Development of a system for 3D reconstruction of objects using passive computer vision methods

The main goal of the master thesis is to develop a system for reconstruction of 3D objects from colour images. The main focus is on passive computer vision methods from which we select two, i.e., Stereo vision and Space carving. Both methods require information about camera poses. The camera pose for a given image is estimated from the information obtained by detecting a reference object, i.e., a standard A4 paper sheet. We develop an Android based mobile application to guide a user during image capturing process and a Matlab based system for 3D object reconstruction from captured images. The reconstructed 3D models of objects where compared to reference 3D models. Using an evaluation metodology we propose, we evaluated the implemented methods and analysed the results. We also present qualitative results and highlight strengths and shortcomings of the two selected passive computer vision methods for 3D reconstruction

Development of a system for 3D reconstruction of objects using passive computer vision methods

The main goal of the master thesis is to develop a system for reconstruction of 3D objects from colour images. The main focus is on passive computer vision methods from which we select two, i.e., Stereo vision and Space carving. Both methods require information about camera poses. The camera pose for a given image is estimated from the information obtained by detecting a reference object, i.e., a standard A4 paper sheet. We develop an Android based mobile application to guide a user during image capturing process and a Matlab based system for 3D object reconstruction from captured images. The reconstructed 3D models of objects where compared to reference 3D models. Using an evaluation metodology we propose, we evaluated the implemented methods and analysed the results. We also present qualitative results and highlight strengths and shortcomings of the two selected passive computer vision methods for 3D reconstruction

Towards an Environment for Efficient and Transparent Virtual Machine Operations: The ENTICE Approach

Cloud computing is based on Virtual Machines (VM) or containers, which provide their own software execution environment that can be deployed by facilitating technologies on top of various physical hardware. The use of VMs or containers represents an efficient way to automatize the overall software engineering and operation life-cycle. Some of the benefits include elasticity and high scalability, which increases the utilization efficiency and decreases the operational costs. VMs or containers as software artifacts are created using provider-specific templates and are stored in proprietary or public repositories for further use. However, technology specific choices may reduce their portability, lead to a vendor lock-in, particularly when applications need to run in federated Clouds. In this paper we present the current state of development of the novel concept of a VM repository and operational environment for federated Clouds named ENTICE. The ENTICE environment has been designed to receive unmodified and functionally complete VM images from its users, and transparently tailor and optimise them for specific Cloud infrastructures with respect to their size, configuration, and geographical distribution, such that they are loaded, delivered, and executed faster and with improved QoS compared to their current behaviour. Furthermore, in this work a specific use case scenario for the ENTICE environment has been provided and the underlying novel technologies have been presented

Development of a system for 3D reconstruction of objects using passive computer vision methods

Cilj magistrskega dela je bil razvoj sistema za 3-dimenzionalno rekonstrukcijo predmetov iz slik. Osredotočili smo se na pasivne metode računalniškega vida in izbrali metodi Stereo vid ter Prostorsko klesanje. Pri uporabi obeh metod morajo biti poznane lege kamere iz katerih so bile slike zajete. Za določitev lege kamere smo uporabili informacijo pridobljeno z detekcijo referenčnega objekta - lista formata A4. Razvili smo sistem, ki uporabnika vodi pri zajemanju slik s pametno mobilno napravo osnovano na platformi Android, nato pa iz zajetih slik zgradi 3D model predmeta v procesnem delu implementiranem v razvojnem okolju Matlab. Tako dobljene 3D modele smo primerjali z referenčnimi, pri čemer smo predlagali ter razvili evalvacijsko metodologijo in napake podrobno preučili ter rezultate ovrednotili. Predstavili smo tudi kvalitativne rezultate na katerih smo izpostavili prednosti in omejitve izbranih metod.The main goal of the master thesis is to develop a system for reconstruction of 3D objects from colour images. The main focus is on passive computer vision methods from which we select two, i.e., Stereo vision and Space carving. Both methods require information about camera poses. The camera pose for a given image is estimated from the information obtained by detecting a reference object, i.e., a standard A4 paper sheet. We develop an Android based mobile application to guide a user during image capturing process and a Matlab based system for 3D object reconstruction from captured images. The reconstructed 3D models of objects where compared to reference 3D models. Using an evaluation metodology we propose, we evaluated the implemented methods and analysed the results. We also present qualitative results and highlight strengths and shortcomings of the two selected passive computer vision methods for 3D reconstruction

Visualization of genetic algorithms in three-dimensional space

The goal of this work is an application for visualization of genetic algorithms on a simple problem in two-dimensional and three-dimensional space. The purpose is to improve comprehension of genetic algorithms. We present the history of genetic algorithms, the methodology with detailed description of all phases of the algorithm and its applications. We describe a problem that we are solving with genetic algorithm. We visualize the solving process, describe the work of graphical user interface and parameters of the algorithm. We describe the role of the genetic algorithm in the application and the role of Bezier curves. We also represent the most important technologies and tools that were used in the developement of the application: Java programming language, Java 3D API library, IntelliJ Idea integrated development envioronment, JFormDesigner tools for making of the graphical user interface, the tools for making and managing the software project Apache Maven and the tools for controlling source code TortoiseSVN. We describe the whole implementation of the problem step by step by making a graphic user interface, Step by step we describe the implementation. We describe the implementation of java library Timing Framework, an architecture of java 3D animation in application and implementation of Maven tool, used for creating the application. In the last chapter we present some interesting solutions and the behavior of the algorithm when solving the problem in two dimensional and three-dimensional space. In conclusion we suggest some ideas for further improvements

Development of a system for 3D reconstruction of objects using passive computer vision methods

Cilj magistrskega dela je bil razvoj sistema za 3-dimenzionalno rekonstrukcijo predmetov iz slik. Osredotočili smo se na pasivne metode računalniškega vida in izbrali metodi Stereo vid ter Prostorsko klesanje. Pri uporabi obeh metod morajo biti poznane lege kamere iz katerih so bile slike zajete. Za določitev lege kamere smo uporabili informacijo pridobljeno z detekcijo referenčnega objekta - lista formata A4. Razvili smo sistem, ki uporabnika vodi pri zajemanju slik s pametno mobilno napravo osnovano na platformi Android, nato pa iz zajetih slik zgradi 3D model predmeta v procesnem delu implementiranem v razvojnem okolju Matlab. Tako dobljene 3D modele smo primerjali z referenčnimi, pri čemer smo predlagali ter razvili evalvacijsko metodologijo in napake podrobno preučili ter rezultate ovrednotili. Predstavili smo tudi kvalitativne rezultate na katerih smo izpostavili prednosti in omejitve izbranih metod.The main goal of the master thesis is to develop a system for reconstruction of 3D objects from colour images. The main focus is on passive computer vision methods from which we select two, i.e., Stereo vision and Space carving. Both methods require information about camera poses. The camera pose for a given image is estimated from the information obtained by detecting a reference object, i.e., a standard A4 paper sheet. We develop an Android based mobile application to guide a user during image capturing process and a Matlab based system for 3D object reconstruction from captured images. The reconstructed 3D models of objects where compared to reference 3D models. Using an evaluation metodology we propose, we evaluated the implemented methods and analysed the results. We also present qualitative results and highlight strengths and shortcomings of the two selected passive computer vision methods for 3D reconstruction

A Recommender System for Robust Smart Contract Template Classification

IoT environments are becoming increasingly heterogeneous in terms of their distributions and included entities by collaboratively involving not only data centers known from Cloud computing but also the different types of third-party entities that can provide computing resources. To transparently provide such resources and facilitate trust between the involved entities, it is necessary to develop and implement smart contracts. However, when developing smart contracts, developers face many challenges and concerns, such as security, contracts’ correctness, a lack of documentation and/or design patterns, and others. To address this problem, we propose a new recommender system to facilitate the development and implementation of low-cost EVM-enabled smart contracts. The recommender system’s algorithm provides the smart contract developer with smart contract templates that match their requirements and that are relevant to the typology of the fog architecture. It mainly relies on OpenZeppelin, a modular, reusable, and secure smart contract library that we use when classifying the smart contracts. The evaluation results indicate that by using our solution, the smart contracts’ development times are overall reduced. Moreover, such smart contracts are sustainable for fog-computing IoT environments and applications in low-cost EVM-based ledgers. The recommender system has been successfully implemented in the ONTOCHAIN ecosystem, thus presenting its applicability