Identification with Retinal Fundus Biometric Analysis Using COSFIRE Filters},
author = {Adrian Apap and Laura Fernandez Robles and George Azzopardi},
doi = {10.3233/978-1-61499-929-4-10},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Proceedings of the first international APPIS conference, Gran Canaria, Spain},
volume = {310},
pages = {10-18},
keywords = {biometrics, brain-inspired, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
2017
7.
Strisciuglio, Nicola; Azzopardi, George; Petkov, Nicolai
Detection of curved lines with B-COSFIRE filters: A case study on crack delineation Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, contour detection, trainable filters
@inproceedings{strisciuglio2017detection,
title = {Detection of curved lines with B-COSFIRE filters: A case study on crack delineation},
author = {Nicola Strisciuglio and George Azzopardi and Nicolai Petkov},
doi = {https://doi.org/10.1007/978-3-319-64689-3_9},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {International Conference on Computer Analysis of Images and Patterns},
pages = {108--120},
organization = {Springer, Cham},
abstract = {The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis thresholding and morphological closing. We demonstrate its effectiveness on a data set of noisy images with cracked pavements, where we achieve state-of-the-art results (F-measure = 0.865). The proposed method can be employed in any computer vision methodology that requires the delineation of curvilinear and elongated structures.},
keywords = {brain-inspired, contour detection, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis thresholding and morphological closing. We demonstrate its effectiveness on a data set of noisy images with cracked pavements, where we achieve state-of-the-art results (F-measure = 0.865). The proposed method can be employed in any computer vision methodology that requires the delineation of curvilinear and elongated structures.
2016
8.
Azzopardi, George; Greco, Antonio; Vento, Mario
Gender recognition from face images with trainable COSFIRE filters Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, face analysis, trainable filters
@inproceedings{azzopardi2016genderb,
title = {Gender recognition from face images with trainable COSFIRE filters},
author = {George Azzopardi and Antonio Greco and Mario Vento},
doi = {10.1109/AVSS.2016.7738068},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
booktitle = {13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},
pages = {235--241},
organization = {IEEE},
abstract = {Gender recognition from face images is an important application in the fields of security, retail advertising and marketing. We propose a novel descriptor based on COSFIRE filters for gender recognition. A COSFIRE filter is trainable, in that its selectivity is determined in an automatic configuration process that analyses a given prototype pattern of interest. We demonstrate the effectiveness of the proposed approach on a new dataset called GENDER-FERET with 474 training and 472 test samples and achieve an accuracy rate of 93.7%. It also outperforms an approach that relies on handcrafted features and an ensemble of classifiers. Furthermore, we perform another experiment by using the images of the Labeled Faces in the Wild (LFW) dataset to train our classifier and the test images of the GENDER-FERET dataset for evaluation. This experiment demonstrates the generalization ability of the proposed approach and it also outperforms two commercial libraries, namely Face++ and Luxand.},
keywords = {brain-inspired, face analysis, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
Gender recognition from face images is an important application in the fields of security, retail advertising and marketing. We propose a novel descriptor based on COSFIRE filters for gender recognition. A COSFIRE filter is trainable, in that its selectivity is determined in an automatic configuration process that analyses a given prototype pattern of interest. We demonstrate the effectiveness of the proposed approach on a new dataset called GENDER-FERET with 474 training and 472 test samples and achieve an accuracy rate of 93.7%. It also outperforms an approach that relies on handcrafted features and an ensemble of classifiers. Furthermore, we perform another experiment by using the images of the Labeled Faces in the Wild (LFW) dataset to train our classifier and the test images of the GENDER-FERET dataset for evaluation. This experiment demonstrates the generalization ability of the proposed approach and it also outperforms two commercial libraries, namely Face++ and Luxand.
9.
Azzopardi, George; Robles, Laura Fernandez; Alegre, Enrique; Petkov, Nicolai
Increased Generalization Capability of Trainable COSFIRE Filters with Application to Machine Vision Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, machine vision, trainable filters
@inproceedings{Azzopardi2016,
title = {Increased Generalization Capability of Trainable COSFIRE Filters with Application to Machine Vision},
author = {George Azzopardi and Laura Fernandez Robles and Enrique Alegre and Nicolai Petkov},
doi = {10.1109/ICPR.2016.7900152},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
booktitle = {23rd International Conference on Pattern Recognition (ICPR)},
publisher = {IEEE},
abstract = {The recently proposed trainable COSFIRE filters are highly effective in a wide range of computer vision applications, including object recognition, image classification, contour detection and retinal vessel segmentation. A COSFIRE filter is selective for a collection of contour parts in a certain spatial arrangement. These contour parts and their spatial arrangement are determined in an automatic configuration procedure from a single user-specified pattern of interest. The traditional configuration, however, does not guarantee the selection of the most distinctive contour parts. We propose a genetic algorithm-based optimization step in the configuration of COSFIRE filters that determines the minimum subset of contour parts that best characterize the pattern of interest. We use a public dataset of images of an edge milling head machine equipped with multiple cutting tools to demonstrate the effectiveness of the proposed optimization step for the detection and localization of such tools. The optimization process that we propose yields COSFIRE filters with substantially higher generalization capability. With an average of only six COSFIRE filters we achieve high precision P and recall R rates (P = 91.99%; R = 96.22%). This outperforms the original COSFIRE filter approach (without optimization) mostly in terms of recall. The proposed optimization procedure increases the efficiency of COSFIRE filters with little effect on the selectivity.},
keywords = {brain-inspired, machine vision, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
The recently proposed trainable COSFIRE filters are highly effective in a wide range of computer vision applications, including object recognition, image classification, contour detection and retinal vessel segmentation. A COSFIRE filter is selective for a collection of contour parts in a certain spatial arrangement. These contour parts and their spatial arrangement are determined in an automatic configuration procedure from a single user-specified pattern of interest. The traditional configuration, however, does not guarantee the selection of the most distinctive contour parts. We propose a genetic algorithm-based optimization step in the configuration of COSFIRE filters that determines the minimum subset of contour parts that best characterize the pattern of interest. We use a public dataset of images of an edge milling head machine equipped with multiple cutting tools to demonstrate the effectiveness of the proposed optimization step for the detection and localization of such tools. The optimization process that we propose yields COSFIRE filters with substantially higher generalization capability. With an average of only six COSFIRE filters we achieve high precision P and recall R rates (P = 91.99%; R = 96.22%). This outperforms the original COSFIRE filter approach (without optimization) mostly in terms of recall. The proposed optimization procedure increases the efficiency of COSFIRE filters with little effect on the selectivity.
2015
10.
Guo, Jiapan; Shi, Chenyu; Azzopardi, George; Petkov, Nicolai
Recognition of architectural and electrical symbols by COSFIRE filters with inhibition Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: object detection, pattern recognition, trainable filters
@inproceedings{guo2015recognition,
title = {Recognition of architectural and electrical symbols by COSFIRE filters with inhibition},
author = {Jiapan Guo and Chenyu Shi and George Azzopardi and Nicolai Petkov},
doi = {10.1007/978-3-319-23117-4_30},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {International Conference on Computer Analysis of Images and Patterns},
pages = {348--358},
organization = {Springer, Cham},
abstract = {The automatic recognition of symbols can be used to automatically convert scanned drawings into digital representations compatible with computer aided design software. We propose a novel approach to automatically recognize architectural and electrical symbols. The proposed method extends the existing trainable COSFIRE approach by adding an inhibition mechanism that is inspired by shape-selective TEO neurons in visual cortex. A COSFIRE filter with inhibition takes as input excitatory and inhibitory responses from line and edge detectors. The type (excitatory or inhibitory) and the spatial arrangement of low level features are determined in an automatic configuration step that analyzes two types of prototype pattern called positive and negative. Excitatory features are extracted from a positive pattern and inhibitory features are extracted from one or more negative patterns. In our experiments we use four subsets of images with different noise levels from the Graphics Recognition data set (GREC 2011) and demonstrate that the inhibition mechanism that we introduce improves the effectiveness of recognition substantially.},
keywords = {object detection, pattern recognition, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
The automatic recognition of symbols can be used to automatically convert scanned drawings into digital representations compatible with computer aided design software. We propose a novel approach to automatically recognize architectural and electrical symbols. The proposed method extends the existing trainable COSFIRE approach by adding an inhibition mechanism that is inspired by shape-selective TEO neurons in visual cortex. A COSFIRE filter with inhibition takes as input excitatory and inhibitory responses from line and edge detectors. The type (excitatory or inhibitory) and the spatial arrangement of low level features are determined in an automatic configuration step that analyzes two types of prototype pattern called positive and negative. Excitatory features are extracted from a positive pattern and inhibitory features are extracted from one or more negative patterns. In our experiments we use four subsets of images with different noise levels from the Graphics Recognition data set (GREC 2011) and demonstrate that the inhibition mechanism that we introduce improves the effectiveness of recognition substantially.
11.
Strisciuglio, Nicola; Azzopardi, George; Vento, Mario; Petkov, Nicolai
Multiscale blood vessel delineation using B-COSFIRE filters Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, medical image analysis, segmentation, trainable filters
@inproceedings{strisciuglio2015multiscale,
title = {Multiscale blood vessel delineation using B-COSFIRE filters},
author = {Nicola Strisciuglio and George Azzopardi and Mario Vento and Nicolai Petkov},
doi = {10.1007/978-3-319-23117-4_26},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {International Conference on Computer Analysis of Images and Patterns},
pages = {300--312},
organization = {Springer, Cham},
abstract = {We propose a delineation algorithm that deals with bar-like structures of different thickness. Detection of linear structures is applicable to several fields ranging from medical images for segmentation of vessels to aerial images for delineation of roads or rivers. The proposed method is suited for any delineation problem and employs a set of B-COSFIRE filters selective for lines and line-endings of different thickness. We determine the most effective filters for the application at hand by Generalized Matrix Learning Vector Quantization (GMLVQ) algorithm. We demonstrate the effectiveness of the proposed method by applying it to the task of vessel segmentation in retinal images. We perform experiments on two benchmark data sets, namely DRIVE and STARE. The experimental results show that the proposed delineation algorithm is highly effective and efficient. It can be considered as a general framework for a delineation task in various applications.},
keywords = {brain-inspired, medical image analysis, segmentation, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a delineation algorithm that deals with bar-like structures of different thickness. Detection of linear structures is applicable to several fields ranging from medical images for segmentation of vessels to aerial images for delineation of roads or rivers. The proposed method is suited for any delineation problem and employs a set of B-COSFIRE filters selective for lines and line-endings of different thickness. We determine the most effective filters for the application at hand by Generalized Matrix Learning Vector Quantization (GMLVQ) algorithm. We demonstrate the effectiveness of the proposed method by applying it to the task of vessel segmentation in retinal images. We perform experiments on two benchmark data sets, namely DRIVE and STARE. The experimental results show that the proposed delineation algorithm is highly effective and efficient. It can be considered as a general framework for a delineation task in various applications.
12.
Neocleous, Andreas; Azzopardi, George; Schizas, Christos N; Petkov, Nicolai
Filter-Based Approach for Ornamentation Detection and Recognition in Singing Folk Music Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: signal processing, time-series, trainable filters
@inproceedings{neocleous2015filter,
title = {Filter-Based Approach for Ornamentation Detection and Recognition in Singing Folk Music},
author = {Andreas Neocleous and George Azzopardi and Christos N Schizas and Nicolai Petkov},
doi = {10.1007/978-3-319-23192-1_47},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {International Conference on Computer Analysis of Images and Patterns},
pages = {558--569},
organization = {Springer International Publishing},
abstract = {Ornamentations in music play a significant role for the emotion whi1ch a performer or a composer aims to create. The automated identification of ornamentations enhances the understanding of music, which can be used as a feature for tasks such as performer identification or mood classification. Existing methods rely on a pre-processing step that performs note segmentation. We propose an alternative method by adapting the existing two-dimensional COSFIRE filter approach to one-dimension (1D) for the automatic identification of ornamentations in monophonic folk songs. We construct a set of 1D COSFIRE filters that are selective for the 12 notes of the Western music theory. The response of a 1D COSFIRE filter is computed as the geometric mean of the differences between the fundamental frequency values in a local neighbourhood and the preferred values at the corresponding positions. We apply the proposed 1D COSFIRE filters to the pitch tracks of a song at every position along the entire signal, which in turn give response values in the range [0,1]. The 1D COSFIRE filters that we propose are effective to recognize meaningful musical information which can be transformed into symbolic representations and used for further analysis. We demonstrate the effectiveness of the proposed methodology in a new data set that we introduce, which comprises five monophonic Cypriot folk tunes consisting of 428 ornamentations. The proposed method is effective for the detection and recognition of ornamentations in singing folk music.},
keywords = {signal processing, time-series, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
Ornamentations in music play a significant role for the emotion whi1ch a performer or a composer aims to create. The automated identification of ornamentations enhances the understanding of music, which can be used as a feature for tasks such as performer identification or mood classification. Existing methods rely on a pre-processing step that performs note segmentation. We propose an alternative method by adapting the existing two-dimensional COSFIRE filter approach to one-dimension (1D) for the automatic identification of ornamentations in monophonic folk songs. We construct a set of 1D COSFIRE filters that are selective for the 12 notes of the Western music theory. The response of a 1D COSFIRE filter is computed as the geometric mean of the differences between the fundamental frequency values in a local neighbourhood and the preferred values at the corresponding positions. We apply the proposed 1D COSFIRE filters to the pitch tracks of a song at every position along the entire signal, which in turn give response values in the range [0,1]. The 1D COSFIRE filters that we propose are effective to recognize meaningful musical information which can be transformed into symbolic representations and used for further analysis. We demonstrate the effectiveness of the proposed methodology in a new data set that we introduce, which comprises five monophonic Cypriot folk tunes consisting of 428 ornamentations. The proposed method is effective for the detection and recognition of ornamentations in singing folk music.
13.
Fernández-Robles, Laura; Azzopardi, George; Alegre, Enrique; Petkov, Nicolai
Cutting Edge Localisation in an Edge Profile Milling Head Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: machine vision, trainable filters
@inproceedings{Fern\'{a}ndez-Robles2015,
title = {Cutting Edge Localisation in an Edge Profile Milling Head},
author = {Laura Fern\'{a}ndez-Robles and George Azzopardi and Enrique Alegre and Nicolai Petkov},
doi = {10.1007/978-3-319-23117-4_29},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Proceedings Part II of CAIP2015, LNCS 9257
},
pages = {336-347},
publisher = {Springer},
abstract = {Wear evaluation of cutting tools is a key issue for prolonging their lifetime and ensuring high quality of products. In this paper, we present a method for the effective localisation of cutting edges of inserts in digital images of an edge profile milling head. We introduce a new image data set of 144 images of an edge milling head that contains 30 inserts. We use a circular Hough transform to detect the screws that fasten the inserts. In a cropped area around a detected screw, we use Canny’s edge detection algorithm and Standard Hough Transform to localise line segments that characterise insert edges. We use this information and the geometry of the insert to identify which of these line segments is the cutting edge. The output of our algorithm is a set of quadrilateral regions around the identified cutting edges. These regions can then be used as input to other algorithms for the quality assessment of the cutting edges. Our results show that the proposed method is very effective for the localisation of the cutting edges of inserts in an edge profile milling machine.},
keywords = {machine vision, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
Wear evaluation of cutting tools is a key issue for prolonging their lifetime and ensuring high quality of products. In this paper, we present a method for the effective localisation of cutting edges of inserts in digital images of an edge profile milling head. We introduce a new image data set of 144 images of an edge milling head that contains 30 inserts. We use a circular Hough transform to detect the screws that fasten the inserts. In a cropped area around a detected screw, we use Canny’s edge detection algorithm and Standard Hough Transform to localise line segments that characterise insert edges. We use this information and the geometry of the insert to identify which of these line segments is the cutting edge. The output of our algorithm is a set of quadrilateral regions around the identified cutting edges. These regions can then be used as input to other algorithms for the quality assessment of the cutting edges. Our results show that the proposed method is very effective for the localisation of the cutting edges of inserts in an edge profile milling machine.
2013
14.
Azzopardi, George; Petkov, Nicolai
A shape descriptor based on trainable COSFIRE filters for the recognition of handwritten digits Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, image classification, trainable filters
@inproceedings{azzopardi2013shape,
title = {A shape descriptor based on trainable COSFIRE filters for the recognition of handwritten digits},
author = {George Azzopardi and Nicolai Petkov},
doi = {10.1007/978-3-642-40246-3_2},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
booktitle = {International Conference on Computer Analysis of Images and Patterns},
pages = {9--16},
organization = {Springer Berlin Heidelberg},
abstract = {The recognition of handwritten digits is an application which has been used as a benchmark for comparing shape recognition methods. We train COSFIRE filters to be selective for different parts of handwritten digits. In analogy with the neurophysiological concept of population coding we use the responses of multiple COSFIRE filters as a shape descriptor of a handwritten digit. We demonstrate the effectiveness of the proposed approach on two data sets of handwritten digits: Western Arabic (MNIST) and Farsi for which we achieve high recognition rates of 99.52% and 99.33%, respectively. COSFIRE filters are conceptually simple, easy to implement and they are versatile trainable feature detectors. The shape descriptor that we propose is highly effective to the automatic recognition of handwritten digits.},
keywords = {brain-inspired, image classification, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
The recognition of handwritten digits is an application which has been used as a benchmark for comparing shape recognition methods. We train COSFIRE filters to be selective for different parts of handwritten digits. In analogy with the neurophysiological concept of population coding we use the responses of multiple COSFIRE filters as a shape descriptor of a handwritten digit. We demonstrate the effectiveness of the proposed approach on two data sets of handwritten digits: Western Arabic (MNIST) and Farsi for which we achieve high recognition rates of 99.52% and 99.33%, respectively. COSFIRE filters are conceptually simple, easy to implement and they are versatile trainable feature detectors. The shape descriptor that we propose is highly effective to the automatic recognition of handwritten digits.
15.
Azzopardi, George; Petkov, Nicolai
COSFIRE: A Brain-Inspired Approach to Visual Pattern Recognition Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: image classification, trainable filters
@inproceedings{azzopardi2013cosfirec,
title = {COSFIRE: A Brain-Inspired Approach to Visual Pattern Recognition},
author = {George Azzopardi and Nicolai Petkov},
doi = {10.1007/978-3-319-12084-3_7},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
booktitle = {International Workshop on Brain-Inspired Computing},
pages = {76--87},
organization = {Springer, Cham},
abstract = {The primate visual system has an impressive ability to generalize and to discriminate between numerous objects and it is robust to many geometrical transformations as well as lighting conditions. The study of the visual system has been an active reasearch field in neuropysiology for more than half a century. The construction of computational models of visual neurons can help us gain insight in the processing of information in visual cortex which we can use to provide more robust solutions to computer vision applications. Here, we demonstrate how inspiration from the functions of shape-selective V4 neurons can be used to design trainable filters for visual pattern recognition. We call this approach COSFIRE, which stands for Combination of Shifted Filter Responses. We illustrate how a COSFIRE filter can be configured to be selective for the spatial arrangement of lines and/or edges that form the shape of a given prototype pattern. Finally, we demonstrate the effectiveness of the COSFIRE approach in three applications: the detection of vascular bifurcations in retinal fundus images, the localization and recognition of traffic signs in complex scenes and the recognition of handwritten digits. This work is a further step in understanding how visual information is processed in the brain and how information on pixel intensities is converted into information about objects. We demonstrate how this understanding can be used for the design of effective computer vision algorithms.},
keywords = {image classification, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
The primate visual system has an impressive ability to generalize and to discriminate between numerous objects and it is robust to many geometrical transformations as well as lighting conditions. The study of the visual system has been an active reasearch field in neuropysiology for more than half a century. The construction of computational models of visual neurons can help us gain insight in the processing of information in visual cortex which we can use to provide more robust solutions to computer vision applications. Here, we demonstrate how inspiration from the functions of shape-selective V4 neurons can be used to design trainable filters for visual pattern recognition. We call this approach COSFIRE, which stands for Combination of Shifted Filter Responses. We illustrate how a COSFIRE filter can be configured to be selective for the spatial arrangement of lines and/or edges that form the shape of a given prototype pattern. Finally, we demonstrate the effectiveness of the COSFIRE approach in three applications: the detection of vascular bifurcations in retinal fundus images, the localization and recognition of traffic signs in complex scenes and the recognition of handwritten digits. This work is a further step in understanding how visual information is processed in the brain and how information on pixel intensities is converted into information about objects. We demonstrate how this understanding can be used for the design of effective computer vision algorithms.
2012
16.
Azzopardi, George; Petkov, Nicolai
Detection of retinal vascular bifurcations by rotation-and scale-invariant COSFIRE filters Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, medical image analysis, trainable filters
@inproceedings{azzopardi2012detection,
title = {Detection of retinal vascular bifurcations by rotation-and scale-invariant COSFIRE filters},
author = {George Azzopardi and Nicolai Petkov},
doi = {10.1007/978-3-642-31298-4_43},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
booktitle = {International Conference Image Analysis and Recognition},
pages = {363--371},
organization = {Springer, Berlin, Heidelberg},
abstract = {The analysis of the vascular tree in retinal fundus images is important for identifying risks of various cardiovascular diseases. We propose trainable COSFIRE (Combination Of Shifted FIlter REsponses) filters to detect vascular bifurcations. A COSFIRE filter is automatically configured to be selective for a bifurcation that is specified by a user from a training image. The configuration selects given channels of a bank of Gabor filters and determines certain blur and shift parameters. A COSFIRE filter response is computed as the product of the blurred and shifted responses of the selected Gabor filters. The filter responds to bifurcations that are similar to the one used for its configuration. The proposed filters achieve invariance to rotation and scale. With only five COSFIRE filters we achieve a recall of 98.77% at a precision of 95.32% on a data set of 40 binary fundus images (from DRIVE), containing more than 5000 bifurcations.},
keywords = {brain-inspired, medical image analysis, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
The analysis of the vascular tree in retinal fundus images is important for identifying risks of various cardiovascular diseases. We propose trainable COSFIRE (Combination Of Shifted FIlter REsponses) filters to detect vascular bifurcations. A COSFIRE filter is automatically configured to be selective for a bifurcation that is specified by a user from a training image. The configuration selects given channels of a bank of Gabor filters and determines certain blur and shift parameters. A COSFIRE filter response is computed as the product of the blurred and shifted responses of the selected Gabor filters. The filter responds to bifurcations that are similar to the one used for its configuration. The proposed filters achieve invariance to rotation and scale. With only five COSFIRE filters we achieve a recall of 98.77% at a precision of 95.32% on a data set of 40 binary fundus images (from DRIVE), containing more than 5000 bifurcations.
17.
Azzopardi, George; Petkov, Nicolai
Detection of retinal vascular bifurcations by rotation-, scale-and reflection-invariant COSFIRE filters Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, medical image analysis, trainable filters
@inproceedings{azzopardi2012detectionb,
title = {Detection of retinal vascular bifurcations by rotation-, scale-and reflection-invariant COSFIRE filters},
author = {George Azzopardi and Nicolai Petkov},
doi = {10.1109/CBMS.2012.6266338},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
booktitle = {2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)},
pages = {1--4},
organization = {IEEE},
abstract = {We propose trainable filters, which we call COSFIRE (Combination Of Shifted FIlter REsponses), and use to detect vascular bifurcations in retinal images. We configure a COSFIRE filter to be selective for a bifurcation that is specified by a user in a single-step training phase. The automatic configuration comprises the selection of channels of a bank of Gabor filters and the determination of certain blur and shift parameters. A COSFIRE filter response is computed as the geometric mean of the blurred and shifted responses of the selected Gabor filters. The proposed filters share similar properties with some shape-selective neurons in visual cortex. With only five filters we achieve a recall of 98.57% at a precision of 95.37% on the 40 binary retinal images (from DRIVE), containing more than 5000 bifurcations.},
keywords = {brain-inspired, medical image analysis, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose trainable filters, which we call COSFIRE (Combination Of Shifted FIlter REsponses), and use to detect vascular bifurcations in retinal images. We configure a COSFIRE filter to be selective for a bifurcation that is specified by a user in a single-step training phase. The automatic configuration comprises the selection of channels of a bank of Gabor filters and the determination of certain blur and shift parameters. A COSFIRE filter response is computed as the geometric mean of the blurred and shifted responses of the selected Gabor filters. The proposed filters share similar properties with some shape-selective neurons in visual cortex. With only five filters we achieve a recall of 98.57% at a precision of 95.37% on the 40 binary retinal images (from DRIVE), containing more than 5000 bifurcations.
2011
18.
Azzopardi, George; Petkov, Nicolai
Detection of retinal vascular bifurcations by trainable V4-like filters Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, medical image analysis, trainable filters
@inproceedings{azzopardi2011detection,
title = {Detection of retinal vascular bifurcations by trainable V4-like filters},
author = {George Azzopardi and Nicolai Petkov},
doi = {10.1007/978-3-642-23672-3_55},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {International Conference on Computer Analysis of Images and Patterns},
pages = {451--459},
organization = {Springer, Berlin, Heidelberg},
abstract = {The detection of vascular bifurcations in retinal fundus images is important for finding signs of various cardiovascular diseases. We propose a novel method to detect such bifurcations. Our method is implemented in trainable filters that mimic the properties of shape-selective neurons in area V4 of visual cortex. Such a filter is configured by combining given channels of a bank of Gabor filters in an AND-gate-like operation. Their selection is determined by the automatic analysis of a bifurcation feature that is specified by the user from a training image. Consequently, the filter responds to the same and similar bifurcations. With only 25 filters we achieved a correct detection rate of 98.52% at a precision rate of 95.19% on a set of 40 binary fundus images, containing more than 5000 bifurcations. In principle, all vascular bifurcations can be detected if a sufficient number of filters are configured and used.},
keywords = {brain-inspired, medical image analysis, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
The detection of vascular bifurcations in retinal fundus images is important for finding signs of various cardiovascular diseases. We propose a novel method to detect such bifurcations. Our method is implemented in trainable filters that mimic the properties of shape-selective neurons in area V4 of visual cortex. Such a filter is configured by combining given channels of a bank of Gabor filters in an AND-gate-like operation. Their selection is determined by the automatic analysis of a bifurcation feature that is specified by the user from a training image. Consequently, the filter responds to the same and similar bifurcations. With only 25 filters we achieved a correct detection rate of 98.52% at a precision rate of 95.19% on a set of 40 binary fundus images, containing more than 5000 bifurcations. In principle, all vascular bifurcations can be detected if a sufficient number of filters are configured and used.