Concetto Spampinato

Automatic fish classification for underwater species behavior understanding

The aim of this work is to propose an automatic fish classification system that operates in the natural underwater environment to assist marine biologists in understanding subehavior. Fish classification is performed by combining two types of features: 1) Texture features extracted by using statistical moments of the gray-level histogram, spatial Gabor filtering and properties of the co-occurrence matrix and 2) Shape Features extracted by using the Curvature Scale Space transform and the histogram of Fourier descriptors of boundaries. An affine transformation is also applied to the acquired images to represent fish in 3D by multiple views for the feature extraction. The system was tested on a database containing 360 images of ten different species achieving as average correct rate of about 92%. Then, fish trajectories extracted using the proposed fish classification combined with a tracking system, are analyzed in order to understand anomalous behavior. In detail, the tracking layer computer fish trajectories, the classification layer associates trajectories to fish species and then by clustering these trajectories we are able to detect unusual fish behaviors to be further investigated by marine biologists.

Combining literature text mining with microarray data: advances for system biology modeling

A huge amount of important biomedical information is hidden in the bulk of research articles in biomedical fields. At the same time, the publication of databases of biological information and of experimental datasets generated by high-throughput methods is in great expansion, and a wealth of annotated gene databases, chemical, genomic (including microarray datasets), clinical and other types of data repositories are now available on the Web. Thus a current challenge of bioinformatics is to develop targeted methods and tools that integrate scientific literature, biological databases and experimental data for reducing the time of database curation and for accessing evidence, either in the literature or in the datasets, useful for the analysis at hand. Under this scenario, this article reviews the knowledge discovery systems that fuse information from the literature, gathered by text mining, with microarray data for enriching the lists of down and upregulated genes with elements for biological understanding and for generating and validating new biological hypothesis. Finally, an easy to use and freely accessible tool, GeneWizard, that exploits text mining and microarray data fusion for supporting researchers in discovering gene-disease relationships is described.

Adaptive Background Modeling Integrated With Luminosity Sensors and Occlusion Processing for Reliable Vehicle Detection

This paper presents a novel vehicle detection and tracking system with stationary camera that relies on a recursive background-modeling approach, i.e., the adaptive Poisson mixture model, which is integrated with a hardware module consisting of luminosity sensors. The luminosity information side channel allows the system to effectively handle rapid changes in illumination, which is typical of outdoor applications and bottleneck of the existing background pixel classification methods. A novel algorithm for detecting and removing partial and full occlusions among blobs is also proposed. Partial occlusions are detected by evaluating the ratio between the area of the vehicle and the area of the vehicles convex hull and are suppressed by identifying a cutting line using curvature analysis. A predictive model of the shape and motion features of the vehicles over consecutive frames instead corrects the error of the previous levels when full occlusions or background-vehicle occlusions occur in the scene. Quantitative evaluation and comparisons on some real-world scenarios demonstrate that the proposed approach outperforms state-of-the-art methods in terms of both vehicle detection and processing time, particularly due to the robustness and the efficiency of the background-modeling algorithm.

Evaluation of the Traffic Parameters in a Metropolitan Area by Fusing Visual Perceptions and CNN Processing of Webcam Images

This paper proposes a traffic monitoring architecture based on a high-speed communication network whose nodes are equipped with fuzzy processors and cellular neural network (CNN) embedded systems. It implements a real-time mobility information system where visual human perceptions sent by people working on the territory and video-sequences of traffic taken from Webcams are jointly processed to evaluate the fundamental traffic parameters for every street of a metropolitan area. This paper presents the whole methodology for data collection and analysis and compares the accuracy and the processing time of the proposed soft computing techniques with other existing algorithms. Moreover, this paper discusses when and why it is recommended to fuse the visual perceptions of the traffic with the automated measurements taken from the Webcams to compute the maximum traveling time that is likely needed to reach any destination in the traffic network.

Special issue on animal and insect behaviour understanding in image sequences

Imaging systems are, nowadays, used increasingly in a range of ecological monitoring applications, in particular for biological, fishery, geological and physical surveys. These technologies have improved radically the ability to capture high-resolution images in challenging environments and consequently to manage effectively natural resources. Unfortunately, advances in imaging devices have not been followed by improvements in automated analysis systems, necessary because of the need for timeconsuming and expensive inputs by human observers. This analytical ‘bottleneck’ greatly limits the potentialities of these technologies and increases demand for automatic content analysis approaches to enable proactive provision of analytical information. On the other side, the study of the behaviour by processing visual data has become an active research area in computer vision. The visual information gathered from image sequences is extremely useful to understand the behaviour of the different objects in the scene, as well as how they interact with each other or with the surrounding environment. However, whilst a large number of video analysis techniques have been developed specifically for investigating events and behaviour in human-centred applications, very little attention has been paid to the understanding of other live organisms, such as animals and insects, although a huge amount of video data are routinely recorded, e.g. the Fish4Knowledge project (www. fish4knowledge.eu) or the wide range of nest cams (http:// watch.birds.cornell.edu/nestcams/home/index) continuously monitor, respectively, underwater reef and bird nests (there exist also variants focusing on wolves, badgers, foxes etc.). The automated analysis of visual data in real-life environments for animal and insect behaviour understanding poses several challenges for computer vision researchers

An innovative web-based collaborative platform for video annotation

Large scale labeled datasets are of key importance for the development of automatic video analysis tools as they, from one hand, allow multi-class classifiers training and, from the other hand, support the algorithms’ evaluation phase. This is widely recognized by the multimedia and computer vision communities, as witnessed by the growing number of available datasets; however, the research still lacks in annotation tools able to meet user needs, since a lot of human concentration is necessary to generate high quality ground truth data. Nevertheless, it is not feasible to collect large video ground truths, covering as much scenarios and object categories as possible, by exploiting only the effort of isolated research groups. In this paper we present a collaborative web-based platform for video ground truth annotation. It features an easy and intuitive user interface that allows plain video annotation and instant sharing/integration of the generated ground truths, in order to not only alleviate a large part of the effort and time needed, but also to increase the quality of the generated annotations. The tool has been on-line in the last four months and, at the current date, we have collected about 70,000 annotations. A comparative performance evaluation has also shown that our system outperforms existing state of the art methods in terms of annotation time, annotation quality and system’s usability.

Enhanced motor cortex facilitation in patients with vascular cognitive impairment-no dementia.

Data on Transcranial Magnetic Stimulation (TMS) derived measures of cortical excitability and intracortical circuits in age-related white matter changes are scarce. We aimed to assess early changes of motor cortex excitability in nondemented elderly patients with subcortical ischemic vascular disease (SVD). Ten SVD elderly and ten age-matched controls underwent paired-pulse TMS for the analysis of intracortical inhibition (ICI) and facilitation (ICF). All subjects performed neuropsychological assessment and brain magnetic resonance imaging. SVD patients showed abnormal executive control function. No statistically significant differences were found for resting motor threshold, cortical silent period between SVD patients and controls or between the two hemispheres, in patients. A significant enhancement of mean ICF was observed in SVD patients. This study provides the first evidence of functional changes in intracortical excitatory neuronal circuits in patients with SVD and clinical features of vascular cognitive impairment-no dementia. Further studies are required to evaluate whether the observed change of ICF might predict cognitive and/or motor impairment in a population at risk for subcortical vascular dementia.

Variational Method for Image Denoising by Distributed Genetic Algorithms on GRID Environment

The aim of this paper is to present a novel distributed genetic algorithm architecture implemented on grid computing by using the G-Lite middleware developed in the EGEE project. Genetic algorithms are known for their capability to solve a wide range of optimization problems and one of the most relevant features of GAs is their structural parallelism that fits well the intrinsically distributed grid architecture. The proposed architecture is based on different specialized autonomous entities able to interact in order to carry out a global optimization task. The interaction is based on exchange of knowledge on the problem and solutions. In this way the main problem can be solved by using many cooperative small entities that can be classified into different specialized families that cover only one aspect of the global problem. The topology is based on archipelagos of islands that interact by chromosomes migrating with a user-definable strategy. Grid has been mainly used in the high performance computing area. The properties of the proposed GAs architecture and its related computing properties have great potential in solving big instances of optimization problems. Furthermore this implementation (distributed genetic algorithms with grid computing) is suitable to solve time consuming problems reducing by executing different instances on many virtual organizations (VOs) according to the grid philosophy. The proposed parallel algorithm has been tested on denoising problems applied to image processing which are known to be time consuming. The paper reports some results about the time performance compared to traditional denoising filter algorithms

Transcranial Magnetic Stimulation in the Assessment of Motor Cortex Excitability and Treatment of Drug-Resistant Major Depression

Major depression is one of the leading causes of disabling condition worldwide and its treatment is often challenging and unsatisfactory, since many patients become refractory to pharmacological therapies. Transcranial magnetic stimulation (TMS) is a noninvasive neurophysiological investigation mainly used to study the integrity of the primary motor cortex excitability and of the cortico-spinal tract. The development of paired-pulse and repetitive TMS (rTMS) paradigms has allowed investigators to explore the pathophysiology of depressive disorders and other neuropsychiatric diseases linked to brain excitability dysfunctions. Repetitive transcranial magnetic stimulation has also therapeutic and rehabilitative capabilities since it is able to induce changes in the excitability of inhibitory and excitatory neuronal networks that may persist in time. However, the therapeutic effects of rTMS on major depression have been demonstrated by analyzing only the improvement of neuropsychological performance. The aim of this study was to investigate cortical excitability changes on 12 chronically-medicated depressed patients (test group) after rTMS treatment and to correlate neurophysiological findings to neuropsychological outcomes. In detail, we assessed different parameters of cortical excitability before and after active rTMS in the test group, then compared to those of 10 age-matched depressed patients (control group) who underwent sham rTMS. In line with previous studies, at baseline both groups exhibited a significant interhemispheric difference of motor cortex excitability. This neurophysiological imbalance was then reduced in the patients treated with active rTMS, resulting also in a clinical benefit as demonstrated by the improvement in neuropsychological test scores. On the contrary, after sham rTMS, the interhemispheric difference was still evident in the control group. The reported clinical benefits in the test group might be related to the plastic remodeling of synaptic connection induced by rTMS treatment.

Discovering Genes-Diseases Associations From Specialized Literature Using the Grid

This paper proposes a novel method for text mining on the Grid, aimed at pointing out hidden relationships for hypothesis generation and suitable for semi-interactive querying. The method is based on unsupervised clustering and the outputs are visualized with contextual information. Grid implementation is crucial for feasibility. We demonstrate it with a mining run for discovering genes-diseases associations from bibliographic sources and annotated databases. The proposed methodology is in view of a Grid architecture specialized in bioinformatics mining tasks. Some performance considerations are provided.