Publications by Rita Cucchiara

This paper describes a complete architecture for the interactive exploration and annotation of artistic collections. In particular the focus is on Renaissance illuminated manuscripts, which typically contain thousands of pictures, used to comment or embellish the manuscript Gothic text. The final aim is to create a human centered multimedia application allowing the non practitioners to enjoy these masterpieces and expert users to share their knowledge. The system is composed by a modern user interface for browsing, surfing and querying, an automatic segmentation module, to ease the initial picture extraction task, and a similarity based retrieval engine, used to provide visually assisted tagging capabilities. A relevance feedback procedure is included to further refine the results. Experiments are reported regarding the adopted visual features based on covariance matrices and the Mean Shift Feature Space Warping relevance feedback. Finally some hints on the user interface for museum installations are discussed.

Pattern recognition is becoming a key application in bodyarea networks. This paper presents a framework promoting unsupervised training for multi-modal, multi-sensor classification systems. Specifically, it enables sensors provided with patter-recognition capabilities to autonomously supervise the learning process of other sensors. The approach is discussed using a case study combining a smart camera and a body-worn accelerometer. The body-worn accelerometer sensor is trained to recognize four user activities pairing accelerometer data with labels coming from the camera. Experimental results illustrate the applicability of the approach in different conditions.

In questo articolo si parla delle nuove frontiere di visione artificiale nella videosorveglianza di persone in ambienti pubblici e privati ed in particolare di analisi comportamentale. Sono poi presentate alcuni progetti in corso presso l’ImageLab di Modena

The availability of new techniques and tools for Video Surveillance and the capability of storing huge amounts of visual data acquired by hundreds of cameras every day call for a convergence between pattern recognition, computer vision and multimedia paradigms. A clear need for this convergence is shown by new research projects which attempt to exploit both ontology-based retrieval and video analysis techniques also in the field of surveillance.This paper presents the ViSOR (Video Surveillance Online Repository) framework, designed with the aim of establishing an open platform for collecting, annotating, retrieving, and sharing surveillance videos, as well as evaluating the performance of automatic surveillance systems. Annotations are based on a reference ontology which has been defined integrating hundreds of concepts, some of them coming from the LSCOM and MediaMill ontologies. A new annotation classification schema is also provided, which is aimed at identifying the spatial, temporal and domain detail level used.The ViSOR web interface allows video browsing, querying by annotated concepts or by keywords, compressed video previewing, media downloading and uploading.Finally, ViSOR includes a performance evaluation desk which can be used to compare different annotations.

This paper presents an innovative approach for localizingand segmenting duplicate objects for industrial applications.The working conditions are challenging, withcomplex heavily-occluded objects, arranged at random inthe scene. To account for high flexibility and processingspeed, this approach exploits SIFT keypoint extraction andmean shift clustering to efficiently partition the correspondencesbetween the object model and the duplicates ontothe different object instances. The re-projection (by meansof an Euclidean transform) of some delimiting points ontothe current image is used to segment the object shapes. Thisprocedure is compared in terms of accuracy with existinghomography-based solutions which make use of RANSACto eliminate outliers in the homography estimation. Moreover,in order to improve the extraction in the case of reflectiveor transparent objects, multiple object models are usedand fused together. Experimental results on different andchallenging kinds of objects are reported.

This paper proposes a real-time system capable to extract andmodel object trajectories from a multi-camera setup with theaim of identifying abnormal paths. The trajectories are modeledas a sequence of positional distributions (2D Gaussians)and clustered in the training phase by exploiting an innovativedistance measure based on a global alignment techniqueand Bhattacharyya distance between Gaussians. An on-lineclassification procedure is proposed in order to on-the-fly classifynew trajectories into either “normal” or “abnormal” (in thesense of rarely seen before, thus unusual and potentially interesting).Experiments on a real scenario will be presented.

Proceedings of the XIth International Conference on Artificial Intelligence

On-line action recognition from a continuous stream of actionsis still an open problem with fewer solutions proposedcompared to time-segmented action recognition. The mostchallenging task is to classify the current action while findingits time boundaries at the same time. In this paper wepropose an approach capable of performing on-line actionsegmentation and recognition by means of batteries of HMMtaking into account all the possible time boundaries and actionclasses. A suitable Bayesian normalization is appliedto make observation sequences of different length comparableand computational optimizations are introduce to achievereal-time performances. Results on a well known actiondataset prove the efficacy of the proposed method

In this paper a document analysis tool for historical manuscripts is proposed. The goal is to automatically segment layout components of the page, that is text, pictures and decorations. We specifically focused on the pictures, proposing a set of visual features able to identify significant pictures and separating them from all the floral and abstract decorations. The analysis is performed by blocks using a limited set of color and texture features, including a new texture descriptor particularly effective for this task, namely Gradient Spatial Dependency Matrix. The feature vectors are processed by an embedding procedure which allows increased performance in later SVM classification.