Abstract
While video surveillance systems are spreading everywhere, extracting meaningful information from what they are recording is still prohibitively expensive. There is a major effort under way in order to make this process economical by including an intelligent software that eases the burden of the system. In this paper, we introduce an incremental learning framework to classify parallel data streams generated in a multi-camera surveillance scenario. The framework exploits active learning strategies in order to interact wisely with operators to address various problems that exist in such non-stationary environments, such as concept drift and concept evolution. If we look at the problem as mining parallel streams, the framework can address learning from uneven parallel streams applying a class-based ensemble, a problem that has not been addressed before. Favourable results indicate the success of the framework.

Abstract
Through the years, several CAD systems have been developed to help radiologists in the hard task of detecting signs of cancer in the numerous screening mammograms. A more recent trend includes the development of pre-CAD systems aiming at identifying normal mammograms instead of detecting suspicious ones. Normal breasts are screened-out from the process, leaving radiologists more time to focus on more difficult cases. In this work, a new approach for the identification of normal breasts is presented. Considering that even breasts with malignant findings are mostly constituted by normal tissue, the breast area is divided into blocks which are then compared pairwise. If all blocks are very similar, the breast is labelled as normal, and as suspicious otherwise. Features characterizing the pairwise block similarity and characterizing the intra-block pixel distribution are used to design a predictive method based on machine learning techniques. The proposed solution was applied on a real world screening setting composed by nearly 18000 mammograms. Results are similar to the more complex state of the art approaches by correctly identifying more than 20% of the normal mammograms. These results suggest the usefulness of the relative comparison instead of the absolute classification. When properly used, simple statistics can suffice to distinguish the clearly normal breasts.

Abstract
Breast cancer is one of the most mediated malignant diseases, because of its high incidence and prevalence, but principally due to its physical and psychological invasiveness. Surgeons and patients have often many options to consider for undergoing the procedure. The ability to visualise the potential outcomes of the surgery and make decisions on their surgical options is, therefore, very important for patients and surgeons. In this paper we investigate the fitting of a 3d point cloud of the breast to a parametric model usable in surgery planning, obtaining very promising results with real data.

Abstract
Breast Cancer is still a serious health threat to women, both physically and psychologically. Fortunately, treatments involving complete breast removal are rarely needed today, as better treatment options are available. Mammography can show changes in the breast up to two years before a physician can feel them. Computer-aided detection and diagnosis is considered to be one of the most promising approaches that may improve the efficiency of mammography. Furthermore, there is a strong correlation between the presence of calcifications and the occurrence of breast cancer. In this paper we present a new technique to detect calcifications in mammogram images. The main objective is to support radiologists with automatic detection methods applied to medical images. Motivated by the fact that calcifications, when compared to the rest of the image, exhibit irregular characteristics, a technique based on Bayesian surprise is used. Tests were performed using INBreast, a recent fully annotated database, composed of full field digital mammograms. Comparison both with a recently proposed state of the art method and other common image techniques showed the superiority of our method. False positives are, however, still an issue and further studies focused on their reduction while maintaining a high sensitivity are planned.

Abstract
Biometric systems based on iris are vulnerable to several attacks, particularly direct attacks consisting on the presentation of a fake iris to the sensor. The development of iris liveness detection techniques is crucial for the deployment of iris biometric applications in daily life specially in the mobile biometric field. The 1st Mobile Iris Liveness Detection Competition (MobILive) was organized in the context of IJCB2014 in order to record recent advances in iris liveness detection. The goal for (MobILive) was to contribute to the state of the art of this particular subject. This competition covered the most common and simple spoofing attack in which printed images from an authorized user are presented to the sensor by a non-authorized user in order to obtain access. The benchmark dataset was the MobBIOfake database which is composed by a set of 800 iris images and its corresponding fake copies (obtained from printed images of the original ones captured with the same handheld device and in similar conditions). In this paper we present a brief description of the methods and the results achieved by the six participants in the competition. © 2014 IEEE.

Abstract