Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.

Multivariate Density Estimation

Traditional and recent approaches in background modeling for foreground detection: An overview

Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching developed by David Lowe (1999,2004). This descriptor as well as related image descriptors are used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for robust image matching and object recognition under real-world conditions.In its original formulation, the SIFT descriptor comprised a method for detecting interest points from a grey-level image at which statistics of local gradient directions of image intensities were accumulated to give a summarizing description of the local image structures in a local neighbourhood around each interest point, with the intention that this descriptor should be used for matching corresponding interest points between different images. Later, the SIFT descriptor has also been applied at dense grids (dense SIFT) which have been shown to lead to better performance for tasks such as object categorization and texture classification. The SIFT descriptor has also been extended from grey-level to colour images and from 2-D spatial images to 2+1-D spatio-temporal video.

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Scale Invariant Feature Transform

Traditional wearable devices have various shortcomings, such as uncomfortableness for long-term wearing, and insufficient accuracy, etc. Thus, health monitoring through traditional wearable devices is hard to be sustainable. In order to obtain healthcare big data by sustainable health monitoring, we design "Smart Clothing", facilitating unobtrusive collection of various physiological indicators of human body. To provide pervasive intelligence for smart clothing system, mobile healthcare cloud platform is constructed by the use of mobile internet, cloud computing and big data analytics. This paper introduces design details, key technologies and practical implementation methods of smart clothing system. Typical applications powered by smart clothing and big data clouds are presented, such as medical emergency response, emotion care, disease diagnosis, and real-time tactile interaction. Especially, electrocardiograph signals collected by smart clothing are used for mood monitoring and emotion detection. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make smart clothing ubiquitous for a wide range of applications.

Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring

This paper presents a novel vehicle detection and tracking system with stationary camera that relies on a recursive background-modeling approach, ie, 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 vehicle's 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

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

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.

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

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.

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

The new generation of Intelligent Transportation Systems (ITSs) should be able both to monitor in real-time the current user location and traffic conditions, and to access the heterogeneous data sources maintained by different organisations and useful for ITS applications. For this reason, a layered ITS architecture is proposed, which consists of a Wi-Fi communication layer for data exchange between users and control devices, a monitoring layer provided with fast algorithms to locate the users and to compute the travel times of a traffic network, an application layer to supervise the monitoring layer and to map the proprietary data sources into a standard format, i.e., the Resource Description Framework Schema (RDFS), and a semantic layer to retrieve and process RDFS data for supporting mobile applications. Implementation details are also given. In particular, a novel motion detection technique based on the Poisson distribution to evaluate the traffic parameters and a technique based on neural networks to locate the users are presented, whereas a suitable topology of the overall system to timely inform the mobile users is outlined. The architecture supports the internetworking of different monitoring and communication technologies to best fit a variety of users and use scenarios.

Integrating Location Tracking, Traffic Monitoring and Semantics in a Layered ITS Architecture

In this paper we propose an implicit relevance feedback method with the aim to improve the performance of known Content Based Image Retrieval (CBIR) systems by re-ranking the retrieved images according to users' eye gaze data. This represents a new mechanism for implicit relevance feedback, in fact usually the sources taken into account for image retrieval are based on the natural behavior of the user in his/her environment estimated by analyzing mouse and keyboard interactions. In detail, after the retrieval of the images by querying CBIRs with a keyword, our system computes the most salient regions (where users look with a greater interest) of the retrieved images by gathering data from an unobtrusive eye tracker, such as Tobii T60. According to the features, in terms of color, texture, of these relevant regions our system is able to re-rank the images, initially, retrieved by the CBIR. Performance evaluation, carried out on a set of 30 users by using Google Images and "pyramid" like keyword, shows that about the 87% of the users is more satisfied of the output images when the re-raking is applied.

Alberto Faro

Papers

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

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

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

Integrating Location Tracking, Traffic Monitoring and Semantics in a Layered ITS Architecture

Visual attention for implicit relevance feedback in a content based image retrieval