HySenS data exploitation for urban land cover analysis

This paper addresses the use of HySenS airborne hyperspectral data for environmental urban monitoring. It is known that hyperspectral data can help to characterize some of the relations between soil composition, vegetation characteristics, and natural/artificial materials in urbanized areas. During the project we collected DAIS and ROSIS data over the urban test area of Pavia, Northern Italy, though due to a late delivery of ROSIS data only DAIS data was used in this work. Here we show results referring to an accurate characterization and classification of land cover/use, using different supervised approaches, exploiting spectral as well as spatial information. We demonstrate the possibility to extract from the hyperspectral data information which is very useful for environmental characterization of urban areas

Evaluation framework for crowd behaviour simulation and analysis based on real videos and scene reconstruction

This paper has been presented at : 6th Latin-American Conference on Networked and Electronic Media (LACNEM 2015)Crowd simulation has been regarded as an important research topic in computer graphics, computer vision, and related areas. Various approaches have been proposed to simulate real life scenarios. In this paper, a novel framework that evaluates the accuracy and the realism of crowd simulation algorithms is presented. The framework is based on the concept of recreating real video scenes in 3D environments and applying crowd and pedestrian simulation algorithms to the agents using a plug-in architecture. The real videos are compared with recorded videos of the simulated scene and novel Human Visual System (HVS) based similarity features and metrics are introduced in order to compare and evaluate simulation methods. The experiments show that the proposed framework provides efficient methods to evaluate crowd and pedestrian simulation algorithms with high accuracy and low cost

HySenS data exploitation for urban land cover analysis

This paper addresses the use of HySenS airborne hyperspectral data for environmental urban monitoring. It is known that hyperspectral data can help to characterize some of the relations between soil composition, vegetation characteristics, and natural/artificial materials in urbanized areas. During the project we collected DAIS and ROSIS data over the urban test area of Pavia, Northern Italy, though due to a late delivery of ROSIS data only DAIS data was used in this work. Here we show results referring to an accurate characterization and classification of land cover/use, using different supervised approaches, exploiting spectral as well as spatial information. We demonstrate the possibility to extract from the hyperspectral data information which is very useful for environmental characterization of urban areas

Risk analysis for smart homes and domestic robots using robust shape and physics descriptors, and complex boosting techniques

In this paper, the notion of risk analysis within 3D scenes using vision based techniques is introduced. In particular the problem of risk estimation of indoor environments at the scene and object level is considered, with applications in domestic robots and smart homes. To this end, the proposed Risk Estimation Framework is described, which provides a quantified risk score for a given scene. This methodology is extended with the introduction of a novel robust kernel for 3D shape descriptors such as 3D HOG and SIFT3D, which aims to reduce the effects of outliers in the proposed risk recognition methodology. The Physics Behaviour Feature (PBF) is presented, which uses an object's angular velocity obtained using Newtonian physics simulation as a descriptor. Furthermore, an extension of boosting techniques for learning is suggested in the form of the novel Complex and Hyper-Complex Adaboost, which greatly increase the computation efficiency of the original technique. In order to evaluate the proposed robust descriptors an enriched version of the 3D Risk Scenes (3DRS) dataset with extra objects, scenes and meta-data was utilised. A comparative study was conducted demonstrating that the suggested approach outperforms current state-of-the-art descriptors

Outcome measurement in the correction of mandibular asymmetry

This study related clinical assessments of the severity of mandibular asymmetry with computerized measurements, obtained by digitizing mandibular outlines from standardized facial photographs. Four ratios were calculated: area (size), compactness (shape), perimeter (length of outline), and moment (center of area). When comparing clinical severity with computer assessment, significant correlations were observed; those for area and compactness were the highest. Sixteen patients subsequently underwent corrective surgery, and their ratios were used to relate the degree of improvement to the original severity of the asymmetry. The posttreatment ratios were also used to audit the outcome, comparing the patients' scores as a group with results previously obtained from patients with normal symmetry and mild asymmetry. Posttreatment outcomes were significantly different from the normal outline group but were comparable with outcomes of patients with mild mandibular asymmetry. The system provided a sensitive, noninvasive method of assessing treatment change and could be useful in providing an objective means of quantifying treatment outcomes