Publications by Rita Cucchiara

In this work we propose a system for automatic document segmentation to extract graphical elements from historical manuscripts and then to identify significant pictures from them, removing floral and abstract decorations. The system performs a block based analysis by means of color and texture features. The Gradient Spatial Dependency Matrix, a new texture operator particularly effective for this task, is proposed. The feature vectors are processed by an embedding procedure which allows increased performance in later SVM classification. Results for both feature extraction and embedding based classification are reported, supporting the effectiveness of the proposal.

It is our great pleasure to welcome you to the 1st ACM Workshop on Multimedia in Forensics -- MiFor'09.With the proliferation of multimedia data on the web, surveillance cameras in cities, and mobile phones in everyday life we see an enormous growth in multimedia data that needs to be analyzed by forensic investigators. The sheer volume of such datasets makes manual inspection of all data impossible. Tools are needed to support the investigator in their quest for relevant clues and evidence and in their strive towards preventing crime.The multimedia community has developed new solutions for management of large collections of video footage, images, audio and other multimedia content, knowledge extraction and categorization, pattern recognition, indexing and retrieval, searching, browsing and visualization, and modeling and simulation in various domains. Due to the inherent uncertainty and complexity of forensic data, applying those techniques to forensic data is not straightforward. The time is ripe to tailor these results for forensics. Multimedia in forensics is the workshop which target is to join the research topics and the applications.The workshop aims at addressing the multimedia toolbox supporting the forensic process from the prevention of crime, capturing and annotation of the crime scene, the investigation of the data in the lab, up to the presentation of the results in court. It is a first attempt in bringing multimedia tools in to this exciting application field. The target audience consists of researchers working on innovative technology, representatives from companies developing tools, and forensic investigators in various disciplines.Despite the ambitious objective for the workshop and it being the first edition, it attracted a good number of quality submissions fairly distributed among different countries and among the different topics of the workshop. The MiFor09 Technical Program Committee includes the most experienced researchers in the related research fields, and thanks to their indispensable effort we were able to select 11 papers for oral presentation.The workshop schedules four oral sessions, named "Detection and Mining", "Multimedia forensics prototypes", "Forgery and Splicing Detection" and "Tracking". In addition, the program includes a keynote address by Professor Mohan Kankanhalli, a distinguished lecturer in the field.

This chapter will address most of the aspects of modern video surveillance with the reference to the research activity conducted at University of Modena and Reggio Emilia, Italy, within the scopes of the national FREE SURF (FREE SUrveillance in a pRivacy-respectFul way) and NATO-funded BE SAFE (Behavioral lEarning in Surveilled Areas with Feature Extraction) projects. Moving object detection and tracking from a single camera, multi-camera consistent labeling and trajectory shape analysis for path classification will be the main topics of this chapter.

Ambient Intelligence (AmI) is an emerging field of research that comprises new paradigms, techniques and systems for intelligent processing of distributed sensing. A challenging arena for AmI framework is represented by urban environments that are characterized by high complexity, numerous sources of data,and spreading of interesting and non-trivial applications. In this context, the project LAICA (Laboratory of Ambient Intelligence for a friendly city) represents a real experiment of the usefulness of AmI for advanced services to citizens. This chapter will address solutions of video analysis that can be directly applied in urban AmI. It describes in details the uniqueness of LAICA approach, focusing in particular on the use of computer vision techniques for monitoring public parks. People surveillanceand web-based video broadcasting will be taken into account.

This paper reports an application of trajectory analysis in which forensics and video surveillance techniques are jointly employed for providing a new tool of multimedia forensics. Advanced video surveillance techniques are used to extract from a multi-camera system the trajectories of the moving people which are then modelled by either their positions (projected on the ground plane) or their directions of movement. Both these two representations can be very suitable for querying large video repositories, by searching for similar trajectories in terms of either sequences of positions or trajectory shape (encoded as sequence of angles, where positions do not care). Preliminary examples of the possible use of this approach are shown.

In this paper we present a system for automatic segmentation, annotation and image retrieval based on content, focused on illuminated manuscripts and in particular the Borso D'Este Holy Bible. To enhance the interaction possibilities with this work, full of decorations and illustrations, we exploit some well known document analysis techniques in addition to some new approaches, in order to achieve good segmentation of pages into meaningful visual objects with the relative annotation. We wanted to extend the standard keyword-based retrieval approach in a commentary with a modern visual-based retrieval by appearance similarity: an entire software user interface for exploration and visual search of illuminated manuscripts.

This paper presents a method for recognizing human actions in a multi-camera setup. The proposed method automatically extracts significant points on the human body, without the need of artificial markers. A sophisticated appearance-based tracking able to cope with occlusions is exploited to extract a probability map for each moving object. A segmentation technique based on mixture of Gaussians is then employed to extract and track significant points on this map, corresponding to significant regions on the human silhouette. The point tracking produces a set of 3D trajectories that are compared with other trajectories by means of global alignment and dynamic programming techniques. Preliminary experiments showed the potentiality of the proposed approach.

Recognizing different actions with a unique approach can be a difficult task. This paper proposes a novel holistic representation of actions that we called "action signature". This 1D trajectory is obtained by parsing the 2D image containing the orientations of the gradient calculated on the motion feature map called motion-history image. In this way, the trajectory is a sketch representation of how the object motion varies in time. A robust statistical framework based on mixtures of von Mises distributions and dynamic programming for sequence alignment are used to compare and classify actions/trajectories. The experimental results show a rather high accuracy in distinguishing quite complicated actions, such as drinking, jumping, or abandoning an object.

AD-HOC copes with the problem of multiple people tracking in video surveillance in presence of large occlusions. The main novelty is the adoption of an appearance-based approach in a formal Bayesian framework: the status of each object is defined at pixel level, where each pixel is characterized by the appearance, i.e. the color (integrated along the time) and the likelihood to belong to the object. With these data at pixel-level and a probability of non-occlusion at object-level, the problem of occlusions is addressed. The method does not aim at detecting the presence of an occlusion only, but classifies the type of occlusion at a sub-region level and evolve the status of theobject in a selective way. The AD-HOC tracking has been tested in many application for indoor and outdoor surveillance. Results on PETS2006 test set are reported where many people and abandoned objects are detected and tracked.