Publications

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.

This study presents a fully automated membrane segmentation technique for immunohistochemical tissue images with membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the cellular membranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear membranes as a spatial reference. Accurate cell-by-cell membrane segmentation allows per cell morphological analysis and quantification of the target membrane proteins that is fundamental in several medical applications such as cancer characterization and classification, personalized therapy design, and for any other applications requiring cell morphology characterization. Experimental results on real datasets from different anatomical locations demonstrate the wide applicability and high accuracy of our approach in the context of IHC analysis.

We propose a new simplified 3D body model (called Sarc3D) for surveillance application, that can be created, updated and compared in rea-time.People are detected and tracked in each calibrated camera, and their silhouette, appearance, position and orientation are extracted and used to place, scale and orientate a 3D body model. Foreach vertex of the model a signature (color features, reliability and saliency) is computed from the 2D appearance images and exploited for mathing. This approach achieves robustness against partial occlusions, pose and viewpoint changes. The complete proposal and a full experimental evaluation is presented, using a new benchmark suite and the PETS2009 dataset.

Nowadays, a considerable amount of genetic and biomedical studies are mostly diffused on the Web and freely available. This exciting capability, if from one side opens the way to new scenarios of cooperating research, on the other side makes the knowledge retrieval and extraction an extremely time consuming operation. In this context, the development of new tools and algorithms to automatically support the scientist activity to achieve a reliable interpretation of the complex interactions among biological entities is mandatory. In this paper we present a new methodology aimed at quantifying the biological degree of correlation among biomedical terms present in literature. The proposed method overcomes the limitation of current tools based on public literature information only, by exploiting the trustworthy information provided by biological pathways databases. We demonstrate how to integrate trusted pathway information in a semantic correlation extraction chain based on UMLS Metathesaurus and relying on PubMed as literature database. The effectiveness of the obtained results remarks the importance of automatically quantifying the degree of correlation among biomedical terms in order to helpfully support the scientist research activity.

The identification of intruders in groups of people moving in wide open areas represents a challenging scenario where coordination between cameras can be certainly used but this solution is not enough. In this paper, we propose to go beyond pure vision-based approaches by integrating the use of distributed cameras with the RFID technology. To this end, we introduce a system that “maps” RFID tags to people detected by cameras by using sophisticated techniques to filter the singular modalities and an evidential fusion architecture, based on Transferable Belief Model, to combine the two sources of information and manage conflict between them. The conducted experimental evaluation shows very promising results, especially in treating groups of people.

Dangerous situations arise in everyday life and many efforts have been lavished to exploit technology to increase the level of safety in urban areas. Video analysis is absolutely one of the most important and emerging technology for security purposes. Automatic video surveillance systems commonly analyze the scene searching for moving objects. Well known techniques exist to cope with this problem that is commonly referred as change detection". Every time a dierence against a reference model is sensed, it should be analyzed to allow the system to discriminateamong a usual situation or a possible threat. When the sensor is a camera, motion is the key element to detect changes and moving objects must be correctly classied according to their nature. In this context we can distinguish among two dierent kinds of threat that can lead to dangerous situations in a video-surveilled environment. The first one is due to environmental changes such as rain, fog or smoke present in the scene. This kind of phenomena are sensed by the camera as moving pixelsand, subsequently as moving objects in the scene. This kind of threats shares some common characteristics such as texture, shape and color information and can be detected observing the features' evolution in time. The second situation arises whenpeople are directly responsible of the dangerous situation. In this case a subject is acting in an unusual way leading to an abnormal situation. From the sensor's point of view, moving pixels are still observed, but specic features and time-dependent statistical models should be adopted to learn and then correctly detect unusual and dangerous behaviors. With these premises, this chapter will present two different case studies. The rst one describes the detection of environmental changes in theobserved scene and details the problem of reliably detecting smoke in outdoor environments using both motion information and global image features, such as color information and texture energy computed by the means of the Wavelet transform.The second refers to the problem of detecting suspicious or abnormal people behaviors by means of people trajectory analysis in a multiple cameras video-surveillance scenario. Specically, a technique to infer and learn the concept of normality is proposed jointly with a suitable statistical tool to model and robustly compare people trajectories.

People social interaction analysis is a complex and interesting problem that can be faced from several points of view depending on the application context. In videosurveillance contexts many indicators of people habits and relations exist and, among these, people trajectories analysis can reveal many aspects of the way people behave in social environments. We propose a statistical framework for trajectories mining that analyzes, in an integrated solution, several aspects of the trajectories such as location, shape and speed properties. Three different models are proposed to deal with non-idealities of the selected features in conjunction with a robust inexact- matching similarity measure for comparing sequences with different lengths. Experimental results in a real scenario demonstrates the efficacy of the framework in clustering people trajectories with the purpose of analyze frequent behaviors in complex environments.

Two of the most important state-of-the-art challenges in face recognition are: dealing with image acquisition conditions very different between the gallery and the probe set and dealing with large datasets of individuals. In this paper we face both aspects presenting a method which is able to work in “real life” scenarios, in which face images are differently illuminated, can be partially occluded or can show different facial expressions or noise levels. Our proposed system has been tested with datasets of 1000 different individuals, showing performances usually obtained with much smaller gallery sets and much better images. The approach we propose is based on SIFT descriptors, which are known to be robust to different illumination conditions and noise levels. SIFTs are used to automatically detect face regions (mouth area, eye area, etc.). Such regions are then independently compared with the corresponding regions of the gallery images for computing a similarity-based renking of the system’s database. © 2010 Taylor & Francis Group, LLC.

In this paper we detail a proposal of exploitation of expert-made commentaries in a unified system for illuminated manuscripts images analysis. In particular we will explore the possibility to improve the automatic segmentation of meaningful pictures, as well as the retrieval by similarity search engine, using clusters of keywords extracted from commentaries as semantic information.