Publications

This paper describes the design and development of a Veiling Luminance estimation system based on the use of a CMOS image sensor, fully implemented on FPGA. The system is composed of the CMOS Image sensor, FPGA, DDR SDRAM, USB controller and SPI (Serial Peripheral Interface) Flash. The FPGA is used to build a system-on-chip integrating a soft processor (Xilinx MicroBlaze) and all the hardware blocks needed to handle the external peripherals and memory. The soft processor is used to handle image acquisition and all computational tasks need to compute the Veiling Luminance value. The advantages of this single chip FPGA implementation include the reduction of the hardware requirements, power consumption, and system complexity. The problem of the high dynamic range images have been addressed with multiple acquisitions at different exposure times. Vignetting, radial distortion and angular weighting, as required by veiling luminance definition, are handled by a single integer look-up table (LUT) access. Results are compared with a state of the art certified instrument.

Nearly two decades have passed since the publication of the first study reporting the discovery of microRNAs (miRNAs). The key role of miRNAs in post-transcriptional gene regulation led to the performance of an increasing number of studies focusing on origins, mechanisms of action and functionality of miRNAs. In order to associate each miRNA to a specific functionality it is essential to unveil the rules that govern miRNA action. Despite the fact that there has been significant improvement exposing structural characteristics of the miRNA-mRNA interaction, the entire physical mechanism is not yet fully understood. In this respect, the development of computational algorithms for miRNA target prediction becomes increasingly important. This manuscript summarizes the research done on miRNA target prediction. It describes the experimental data currently available and used in the field and presents three lines of computational approaches for target prediction. Finally, the authors put forward a number of considerations regarding current challenges and future directions.

The aim of this work was to design and implement a new bioinformatics software which is able to identify the protein peptides from the peaks which arise from in-source or MS/MS fragmentation. The oligopeptide fraction was extracted from Fontina cheese at different ages of ripening and subsequently analyzed by LC/MS/MS. On the resulting total ion chromatograms, the peptides were identified by a method based both on the in-source fragmentation detectable with a single-quadrupole mass analyzer and by a new software which was developed. This software performs an in-silico digestion of the major milk proteins, it calculates all the possible peptide fragments which are generated by the loss of the first N- or C-terminal amino acids, and finally, it matches the experimental ion chromatogram with the in-silico which generated theoretical spectrum to identify the exact amino-acid protein sequence of the unknown oligopeptide. With this tool, the useful insights into the proteolytic processes which occur during Fontina cheese aging are obtained, which leads to a better knowledge about the functional features of the proteolysis end product.

For this demo, we present a set of stark applications designed to evaluate the performance of a color similarity retrieval system against human operators performance in the same tasks. The proposed series of tests give some interesting insights about the perception of color classes and the reliability of manual annotation in the fashion context. © 2012 Springer-Verlag.

In this article, we present a computational multiscale model for the characterization of subcellular proteins. The model is encoded inside a simulation tool that builds coarse-grained (CG) force fields from atomistic simulations. Equilibrium molecular dynamics simulations on an all-atom model of the actin filament are performed. Then, using the statistical distribution of the distances between pairs of selected groups of atoms at the output of the MD simulations, the force field is parameterized using the Boltzmann inversion approach. This CG force field is further used to characterize the dynamics of the protein via Brownian dynamics simulations. This combination of methods into a single computational tool flow enables the simulation of actin filaments with length up to 400 nm, extending the time and length scales compared to state-of-the-art approaches. Moreover, the proposed multiscale modeling approach allows to investigate the relationship between atomistic structure and changes on the overall dynamics and mechanics of the filament and can be easily (i) extended to the characterization of other subcellular structures and (ii) used to investigate the cellular effects of molecular alterations due to pathological conditions.

The recognition of people orientation in single images is still an open issue in several real cases, when the image resolution is poor, body parts cannot be distinguished and localized or motion cannot be exploited. However, the estimation of a person orientation, even an approximated one, could be very useful to improve people tracking and re-identification systems, or to provide a coarse alignment of body models on the input images. In these situations, holistic features seem to be more effective and faster than model based 3D reconstructions. In this paper we propose to describe the people appearance with multi-level HoG feature sets and to classify their orientation using an array of Extremely Randomized Trees classifiers trained on quantized directions. The outputs of the classifiers are then integrated into a global continuous probability density function using a Mixture of Approximated Wrapped Gaussian distributions. Experiments on the TUD Multiview Pedestrians, the Sarc3D, and the 3DPeS datasets confirm the efficacy of the method and the improvement with respect to state of the art approaches.

In this paper we present a novel algorithm to synthesize an optimal decision tree from OR-decision tables, an extension of standard decision tables, complete with the formal proof of optimality and computational cost analysis. As many problems which require to recognize particular patterns can be modeled with this formalism, we select two common binary image processing algorithms, namely connected components labeling and thinning, to show how these can be represented with decision tables, and the benets of their implementation as optimal decision trees in terms of reduced memory accesses. Experiments are reported, to show the computational time improvements over state of the art implementations.

Artistic image collections are usually managed via textual metadata into standard content management systems. More sophisticated searches can be performed using image retrieval technologies based on visual content. Nevertheless, the problem of the information presentation remains. In this paper we try to move beyond the classic grid-styled presentation model, suggesting a novel use of relevance feedback as a navigation tool. Relevance feedback is therefore used to warp the view and allow the user to spatially navigate the image collection, and at the same time focus on his retrieval aim. This is obtained exploiting a distance based space warping on the 2D projection of the distance matrix. Multitouch gestures are employed to provide feedbacks by natural interaction with the system.