Delineation of line patterns in images using B-COSFIRE filters

Delineation of line patterns in images is a basic step required in various applications such as blood vessel detection in medical images, segmentation of rivers or roads in aerial images, detection of cracks in walls or pavements, etc. In this paper we present trainable B-COSFIRE filters, which are a model of some neurons in area V1 of the primary visual cortex, and apply it to the delineation of line patterns in different kinds of images. B-COSFIRE filters are trainable as their selectivity is determined in an automatic configuration process given a prototype pattern of interest. They are configurable to detect any preferred line structure (e.g. segments, corners, cross-overs, etc.), so usable for automatic data representation learning. We carried out experiments on two data sets, namely a line-network data set from INRIA and a data set of retinal fundus images named IOSTAR. The results that we achieved confirm the robustness of the proposed approach and its effectiveness in the delineation of line structures in different kinds of images.Comment: International Work Conference on Bioinspired Intelligence, July 10-13, 201

Learning sound representations using trainable COPE feature extractors

Sound analysis research has mainly been focused on speech and music processing. The deployed methodologies are not suitable for analysis of sounds with varying background noise, in many cases with very low signal-to-noise ratio (SNR). In this paper, we present a method for the detection of patterns of interest in audio signals. We propose novel trainable feature extractors, which we call COPE (Combination of Peaks of Energy). The structure of a COPE feature extractor is determined using a single prototype sound pattern in an automatic configuration process, which is a type of representation learning. We construct a set of COPE feature extractors, configured on a number of training patterns. Then we take their responses to build feature vectors that we use in combination with a classifier to detect and classify patterns of interest in audio signals. We carried out experiments on four public data sets: MIVIA audio events, MIVIA road events, ESC-10 and TU Dortmund data sets. The results that we achieved (recognition rate equal to 91.71% on the MIVIA audio events, 94% on the MIVIA road events, 81.25% on the ESC-10 and 94.27% on the TU Dortmund) demonstrate the effectiveness of the proposed method and are higher than the ones obtained by other existing approaches. The COPE feature extractors have high robustness to variations of SNR. Real-time performance is achieved even when the value of a large number of features is computed.Comment: Accepted for publication in Pattern Recognitio

Learning audio and image representations with bio-inspired trainable feature extractors

Recent advancements in pattern recognition and signal processing concern the automatic learning of data representations from labeled training samples. Typical approaches are based on deep learning and convolutional neural networks, which require large amount of labeled training samples. In this work, we propose novel feature extractors that can be used to learn the representation of single prototype samples in an automatic configuration process. We employ the proposed feature extractors in applications of audio and image processing, and show their effectiveness on benchmark data sets.Comment: Accepted for publication in the journal "Eleectronic Letters on Computer Vision and Image Understanding

Bio-inspired algorithms for pattern recognition in audio and image processing

In deze thesis doen wij onderzoek naar het ontwerpen van nieuwe patroonherkenningssystemen op basis van vernieuwende filters welke getraind kunnen worden. De thesis richt zich op toepassingen op het gebied van intelligente audiosurveillance en de analyse van medische afbeeldingen. In het eerste deel van dit werk stellen wij een systeem voor dat abnormaliteiten in geluidsfragmenten kan detecteren. Voorbeelden van abnormaliteiten zijn het breken van glas, geweerschoten, schreeuwende mensen, slippende banden en autobotsingen. Het beoogde systeem is gebaseerd op het gebruik van CoPE filters. Deze filters kunnen nieuwe abnormaliteiten herkennen aan de hand van voorbeelden, vergelijkbaar met de manier waaropmensen nieuwe concepten leren. Een dergelijk systeem kan toegepast worden om bestaande surveillance systemen te verbeteren. Wij hebben een strategie ontworpen voor de uitrol van microfoons op plekken waar cameras niet toegestaan zijn (e.g. publieke toiletten) of waar het plaatsen van cameras te duur is (e.g. grote parkeerplaatsen). Het systeem kan de politie helpen bij het opsporen van criminele activiteiten of bij het herkennen van gevaarlijk situaties.In het tweede gedeelte van de thesis gebruiken wij COSFIRE filters om automatisch bloedvaten te segmenteren in afbeeldingen van netvliezen. De handmatige analyse van netvliesafbeeldingen is tijdrovend en duur. Een geautomatiseerd systeem maakt bevolkingsonderzoek mogelijk en helpt dokters bij het herkennen van medische aandoeningen, zoals diabetische retinopathie, in een vroeg stadium. Daarnaast brengt het systeem lage medische onkosten met zich mee. Dit werk biedt nieuwe methoden voor patroonherkenning in geluidsfragmenten en voor het verwerken van afbeeldingen, en is toepasbaar in de praktijk.In this thesis, we investigate the construction of pattern recognition systems that are based on the use of novel trainable filters. The thesis addresses two important applications in the fields of intelligent audio surveillance and medical image analysis.In the first part of the work, we propose a system for the detection of abnormal audio events, such as glass breaking, gun shots, screams, tire skidding and car accidents. The proposed system is based on CoPE filters, which make it capable to learn to detect new events by showing examples, in the same way humans learn new concepts. Such a system can be applied to improve actual surveillance systems for public or private security. We designed a deployment strategy to install microphones in places where cameras are not allowed (e.g. public toilet) or too expensive to be installed (e.g. huge parking areas). This would help the police to detect criminal acts or dangerous situations. In the second part of the thesis, we apply COSFIRE filters to the automatic segmentation of blood vessels in retinal images. The manual analysis of retinal images is time-consuming and expensive. An automatic system can allow population screening and help doctors to recognize medical conditions such as diabetic retinopathy in an early stage, keeping at the same time low the costs of medical care. This work provides innovative tools for pattern recognition in audio and image processing and contributes to the research trend of constructing systems for real-world application