63 research outputs found
Foreground Detection Optimization for SoCs embedded on Smart Cameras
No full textIn this paper we study the effectiveness of a set of optimizations applied on a foreground detection and background maintainance algorithm. The optimizations were specifically devised to run in real time on hardware architectures embedded on commercial smart cameras. In order to achieve these aims we focused our attention on two kinds of optimizations based on the elimination of floatingpoint operations and the adoption of SIMD instructions. The optimized version of the algorithm has been tested on two RISC architectures (CRISv32 and MIPS 32Kc) considering different stream resolutions. The results confirm the effectiveness of the proposed solutions, which allows to process in real-time up to VGA resolution
Strategie discorsive "orfiche" e polemica cristiana. Note su Orig., Cels. 1,16 e Aug., C. Faust. Man. 13,1-2
No full textThis essay aims at analyzing the re-use and the polemic re-proposition of some preexisting Orphic discursive fragments in Origen's Contra Celsum 1.16 and Augustine's Contra Faustum Manichaeum 13.1-2
An efficient and effective method for people detection from top-view depth cameras
No full textThe detection of persons from videos is particularly important in many computer vision contexts being an enabling technology for several relevant applications either for security and safety or for business intelligence purposes. The adoption of a depth sensor mounted in a top-view position is often used to achieve high person detection accuracy as it allows to cope effectively with occlusions and difficult lighting conditions. In this paper, we propose a new method for people detection from depth maps produced by sensors mounted in a zenithal position. The method is designed with the aim of providing an optimal trade off between the detection accuracy and the computational complexity. The proposed approach adopts a dynamic background modeling strategy in order to find the objects of interest into the scene; then a lightweight algorithm is used to filter out the noise from the foreground image and to determine the position of the persons into the scene. The experimental analysis carried out on a public and large dataset allowed to demonstrate that the method is fast and accurate. The method has been compared with respect to two different approaches available in the literature for people detection from a depth camera mounted in a zenithal position: an unsupervised method that is fast although not highly accurate, and a supervised one that conversely is very accurate but less computationally efficient. The proposed method allows to achieve comparable accuracy of the supervised approach using very few computational resources, with a reduction of an order of magnitude of the processing times
Benchmarking two algorithms for people detection from top-view depth cameras
No full textAutomatic people detection from videos is an important task in many computer vision applications either for security and safety motivations or for business intelligence purposes. In order to achieve high person detection accuracy many authors propose the adoption of a depth sensor mounted in a top-view position in order to mitigate the effects of occlusions and illumination conditions on the performance. Unfortunately, most approaches presented so far in the scientific literature have been tested on very small datasets which do not account for the typical situations arising in real scenarios and consequently do not allow interested readers to figure out which method has to be used in the specific scenario at hand. In this paper we benchmark two different approaches available in the literature for people detection from a zenithal mounted depth camera; the former is an unsupervised method aimed at finding the head of persons defined as the local minimum regions in the depth map, while the latter is based on the combination of the histograms of oriented gradient description and the support vector machine classifier. The benchmarking is performed on a public dataset of images captured in two different lighting conditions and with varying number of persons; this allows to assess the performance of the considered approaches under different real world scenarios. A detailed analysis of the two methods is reported in the experimental section of the paper allowing the reader to comprehend the pros and cons of each approach on the considered scenes
Addizione stereoselettiva di michael a ß-nitrostireni in assenza di solvente catalizzata da ammine chirali
No full textSolving Fractional Polynomial Problems by Polynomial Optimization Theory
No full textThis letter aims at introducing the framework of polynomial optimization theory to solve fractional polynomial problems (FPPs). Unlike other widely used optimization frameworks, the proposed one applies to a larger class of FPPs, not necessarily defined by concave and/or convex functions. An iterative algorithm that is provably convergent and enjoys asymptotic optimality properties is proposed. Numerical results are used to validate its accuracy in the nonasymptotic regime when applied to the energy efficiency maximization in multiuser multiple-input multiple-output communication systems
MANATEE Project: Monitoring and Mapping of Marine Habitat with Integrated Geomatics Technologies
No full textItalian seas are home to a unique heritage of biodiversity in terms of species and habitats and are protected by EU conventions and directives. To preserve this richness effectively, monitoring activities are key to assess its state of health and evolution, and to enhance our current knowledge of natural processes and stress factors. Where this heritage is compromised, restoration projects can be undertaken, operations requiring the application of technical and scientific methods to ensure robust, reliable and cost-effective data collection. The ensemble of Geomatics techniques can provide valuable support for marine habitat monitoring, in the form of: localization, navigation and mapping of the site of interest following autonomous or guided approaches; generation of digital twins (3D models) of the habitat at the required resolution and accuracy; extraction from the digital twins of statistically significant metrics to assess the time evolution; presentation and sharing of the results with both the scientific community and the general public to promote awareness of environmental protection issues. MANATEE (Monitoring and mApping of mariNe hAbitat with inTegrated gEomatics technologiEs) project is providing these monitoring solutions via the integration of underwater photogrammetry with auxiliary positioning and navigation techniques based on acoustic, pressure and inertial sensors. The developed approaches is implemented in three complementary underwater vehicles, differing in cost, weight and portability, number and grade of navigation, positioning and 3D modelling sensors, and designed to cover habitats different for extension and depth. An observation class UUV (Unmanned Underwater Vehicle), a low-cost micro ROV (Remotely Operated Vehicle), and a 3D surveying and modelling device for scuba divers will be tested in a real-world experiment focusing on the restoration of a crustose coralline algae, Lithophyllum stictiforme
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