Illustrations Segmentation in Digitized Documents Using Local Correlation Features

In this paper we propose an approach for Document Layout Analysis based on local correlation features. We identify and extract illustrations in digitized documents by learning the discriminative patterns of textual and pictorial regions. The proposal has been demonstrated to be effective on historical datasets and to outperform the state-of-the-art in presence of challenging documents with a large variety of pictorial elements

Active query process for digital video surveillance forensic applications

Multimedia forensics is a new emerging discipline regarding the analysis and exploitation of digital data as support for investigation to extract probative elements. Among them, visual data about people and people activities, extracted from videos in an efficient way, are becoming day by day more appealing for forensics, due to the availability of large video-surveillance footage. Thus, many research studies and prototypes investigate the analysis of soft biometrics data, such as people appearance and people trajectories. In this work, we propose new solutions for querying and retrieving visual data in an interactive and active fashion for soft biometrics in forensics. The innovative proposal joins the capability of transductive learning for semi-supervised search by similarity and a typical multimedia methodology based on user-guided relevance feedback to allow an active interaction with the visual data of people, appearance and trajectory in large surveillance areas. Approaches proposed are very general and can be exploited independently by the surveillance setting and the type of video analytic tools

Transitional Care for Patients with Congenital Colorectal Diseases:An EUPSA Network Office, ERNICA, and eUROGEN Joint Venture

Background: Transition of care (TOC; from childhood into adulthood) of patients with anorectal malformations (ARM) and Hirschsprung disease (HD) ensures continuation of care for these patients. The aim of this international study was to assess the current status of TOC and adult care (AC) programs for patients with ARM and HD. Methods: A survey was developed by members of EUPSA, ERN eUROGEN, and ERNICA, including patient representatives (ePAGs), comprising of four domains: general information, general questions about transition to adulthood, and disease-specific questions regarding TOC and AC programs. Recruitment of centres was done by the ERNs and EUPSA, using mailing lists and social media accounts. Only descriptive statistics were reported. Results:In total, 82 centres from 21 different countries entered the survey. Approximately half of them were ERN network members. Seventy-two centres (87.8%) had a self-reported area of expertise for both ARM and HD. Specific TOC programs were installed in 44% of the centres and AC programs in 31% of these centres. When comparing centres, wide variation was observed in the content of the programs. Conclusion: Despite the awareness of the importance of TOC and AC programs, these programs were installed in less than 50% of the participating centres. Various transition and AC programs were applied, with considerable heterogeneity in implementation, content and responsible caregivers involved. Sharing best practice examples and taking into account local and National Health Care Programs might lead to a better continuation of care in the future. Level of Evidence: III.</p

Appearance tracking by transduction in surveillance scenarios

We propose a formulation of people tracking problem as a Transductive Learning (TL) problem. TL is an effective semi-supervised learning technique by which many classification problems have been recently reinterpreted as learning labels from incomplete datasets. In our proposal the joint exploitation of spectral graph theory and Riemannian manifold learning tools leads to the formulation of a robust approach for appearance based tracking in Video Surveillance scenarios. The key advantage of the presented method is a continuously updated model of the tracked target, used in the TL process, that allows to on-line learn the target visual appearance and consequently to improve the tracker accuracy. Experiments on public datasets show an encouraging advancement over alternative state-of the-art techniques

People appearance tracing in video by spectral graph transduction

Following people in different video sources is a challenging task: variations in the type of camera, in the lighting conditions, in the scene settings (e.g. crowd or occlusions) and in the point of view must be accounted. In this paper we propose a system based only on appearance information that, disregarding temporal and spatial information, can be flexibly applied on both moving and static cameras. We exploit the joint use of transductive learning and spectral properties of graph Laplacians proposing a formulation of the people tracing problem as a semi-supervised classification. The knowledge encoded in two labeled input sets of positive and negative samples of the target person and the continuous spectral update of these models allow us to obtain a robust approach for people tracing in surveillance video sequences. Experiments on publicly available datasets show satisfactory results and exhibit a good robustness in dealing with short and long term occlusions

Feature Space Warping Relevance Feedback with Transductive Learning

Relevance feedback is a widely adopted approach to improve content-based information retrieval systems by keeping the user in the retrieval loop. Among the fundamental relevance feedback approaches, feature space warping has been proposed as an effective approach for bridging the gap between high-level semantics and the low-level features. Recently, combination of feature space warping and query point movement techniques has been proposed in contrast to learning based approaches, showing good performance under dierent data distributions. In this paper we propose to merge feature space warping and transductive learning, in order to benet from both the ability of adapting data to the user hints and the information coming from unlabeled samples. Experimental results on an image retrieval task reveal signicant performance improvements from the proposed method

FEATURE SPACE WARPING RELEVANCE FEEDBACK WITH TRANSDUCTIVE LEARNING

Abstract. Relevance feedback is a widely adopted approach to improve content-based information retrieval systems by keeping the user in the retrieval loop. Among the fundamental relevance feedback approaches, feature space warping has been proposed as an effective approach for bridging the gap between high-level semantics and the low-level features. Recently, combination of feature space warping and query point movement techniques has been proposed in contrast to learning based approaches, showing good performance under different data distributions. In this paper we propose to merge feature space warping and transductive learning, in order to benefit from both the ability of adapting data to the user hints and the information coming from unlabeled samples. Experimental results on an image retrieval task reveal significant performance improvements from the proposed method