Publications

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.

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.

The analysis of patterns of movement is a crucial task for several surveillance applications, for instance to classify normal or abnormal people trajectories on the basis of their occurrence. This paper proposes to model the shape of a single trajectory as a sequence of angles described using a Mixture of Von Mises (MoVM) distribution. A complete EM (Expectation Maximization) algorithm is derived for MoVM parameters estimation and an on-line version proposed to meet real time requirement. Maximum-A-Posteriori is used to encode the trajectory as a sequence of symbols corresponding to the MoVM components. Iterative k-medoids clustering groups trajectories in a variable number of similarity classes. The similarity is computed aligning (with dynamic programming) two sequences and considering as symbol-to-symbol distance the Bhattacharyya distance between von Mises distributions. Extensive experiments have been performed on both synthetic and real data. ©2008 IEEE.

Object tracking with freely moving cameras is an openissue, since background information cannot be exploited forforeground segmentation, and plain feature tracking is notrobust enough for target tracking, due to occlusions, distractors and object deformations. In order to deal withsuch challenging conditions a traditional approach, basedon Camshift-like color-based features, is augmented by introducing a structural model of the object to be tracked incorporating previous knowledge about the spatial relationsbetween the parts. Hence, an attributed graph is built ontop of the features extracted from each frame and a graphmatching technique is used to extract the optimal matchwith the model. Pixel-wise and object-wise comparisonwith other tracking techniques with respect to manually obtained ground truth are presented.

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.