Publications

In this paper we present a face recognition system based on the Scale Invariant Feature Transform (SIFT) image descriptors recently proposed by Lowe [6] and largely used in generic object recognition tasks. We show how SIFT descriptors can be used in a robust face recognition system coupled with some simple image normalization processes and geometric constraints on SIFT matchings. In the paper we present an extensive experimental evaluation of the prosed SIFT-based face recognition approach comparing it with a “standard” Linear Discriminant Analysis (LDA)-based method, commonly considered one of the best performing face recognition technique. The two systems have been tested using images collected from different face recognition benchmarks in order to simulate real-life applications in which image acquisition parameters largely vary from one query image to the other. In all our tests the SIFT-based method clearly outperformed the LDA-based one, showing that the a priori knowledge embedded in the SIFT local description of image appearances is more robust than trainingbased systems in which appearance variability factors need to be off-line learned. © 2008 Taru Publication.

In this work we describe a new approach for texture characterization. Starting from the autocorrelation matrix an elegant description through a mixture of Von Mises distributions is proposed. A compact 6 valued descriptor is produced for each block and served as input to an SVM classifier. Tests are carried out on high resolution illuminated manuscripts images.

This paper presents a layered network architecture and the enabling technologies for accomplishing vision-based behavioral analysis of unattended environments. Specifically the vision network covers both the attended environment and its surroundings by means of hybrid cameras. The layer overlooking at the surroundings is laid outdoor and tracks people, monitoring entrance/exit points. It recovers the geometry of the site under surveillance and communicates people positions to a higher level layer. The layer monitoring the unattended environment undertakes similar goals, with the addition of maintaining a global mosaic of the observed scene for further understanding. Moreover, it merges information coming from sensors beyond the vision to deepen the understanding or increase the reliability of the system. The behavioral analysis is demanded to a third layer that merges the information received from the two other layers and infers knowledge about what happened, happens and will be likely happening in the environment. The paper also describes a case study that was implemented in the Engineering Campus of the University of Modena and Reggio Emilia, where our surveillance system has been deployed in a computer laboratory which was often unaccessible due to lack of attendance.

Outdoor surveillance is one of the most attractive application of video processing and analysis. Robust algorithms must be defined and tuned to cope with the non-idealities of outdoor scenes. For instance, in a public park, an automatic video surveillance system must discriminate between shadows, reflections, waving trees, people standing still or moving, and other objects. Visual knowledge coming from multiple cameras can disambiguate cluttered and occluded targets by providing a continuous consistent labeling of tracked objects among the different views. This work proposes a new approach for coping with this problem in multi-camera systems with overlapped Fields of View (FoVs). The presence of overlapped zones allows the definition of a geometry-based approach to reconstruct correspondences between FoVs, using only homography and epipolar lines (hereinafter HECOL: Homography and Epipolar-based COnsistent Labeling) computed automatically with a training phase. We also propose a complete system that provides segmentation and tracking of people in each camera module. Segmentation is performed by means of the SAKBOT (Statistical and Knowledge Based Object Tracker) approach, suitably modified to cope with multi-modal backgrounds, reflections and other artefacts, typical of outdoor scenes. The extracted objects are tracked using a statistical appearance model robust against occlusions and segmentation errors. The main novelty of this paper is the approach to consistent labeling. A specific Camera Transition Graph is adopted to efficiently select the possible correspondence hypotheses between labels. A Bayesian MAP optimization assigns consistent labels to objects detected by several points of views: the object axis is computed from the shape tracked in each camera module and homography and epipolar lines allow a correct axis warping in other image planes. Both forward and backward probability contributions from the two different warping directions make the approach robust against segmentation errors, and capable of disambiguating groups of people. The system has been tested in a real setup of a urban public park, within the Italian LAICA (Laboratory of Ambient Intelligence for a friendly city) project. The experiments show how the system can correctly track and label objects in a distributed system with real-time performance. Comparisons with simpler consistent labeling methods and extensive outdoor experiments with ground truth demonstrate the accuracy and robustness of the proposed approach.

We present in this paper an elephant photo identification system based on the shape comparison of the nicks characterizing the elephants' ears. The method we propose can deal with very cluttered and noisy images as the ones commonly used by zoologists for wild elephant photo identification. Difficult segmentation problems are solved using rough position information input by the user. Such information is used by the system as a basis for a set of segmentation and normalization hypotheses aiming at comparing a query photo Q with different photos of the system's database possibly representing the same individual as Q. The proposed shape comparison method, based on matching multiple, non-connected curves, can be applied to different retrieval by shape problems. Examples with real wild elephant photos are shown. (C) 2007 Elsevier Ltd. All rights reserved.