Rõivaste tekstureerimine kasutades Kinect V2.0

This thesis describes three new garment retexturing methods for FitsMe virtual fitting room applications using data from Microsoft Kinect II RGB-D camera. The first method, which is introduced, is an automatic technique for garment retexturing using a single RGB-D image and infrared information obtained from Kinect II. First, the garment is segmented out from the image using GrabCut or depth segmentation. Then texture domain coordinates are computed for each pixel belonging to the garment using normalized 3D information. Afterwards, shading is applied to the new colors from the texture image. The second method proposed in this work is about 2D to 3D garment retexturing where a segmented garment of a manikin or person is matched to a new source garment and retextured, resulting in augmented images in which the new source garment is transferred to the manikin or person. The problem is divided into garment boundary matching based on point set registration which uses Gaussian mixture models and then interpolate inner points using surface topology extracted through geodesic paths, which leads to a more realistic result than standard approaches. The final contribution of this thesis is by introducing another novel method which is used for increasing the texture quality of a 3D model of a garment, by using the same Kinect frame sequence which was used in the model creation. Firstly, a structured mesh must be created from the 3D model, therefore the 3D model is wrapped to a base model with defined seams and texture map. Afterwards frames are matched to the newly created model and by process of ray casting the color values of the Kinect frames are mapped to the UV map of the 3D model

Flammability of Bio-Based Rigid Polyurethane Foam as Sustainable Thermal Insulation Material

One of the biggest disadvantages of rigid polyurethane foams is its low thermal resistance, high flammability, and high smoke production when burning. Greatest advantage of this thermal insulation material is its low thermal conductivity, which at 20–25 mW/(m·K) is superior to other commercially available insulation materials. In recent years polyurethane materials from renewable resources have been widely studied. But their use on industrial scale was limited due to inconstant performance and relatively high price of raw materials. Different bio-based raw materials, such as rapeseed oil and tall oil, could provide abundant feedstock for PU foam production. Decrease of flammability of PU materials conventionally is achieved by addition of flame retardants, halogen-containing compounds, and phosphates. It can be considered that halogenated fire retardants could have several health hazards, such as volatile compound emission from materials and toxic gas release during burning process. Expandable graphite could be an answer to this flammability problem. This chapter describes development of bio-based rigid polyurethane foams and their flammability reduction using sustainable flame retardants. Different expandable graphite intumescent flame retardants provided significant flammability reduction while maintaining low thermal conductivity of insulation materials

Ensemble approach for detection of depression using EEG features

Depression is a public health issue which severely affects one's well being and cause negative social and economic effect for society. To rise awareness of these problems, this publication aims to determine if long lasting effects of depression can be determined from electoencephalographic (EEG) signals. The article contains accuracy comparison for SVM, LDA, NB, kNN and D3 binary classifiers which were trained using linear (relative band powers, APV, SASI) and non-linear (HFD, LZC, DFA) EEG features. The age and gender matched dataset consisted of 10 healthy subjects and 10 subjects with depression diagnosis at some point in their lifetime. Several of the proposed feature selection and classifier combinations reached accuracy of 90% where all models where evaluated using 10-fold cross validation and averaged over 100 repetitions with random sample permutations.Comment: 8 pages, 2 figure

Foot problems of female medical personnelSubjective complaints and results of the podometric test

The research was carried out in a multi-profile clinic where female medical workers included nurses, doctors and nurse assistants. The number of respondents included in the data analysis of this research was 102. A standardised questionnaire was used to obtain data on age group, position, body mass index, physical activities, ergonomic factors at work, performed objective foot examination methods, awareness of the ways of feet deformation correction. A computerised foot diagnostic system Pad Professional was used to objectively assess the feet condition. The podometric examination was carried out on 78 respondents. The data indicated a widespread foot problem spread among medical workers. Of 102 respondents only 10 (4.2%) had no complaints, and of 78 respondents who underwent the podometric test, none were diagnosed as having a totally healthy foot. The podometric examination showed that 65 (83.3%), which was the vast majority of respondents, had transverse arch flattening. Explicit transverse arch flattening was diagnosed in 8 (10.3%) and longitudinal arch flattening in 5 (6.4%) respondents.publishersversionPeer reviewe

Privacy-Constrained Biometric System for Non-cooperative Users

With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject's hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance