Publications

This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologies.

In this paper a system for illuminated manuscripts images analysis is presented. In particular the bag-of-keypoints strategy, commonly adopted for object recognition, image classification and scene recognition, is applied to the classification of automatically extracted miniatures. Pictures are characterized by SURF descriptors, and a classification procedure is performed, comparing the results of Naive Bayes and histogram intersection distance measures.

We propose a new efficient approach for neighborhood exploration, optimized with decision tables and decision trees, suitable for local algorithms in image processing. In this work, it is employed to speed up two widely used thinning techniques. The performance gain is shown over a large freely available dataset of scanned document images.

Surveillance of wide areas with several connected cameras integrated in the same automatic system is no more a chimera, but modular, scalable and flexible architectures are mandatory to manage them. This paper points out the main issues on the development of distributed surveillance systems and proposes an integrated framework particularly suitable for research purposes. As first, exploiting a computer architecture analogy, a three layer tracking system is proposed, which copes with the integration of both overlapping and non overlapping cameras. Then, a static service oriented architecture is adopted to collect and manage the plethora of high level modules, such as face detection and recognition, posture and action classification, and so on. Finally, the overall architecture is controlled by an event driven communication infrastructure, which assures the scalability and the flexibility of the system.

Two of the most important state-of-the-art challenges in face recognition are: dealing with image acquisition conditions very different between the gallery and the probe set and dealing with large datasets of individuals. In this paper we face both aspects presenting a method which is able to work in “real life” scenarios, in which face images are differently illuminated, can be partially occluded or can show different facial expressions or noise levels. Our proposed system has been tested with datasets of 1000 different individuals, showing performances usually obtained with much smaller gallery sets and much better images. The approach we propose is based on SIFT descriptors, which are known to be robust to different illumination conditions and noise levels. SIFTs are used to automatically detect face regions (mouth area, eye area, etc.). Such regions are then independently compared with the corresponding regions of the gallery images for computing a similarity-based renking of the system’s database. © 2010 Taylor & Francis Group, LLC.

In this paper, we present a new and fast techniquefor background estimation from cluttered image sequences.Most of the background initialization approaches developedso far collect a number of initial frames and then requirea slow estimation step which introduces a delay wheneverit is applied. Conversely, the proposed technique redistributesthe computational load among all the frames bymeans of a patch by patch preprocessing, which makesthe overall algorithm more suitable for real-time applications.For each patch location a prototype set is created andmaintained. The background is then iteratively estimatedby choosing from each set the most appropriate candidatepatch, which should verify a sort of frequency coherencewith its neighbors. To this aim, the Hadamard transformhas been adopted which requires less computation time thanthe commonly used DCT. Finally, a refinement step exploitsspatial continuity constraints along the patch borders toprevent erroneous patch selections. The approach has beencompared with the state of the art on videos from availabledatasets (ViSOR and CAVIAR), showing a speed up of about10 times and an improved accuracy

Nowadays the need for powerful hardware architectures, which allow for high throughput data analysis and calculus, is fundamental especially for biological applications. We have been focused on utilizing the Graphic Processing Unit (GPU) architectures of NVIDIA for accelerating a lipid bilayer simulation tool for biomembranes. ©2010 IEEE.

This paper proposes a comparison of the two most advanced algorithms for connected components labeling, highlighting how they perform on a soft core SoC architecture based on FPGA. In particular we test our block based connected components labeling algorithm, optimized with decision tables and decision trees. The embedded system is composed of the CMOS image sensor, FPGA, DDR SDRAM, USB controller and SPI Flash. Results highlight the importance of caching and instructions and data cache sizes for high performance image processing tasks.

Hidden Markov Models (HMM) have been widely used for action recognition, since they allow to easily model the temporal evolution of a single or a set of numeric features extracted from the data. The selection of the feature set and the related emission probability function are the key issues to be defined. In particular, if the training set is not sufficiently large, a manual or automatic feature selection and reduction is mandatory. In this paper we propose to model the emission probability function as a Mixture of Gaussian and the feature set is obtained from the projection histograms of the foreground mask. The projectionhistograms contain the number of moving pixel for each row and for each column of the frame and they provide sufficient information to infer the instantaneous posture of the person. Then, the HMM framework recovers the temporal evolution of the postures recognizing in such a manner the global action. The proposed method have been successfully tested on the UT-Tower and on the Weizmann Datasets.