3D mudeli koostamine Kinect v2 kaamera abil

Kinect is an easy to use and a ordable RGB-D acquisition device that provides both spatial and color information for captured pixels. That makes it an attractive alternative to regular 3D scanning devices that usually cost signi cantly more and do not provide color info. Second generation of Kinect (v2) provides even better quality depth and color images to user. This thesis describes and implements method for 3D reconstruction using Kinect v2. Method suitability for various objects is tested and analyzed. In most circumstances the method provided satisfactory reconstructions unless very high resolution is desired. However some limitation were observed. Reflective and transparent surfaces cause failure due to depth capturing technology in Kinect v2, symmetric objects cause problems for described frame registration algorithm. For better understanding, Kinect v2 depth measuring process is described

Usable and Sound Static Analysis through its Integration into Automated and Interactive Workflows

Staatiline analüüs võimaldab tarkvara arendajal tuvastada koodis leiduvaid viguning neid parandada enne, kui see jõuab reaalsesse kasutusse. Hoolimata sellest, et tänaseks päevaks on teada mitmeid häid analüüsimeetodeid, põhjustavad ennetatavad tarkvara vead siiski katkestusi kriitiliste rakenduste töös ning võimaldavad kolmandatel isikutel ligipääsu privaatsetele andmetele. Kuigi arendajad on teadlikud staatilise analüüsi kasutamise eelistest, takistavad mitmed asjaolud siiski selliste vahendite laialdasemat kasutuselevõttu. Üheks peamiseks probleemiks on anaüüsi vahendite keerukas ning tüütukasutatavus. Veelgi suuremat vastuseisu kohtavad korrektse (sound) staatilise anaüüsi vahendid, mis lubaksid potentsiaalselt kontrollida teatud tüüpi vigade puudumist programmis. Nende suureks miinuseks on võimalus vigade (valesti) tuvastamiseks ka osades tegelikult korrektsetes programmides.Käesolevas magistritöös uuritakse, mis viisil kasutatakse staatilise analüüsi vahendeid ettevõtetes ning pakutakse välja, kuidas oleks mõistlik integreerida analüüsi tarkvara arenduskeskkonda (IDE) ning tarkvara ehitust automatiseerivasse töövahendisse (build tool). Interaktiivse analüüsi ja automatiseeritud analüüsi tugev integreeritus võib ollaoluline komponent, mis paneks arendajad neid töövahendeid kasutama.Töö tulemusena valmis ka näidislahendus, mis integreerib lekke analüüsi (taintanalysis) IntelliJ ja Gradle töövahenditesse. Välja pakutud lahendus on sobilik lekke analüüsi jaoks, aga selle üldistamine keerulisemate analüüsimeetodite jaoks jääb lahtiseks probleemiks. Näidislahenduse arendus andis võimaluse uurida erinevaid lähenemisi kasutatavusele ning on kasulikuks esimeseks sammuks suurema lõppeesmärgi poole, milleks on kasutajasõbraliku korrektse staatilise analüüsivahendi loomine.Static analysis allows software developers to detect and fix many types of errors in codebefore it is submitted to a production environment. Despite the availability of sophisticatedanalysis techniques, many preventable bugs still cause security vulnerabilitiesthat allow hackers to steal private information. Studies have shown that even thoughdevelopers recognize the benefits of static analysis there are many practical usabilityproblems preventing higher adoption rates.The challenge is even greater with sound analyzers that could potentially verify thetotal absence of specific types of bugs, but at the cost of rejecting some correct programs.This thesis investigates the current situation of adopting static analyzers in the industryand proposes an approach of integrating an analysis into the IDE and build system. Theseamless integration of both interactive and automated analysis may enable developersto adopt sound analysis tools.A prototype implementation of that static analysis workflow for tainting analysisin IntelliJ and Gradle is presented. The integration proposed works well for taintinganalysis used in the prototype, but many challenges remain to generalize this to morecomplex analyses. The prototype has enabled the exploration of different approachesto usability and is a useful first step in a larger project aimed at building a user-friendlysound static analysis framework

3D face reconstruction with region based best fit blending using mobile phone for virtual reality based social media

The use of virtual reality (VR) has been exponentially increasing and due to that many researchers have started to work on developing new VR based social media. For this purpose it is important to have an avatar of the user which look like them to be easily generated by the devices which are accessible, such as mobile phones. In this paper, we propose a novel method of recreating a 3D human face model captured with a phone camera image or video data. The method focuses more on model shape than texture in order to make the face recognizable. We detect 68 facial feature points and use them to separate a face into four regions. For each area the best fitting models are found and are further morphed combined to find the best fitting models for each area. These are then combined and further morphed in order to restore the original facial proportions. We also present a method of texturing the resulting model, where the aforementioned feature points are used to generate a texture for the resulting model

A new kernel development algorithm for edge detection using singular value ratios

The perceptual quality of an image is very sensitive to the degradation of the edge information which is usually caused by many video signal applications such as super-resolution and denoising. Hence, it is very important to detect and enhance the edge information of the image. In this research work, new sets of kernels for edge detection using ratios of singular values of an image are proposed, which results in more detailed detection of edges in the original image. The parameters, which are the elements of kernel matrices and the threshold value used for producing binary image after convolving the kernels with the image of the proposed method, are optimised to achieve more detailed edge detection of the image. The experimental results show that more detailed edges are detected by the proposed method compared to the conventional edge detection techniques