Abstract
Video surveillance systems must process and manage a growing amount of data captured over a network of cameras for various recognition tasks. In order to limit human labour and error, this paper presents a spatial-temporal fusion approach to accurately combine information from Region of Interest (RoI) batches captured in a multi-camera surveillance scenario. In this paper, feature-level and score-level approaches are proposed for spatial-temporal fusion of information to combine information over frames, in a framework based on ensembles of GMM-UBM (Universal Background Models). At the feature-level, features in a batch of multiple frames are combined and fed to the ensemble, whereas at the score-level the outcome of ensemble for individual frames are combined. Results indicate that feature-level fusion provides higher level of accuracy in a very efficient way.

Abstract
The detection of staff lines is the first step of most Optical Music Recognition (OMR) systems. Its great significance derives from the ease with which we can then proceed with the extraction of musical symbols. All OMR tasks are usually achieved using binary images by setting thresholds that can be local or global. These techniques however, may remove relevant information of the music sheet and introduce artifacts which will degrade results in the later stages of the process. It arises therefore a need to create a method that reduces the loss of information due to the binarization. The baseline for the methodology proposed in this paper follows the shortest path algorithm proposed in [1]. The concept of strong staff pixels (SSP's), which is a set of pixels with a high probability of belonging to a staff line, is proposed to guide the cost function. The SSP allows to overcome the results of the binary based detection and to generalize the binary framework to grayscale music scores. The proposed methodology achieves good results.

Abstract
Background and objective: Cosmetic outcome of breast cancer conservative treatment (BCCT) remains without a standard evaluation method. Subjective methods, in spite of their low reproducibility, continue to be the most frequently used. Objective methods, although more reproducible, seem unable to translate all the subtleties involved in cosmetic outcome. The breast cancer conservative treatment cosmetic results (BCCT. core) software was developed in 2007 to try to overcome these pitfalls. The software is a semi-automatic objective tool that evaluates asymmetry, color differences and scar visibility using patient's digital pictures. The purpose of this work is to review the use of the BCCT. core software since its availability in 2007 and to put forward future developments. Methods: All the online requests for BCCT. core use were registered from June 2007 to December 2014. For each request the department, city and country as well as user intention (clinical use/research or both) were questioned. A literature search was performed in Medline, Google Scholar and ISI Web of Knowledge for all publications using and citing "BCCT.core". Results: During this period 102 centers have requested the software essentially for clinical use. The BCCT. core software was used in 19 full published papers and in 29 conference abstracts. Conclusions: The BCCT. core is a user friendly semi-automatic method for the objective evaluation of BCCT. The number of online requests and publications have been steadily increasing turning this computer program into the most frequently used tool for the objective cosmetic evaluation of BCCT.

Abstract
Patient-specific surgical predictions of Breast Conserving Therapy, through mechano-biological simulations, could inform the shared decision making process between clinicians and patients by enabling the impact of different surgical options to be visualised. We present an overview of our processing workflow that integrates MR images and three dimensional optical surface scans into a personalised model. Utilising an interactively generated surgical plan, a multi-scale open source finite element solver is employed to simulate breast deformity based on interrelated physiological and biomechanical processes that occur post surgery. Our outcome predictions, based on the pre-surgical imaging, were validated by comparing the simulated outcome with follow-up surface scans of four patients acquired 6 to 12 months post-surgery. A mean absolute surface distance of 3.3mm between the follow-up scan and the simulation was obtained.

Abstract
Group behaviour characterisation is a topic not so well studied in the video surveillance community due to its difficulty and large variety of topics involved, but mainly because the lack of valid semantic concepts that relate collective activity to social context. In this work, our proposal is three-fold: a new definition of semantic concepts for social group analysis considering environment context, a novel video surveillance dataset that conveys a sociological perspective, and a descriptor that emphasises social interactions cues within a group. Promising results were revealed in order to deal with such complex problem.

Abstract
Cervical cancer remains a significant cause of mortality in low-income countries. Digital colposcopy is a promising and inexpensive technology for the detection of cervical intraepithelial neoplasia. However, diagnostic sensitivity varies widely depending on the doctor expertise. Therefore, automation of this process is needed in both, detection and visualization. Colposcopies cover four steps: macroscopic view with magnifier white light, observation under green light, Hinselmann and Schiller. Also, there are transition intervals where the specialist manipulates the observed area. In this paper, we focus on the temporal segmentation of the video in these steps. Using our solution, physicians may focus on the step of interest and lesion detection tools can determine the interval to diagnose. We solved the temporal segmentation problem using Weighted Automata. Images were described by their chromacity histograms and labeled using a KNN classifier with a precision of 97%. Transition frames were recognized with a precision of 91 %.