Publications

@article{7321013,
title = {Audio Surveillance of Roads: A System for Detecting Anomalous Sounds},
author = {Pasquale Foggia and Nicolai Petkov and Alessia Saggese and Nicola Strisciuglio and Mario Vento},
doi = {10.1109/TITS.2015.2470216},
issn = {1524-9050},
year = {2016},
date = {2016-01-01},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {17},
number = {1},
pages = {279-288},
abstract = {In the last decades, several systems based on video analysis have been proposed for automatically detecting accidents on roads to ensure a quick intervention of emergency teams. However, in some situations, the visual information is not sufficient or sufficiently reliable, whereas the use of microphones and audio event detectors can significantly improve the overall reliability of surveillance systems. In this paper, we propose a novel method for detecting road accidents by analyzing audio streams to identify hazardous situations such as tire skidding and car crashes. Our method is based on a two-layer representation of an audio stream: at a low level, the system extracts a set of features that is able to capture the discriminant properties of the events of interest, and at a high level, a representation based on a bag-of-words approach is then exploited in order to detect both short and sustained events. The deployment architecture for using the system in real environments is discussed, together with an experimental analysis carried out on a data set made publicly available for benchmarking purposes. The obtained results confirm the effectiveness of the proposed approach.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Strisciuglio2016,
title = {Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters},
author = {Nicola Strisciuglio and George Azzopardi and Mario Vento and Nicolai Petkov},
url = {http://dx.doi.org/10.1007/s00138-016-0781-7},
doi = {10.1007/s00138-016-0781-7},
issn = {1432-1769},
year = {2016},
date = {2016-01-01},
journal = {Machine Vision and Applications},
volume = {27},
number = {8},
pages = {1137--1149},
abstract = {The inspection of retinal fundus images allows medical doctors to diagnose various pathologies. Computer-aided diagnosis systems can be used to assist in this process. As a first step, such systems delineate the vessel tree from the background. We propose a method for the delineation of blood vessels in retinal images that is effective for vessels of different thickness. In the proposed method, we employ a set of B-COSFIRE filters selective for vessels and vessel-endings. Such a set is determined in an automatic selection process and can adapt to different applications. We compare the performance of different selection methods based upon machine learning and information theory. The results that we achieve by performing experiments on two public benchmark data sets, namely DRIVE and STARE, demonstrate the effectiveness of the proposed approach.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{FoggiaRoads2015,
title = {Audio surveillance of roads: a system for detecting anomalous sounds},
author = {Pasquale Foggia and Nicolai Petkov and Alessia Saggese and Nicola Strisciuglio and Mario Vento},
doi = {10.1109/TITS.2015.2470216},
year = {2015},
date = {2015-11-03},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {17},
keywords = {Audio analysis and interpretation},
pubstate = {published},
tppubtype = {article}
}

@article{Foggia2015,
title = {Reliable Detection of Audio Events in Highly Noisy Environments},
author = {Pasquale Foggia and Nicolai Petkov and Alessia Saggese and Nicola Strisciuglio and Mario Vento},
url = {http://www.sciencedirect.com/science/article/pii/S0167865515001981},
issn = {0167-8655},
year = {2015},
date = {2015-07-09},
journal = {Pattern Recognition Letters},
keywords = {Audio analysis and interpretation, Classification Paradigms},
pubstate = {published},
tppubtype = {article}
}

@article{Azzopardi2014,
title = {Trainable COSFIRE filters for vessel delineation with application to retinal images},
author = {George Azzopardi and Nicola Strisciuglio and Mario Vento and Nicolai Petkov},
url = {http://www.sciencedirect.com/science/article/pii/S1361841514001364},
issn = {1361-8415},
year = {2015},
date = {2015-01-14},
journal = {Medical Image Analysis},
volume = {19},
number = {1},
pages = {46–57},
abstract = {Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis.

We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding.

The results that we achieve on three publicly available data sets (DRIVE: Se = 0.7655, Sp = 0.9704; STARE: Se = 0.7716, Sp = 0.9701; CHASE_DB1: Se = 0.7585, Sp = 0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.},
keywords = {Image analysis and recognition, Medical image analysis},
pubstate = {published},
tppubtype = {article}
}