Publications

This paper presents a novel approach for detecting multiple instances of the same object for pick-and-place automation. The working conditions are very challenging, with complex objects, arranged at random in the scene, and heavily occluded. This approach exploits SIFT to obtain a set of correspondences between the object model and the current image. In order to segment the multiple instances of the object, the correspondences are clustered among the objects using a voting scheme which determines the best estimate of the object's center through mean shift. This procedure is compared in terms of accuracy with existing homography-based solutions which make use of RANSAC to eliminate outliers in the homography estimation.

This paper presents a novel approach for detecting multipleinstances of the same object for pick-and-place automation.The working conditions are very challenging, with complex objects, arranged at random in the scene, and heavily occluded. This approach exploits SIFT to obtain a set of correspondences between the object model and the current image. In order to segment the multiple instances of the object, the correspondences are clustered among the objects using a voting scheme which determines the best estimate of the object’s center through mean shift. This procedure is compared in terms of accuracy with existing homography-based solutions which make use of RANSAC to eliminate outliers in the homography estimation.

This work proposes a general approach to optimize the time required to perform a choice in a decision support system, with particular reference to image processing tasks with neighborhood analysis. The decisions are encoded in a decision table paradigm that allows multiple equivalent procedures to be performed for the same situation. An automatic synthesis of the optimal decision tree is implemented in order to generate the most efficient order in which conditions should be considered to minimize the computational requirements.To test out approach, the connected component labeling scenario is considered. Results will show the speedup introduced using an automatically built decision system able to efficiently analyze and explore the neighborhood.

In this paper, we present a new approach to object tracking based on batteries of particle filter working in multicamera systems with non overlapped fields of view. In each view the moving objects are tracked with independent particle filters; each filter exploits a likelihood function based on both color and motion information. The consistent labeling of people exiting from a camera field of view and entering in a neighbor one is obtained sharing particles information for the initialization of new filtering trackers. The information exchange algorithm is based on path-nodes, which are a graph-based scene representation usually adopted in computer graphics. The approach has been tested even in case of simultaneous transitions, occlusions, and groups of people. Promising results have been obtained and here presented using a real setup of non overlapped cameras.

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.