Pulsed laser deposition of organic and biological materials

We report on the deposition of soft matter thin films by Matrix Assisted Pulsed Laser Evaporation (MAPLE). In particular, thin layers of biological material (Bovine Serum Albumin) and polymers (polyfluorene) for medical and optoelectronic applications, were realized by laser irradiating a frozen solution containing a low amount of material diluted in a laser absorbing volatile solvent. The depositions were carried out varying different parameters as solvent–solute concentration, solvent nature, laser fluencies, etc. The optical, morphological, structural and spectroscopical properties were detected by means of different analyses as FTIR, photoluminescence, AFM and SDS

Lifestyles and socio-cultural factors among children aged 6-8 years from five Italian towns: The MAPEC-LIFE study cohort

Background: Lifestyles profoundly determine the quality of an individual’s health and life since his childhood. Many diseases in adulthood are avoidable if health-risk behaviors are identified and improved at an early stage of life. The aim of the present research was to characterize a cohort of children aged 6–8 years selected in order to perform an epidemiological molecular study (the MAPEC_LIFE study), investigate lifestyles of the children that could have effect on their health status, and assess possible association between lifestyles and socio-cultural factors. Methods: A questionnaire composed of 148 questions was administered in two different seasons to parents of children attending 18 primary schools in five Italian cities (Torino, Brescia, Pisa, Perugia and Lecce) to obtain information regarding the criteria for exclusion from the study, demographic, anthropometric and health information on the children, as well as some aspects on their lifestyles and parental characteristics. The results were analyzed in order to assess the frequency of specific conditions among the different seasons and cities and the association between lifestyles and socio-economic factors. Results: The final cohort was composed of 1,164 children (50.9 boys, 95.4% born in Italy). Frequency of some factors appeared different in terms of the survey season (physical activity in the open air, the ways of cooking certain foods) and among the various cities (parents’ level of education and rate of employment, sport, traffic near the home, type of heating, exposure to passive smoking, ways of cooking certain foods). Exposure to passive smoking and cooking fumes, obesity, residence in areas with heavy traffic, frequency of outdoor play and consumption of barbecued and fried foods were higher among children living in families with low educational and/or occupational level while children doing sports and consuming toasted bread were more frequent in families with high socio-economic level. Conclusions: The socio-economic level seems to affect the lifestyles of children enrolled in the study including those that could cause health effects. Many factors are linked to the geographical area and may depend on environmental, cultural and social aspects of the city of residence

3D object recognition with trains of keypoints

We present a method for 3D object modeling and recognition which is robust to scale and illumination changes, and to viewpoint variations. The object model is derived from the local keypoints extracted and tracked from an image sequence of the object. The recognition phase is based on an SVM classifier. To check for the presence of the object in an image we represent the image with respect to the object’s model and then classify it. We analyse in depth all the crucial steps of the method, and report very promising results on a dataset of 11 objects, that show how the method is also tolerant to occlusions and moderate scene clutter

Trains of keypoints for 3d object recognition

International audienceThis paper presents a 3D object recognition method that exploits the spatio-temporal coherence of image sequences to capture the object most relevant features. We start from an image sequence that describes the object's visual appearance from different view points. We extract local features (SIFT) and track them over the sequence. The tracked interest points form trains of features that are used to build a vocabulary for the object. Training images are represented with respect to that vocabulary and an SVM classier is trained to recognize the object. We present very promising results on a dataset of 11 objects. Tests are performed under varying illumination, scale, and scene clutter

SVD-matching using SIFT features

The paper tackles the problem of feature points matching between pair of images of the same scene. This is a key problem in computer vision. The method we discuss here is a version of the SVD-matching proposed by Scott and Longuet-Higgins and later modified by Pilu, that we elaborate in order to cope with large scale variations. To this end we add to the feature detection phase a keypoint descriptor that is robust to large scale and view-point changes. Furthermore, we include this descriptor in the equations of the proximity matrix that is central to the SVD-matching. At the same time we remove from the proximity matrix all the information about the point locations in the image, that is the source of mismatches when the amount of scene variation increases. The main contribution of this work is in showing that this compact and easy algorithm can be used for severe scene variations. We present experimental evidence of the improved performance with respect to the previous versions of the algorithm

Analysis on a local approach to 3D object recognition

Abstract. We present a method for 3D object modeling and recognition which is robust to scale and illumination changes, and to viewpoint variations. The object model is derived from the local features extracted and tracked on an image sequence of the object. The recognition phase is based on an SVM classifier. We analyse in depth all the crucial steps of the method, and report very promising results on a dataset of 11 objects, that show how the method is also tolerant to occlusions and moderate scene clutter.

Analysis on a local approach to 3d object recognition

International audienceWe present a method for 3D object modeling and recogni- tion which is robust to scale and illumination changes, and to viewpoint variations. The object model is derived from the local features extracted and tracked on an image sequence of the object. The recognition phase is based on an SVM classier. We analyse in depth all the crucial steps of the method, and report very promising results on a dataset of 11 objects, that show how the method is also tolerant to occlusions and moderate scene clutter