Publications

In this paper we propose to integrate the recently introduces ORB descriptors in the currently favored approach for image classification, that is the Bag of Words model. In particular the problem to be solved is to provide a clustering method able to deal with the binary string nature of the ORB descriptors. We suggest to use a k-means like approach, called k-majority, substituting Euclidean distance with Hamming distance and majority selected vector as the new cluster center. Results combining this new approach with other features are provided over the ImageCLEF 2011 dataset.

Several papers addressed ellipse detection as a first step for several computer vision applications, but most of the proposed solutions are too slow to be applied in real time on large images or with limited hardware resources, as in the case of mobile devices. This demo is based on a novel algorithm for fast and accurate ellipse detection. The proposed algorithm relies on a careful selection of arcs which are candidate to form ellipses and on the use of Hough transform to estimate parameters in a decomposed space. The demo will show it working on a commercial smart-phone. © 2013 IEEE.

This paper deals with people counting in stores for business analytics using stereo vision. Among the several problems in this type of applications, two are the most relevant for our purposes: the management of occlusions and the distinction between adult people (potential customers) and other objects (children, trolleys, strollers, animals, etc.). The proposed solution uses a novel approach for object detection (based on background suppression on a so-called 'depth bird-eye view' and the clustering on the 3D point cloud by means of mean shift with a cylindrical kernel) followed by an adult people classifier which exploits a fitness measure with respect to a cylindrical human body model. The fitness is computed using Montecarlo sampling to estimate the volume occupation. Experiments are conducted on two real setups (including a store in a normal day of activity) and compared with a previous work. The results demonstrate the accuracy of the proposed solution. © 2013 IEEE.

Coarse grain (CG) molecular models have been proposed to simulate complex sys- tems with lower computational overheads and longer timescales with respect to atom- istic level models. However, their acceleration on parallel architectures such as Graphic Processing Units (GPU) presents original challenges that must be carefully evaluated. The objective of this work is to characterize the impact of CG model features on parallel simulation performance. To achieve this, we implemented a GPU-accelerated version of a CG molecular dynamics simulator, to which we applied specic optimizations for CG models, such as dedicated data structures to handle dierent bead type interac- tions, obtaining a maximum speed-up of 14 on the NVIDIA GTX480 GPU with Fermi architecture. We provide a complete characterization and evaluation of algorithmic and simulated system features of CG models impacting the achievable speed-up and accuracy of results, using three dierent GPU architectures as case studies.

The costs associated with the management of healthcare systems have been subject to continuous scrutiny for some time now, with a view to reducing them without affecting the quality as perceived by final users. A number of different solutions have arisen based on centralisation of healthcare services and investments in Information Technology (IT). One such example is centralised management of pharmaceuticals among a group of hospitals which is then incorporated into the different steps of the automation supply chain. This paper focuses on a new picking workstation available for insertion in automated pharmaceutical distribution centres and which is capable of replacing manual workstations and bringing about improvements in working time. The workstation described uses a sophisticated computer vision algorithm to allow picking of very diverse and complex objects randomly available on a belt or in bins. The algorithm exploits state-of-the-art feature descriptors for an approach that is robust against occlusions and distracting objects, and invariant to scale, rotation or illumination changes. Finally, the performance of the designed picking workstation is tested in a large experimentation focused on the management of pharmaceutical items.

In this paper, the problem of automatic people removal from digital photographs is addressed. Removing unintended people from a scene can be very useful to focus further steps of image analysis only on the object of interest, A supervised segmentation algorithm is presented and tested in several scenarios.

Several local features have become quite popular for concept detection and search, due to their ability to capture distinctive details. Typically a Bag of Words approach is followed, where a codebook is built by quantizing the local features. In this paper, we propose to represent SIFT local features extracted from an image as a multivariate Gaussian distribution, obtaining a mean vector and a covariance matrix. Differently from common techniques based on the Bag of Words model, our solution does not rely on the construction of a visual vocabulary, thus removing the dependence of the image descriptors on the specific dataset and allowing to immediately retargeting the features to different classification and search problems. Experimental results are conducted on two very different Cultural Heritage image archives, composed of illuminated manuscript miniatures, and architectural elements pictures collected from the web, on which the proposed approach outperforms the Bag of Words technique both in classification and retrieval.