The goal of this thesis is to develop a smartphone application for augmented reality view; it is an initial attempt to realize a driver assistance functionality using just a smartphone and an external lens. Initially it depicts a brief analysis about the most feasible development technologies for mobile application development, selecting a proper lens and positioning of the smartphone in the car. Later, it discusses the strategies for real-time object detection using OpenCV; the video frames are processed using the strategies to find patterns in the videos. Different techniques like Hough-line transform, watershed, contour detection, color segmentation, color thresholding and HAAR cascades are implemented and compared in terms of real time detection of the desired objects. Then a unified algorithm is implemented for the given scenario which overcomes the challenges faced during the conceptualization phase. Finally, the results are depicted with the snapshots of the real time detection done from the smartphone and then evaluated against the vision of the application and the achieved tasks. This thesis is concluded by stating the prospects of this mobile application in the future.

The production line of industries are getting more efficient and having very high throughput. Different kinds of machineries are being used to make the production safe, fast, precise and reliable. Robot arm is such a machine which helps the production line to be more efficient and productive. Nowadays, many manufacturing industries are using robot-arms to get a competitive edge in manufacturing and can be outfitted for multiple applications like welding, material handling, thermal spraying, painting, drilling and so on. They are widely used to increase product quality and production demand and over all, to ensure safer, faster and efficient production. It is very important to control and maintain these machines very accurately. As a simple mistake of robot arm can cause excessive destructions and bring financial losses to the industries, the robotarms must be very accurate when they are functioning in their production settings.

The demand for Advanced Driver Assistance System (ADAS) applications is increasing day by day and their development requires efficient prototyping and real time testing. ADTF (Automotive Data and Time Triggered Framework) is a software tool from Elektrobit which is used for Development, Validation and Visualization of Vision based applications, mainly for ADAS and Autonomous driving. With the help of ADTF tool, Image or Video data can be recorded and visualized and also the testing of data can be processed both on-line and off-line. The development of ADAS applications needs image and video processing and the algorithm has to be highly efficient and must satisfy Real-time requirements. The main objective of this research would be to integrate OpenCV library with ADTF cross platform. OpenCV libraries provide efficient image processing algorithms which can be used with ADTF for quick benchmarking and testing. An ADTF filter framework has been developed where the OpenCV algorithms can be directly used and the testing of the framework is carried out with .DAT and image files with a modular approach. CMake is also explained in this thesis to build the system with ease of use. The ADTF filters are developed in Microsoft Visual Studio 2010 in C++ and OpenMP API are used for Parallel programming approach.

The goal of this thesis is to develop a smartphone application for augmented reality view; it is an initial attempt to realize a driver assistance functionality using just a smartphone and an external lens. Initially it depicts a brief analysis about the most feasible development technologies for mobile application development, selecting a proper lens and positioning of the smartphone in the car. Later, it discusses the strategies for real-time object detection using OpenCV; the video frames are processed using the strategies to find patterns in the videos. Different techniques like Hough-line transform, watershed, contour detection, color segmentation, color thresholding and HAAR cascades are implemented and compared in terms of real time detection of the desired objects. Then a unified algorithm is implemented for the given scenario which overcomes the challenges faced during the conceptualization phase. Finally, the results are depicted with the snapshots of the real time detection done from the smartphone and then evaluated against the vision of the application and the achieved tasks. This thesis is concluded by stating the prospects of this mobile application in the future.

The purpose of this thesis is to develop the architecture to use the Car2X for observation the local traffic sign and displays it on the OpenStreetMap to provide more information of the road side to the driver. The proposed architecture of this thesis is to convert the traffic sign into the barcode and to be scanned by the barcode scanner and then wirelessly transfers the data to the web server to store the data and displays the traffic sign on the OpenStreetMap in the web browser. It uses two Raspberry Pi boards with CAN-Bus shields for transmitting the data on the CAN-Bus system in the car, a barcode scanner to scan the barcode, a GPS module to get its location, and a WiFi dongle to wirelessly send the data. The thesis also includes the camera to detect the traffic light using OpenCV and sends the GO or STOP command to the car. The results provide the OpenStreetMap with the traffic sign which helps the driver to realize the traffic sign on the road of the desired destination. However, the accuracy of GPS is not satisfied as well as the distance of the barcode scanning, therefore, this thesis suggests that includes the gps position in the barcode and uses the camera to detect the barcode for the improvement in the future.

Computer vision and image processing systems are ubiquitous in automotive domain and manufacturing industry. Lane detection warning systems has been an elementary part of the modern automotive industry. Due to the recent progress in the computer vision and image processing methods, economical and flexible use of computer vision is now pervasive and computing with images is not just for the realm of the science, but also for the arts and social science and even for hobbyists. Image processing is a key technology in automotive industry, even now there is hardly a single manufacturing process that is thinkable without imaging. The applications of image processing and computer vision methods in embedded systems platform, is an ongoing research area since many years. OpenCV, an open-source computer vision library containing optimized algorithms and methods for designing and implementing applications based on video and image processing techniques. These method are organized in the form of modules for specific field including, user-graphic interface, machine learning, feature extraction etc [43]. Vision-based automotive application systems become an important mechanism for lane detection and warning systems to alert a driver about the road in localization of the vehicle [1]. In automotive electronic market, for lane detection problem, vision-based approaches has been designed and developed using different electronic hardware and software components including wireless sensor, camera module, Field-Programmable Gate Array (FPGA) based systems, GPU and digital signal processors (DSP) [13]. The software module consists on the top of real-time operating systems and hardware description programming language including Verilog, or VHDL. One of the most time critical task of vision based systems is to test system applications in real physical environment with wide variety of driving scenarios and validating the whole systems as per the automotive industry standards. For validating and testing the advanced driver assistance systems, there are some commercial tools available including Assist ADTF from Elektrobit, EB company [43]. In addition to the design and strict real-time requirements for advanced driver assistance systems applications based on electronic components and embedded platform, the complexity and characteristics of the implemented algorithms are two parameters that need to be taken into consideration choosing hardware and software component [13]. The development of vision-based automotive application, based on alone electronic and micro-controller is not a feasible solution approach [35] [13] and GPU based solution are attractive but has many other issues including power consumption. In this thesis project, image and video processing module is used from OpenCV library for road lane detection problems. In proposed lane detection methods, low-level image processing algorithms and methods are used to extract relevant information for lane detection problem by applying contrast analysis at pixel level intensity values. Furthermore, the work at hand presents different approaches for solving relevant partial problems in the domain of lane detection. The aim of the work is to apply contrast analysis based on low-level image processing methods to extract relevant lane model information from the grid of intensity values of pixel elements available in image frame. The approaches presented in this project work are based on contrast analysis of binary mask image frame extracted after applying range threshold. A set of points, available in an image frame, based lane feature models are used for detecting lanes on color image frame captured from video. For the performance measurement and evaluation, the proposed methods are tested on different systems setup, including Linux, Microsoft Windows, CodeBlocks, Visual Studio 2012 and Linux based Rasbian-Jessie operating systems running on Intel i3, AMD A8 APU, and embedded systems based (Raspberry Pi 2 Model B) ARM v7 processor respectively.

Automation in warehouses has been improved in a very certain manner, combining sensors for perception of the environment and mapping of the warehouse. The most common characteristic, which makes the products and the pallet rack cells discriminative, are the barcodes placed on them. This means that the warehouse management system should successfully perceive all the necessary information of the detected barcodes, which also includes their position in the warehouse and build a barcode map of the environment. For this process a barcode reader is needed, with extended capabilities such as estimation of the 3-dimensional coordinates of the barcodes. The main idea of this research was the development of a system to be used in future work, placed on the roof of a forklift, which will be able to detect the barcodes and localize itself using an existing map of the warehouse and update new information in this map. However, the purpose of this project was the investigation of a suitable system for barcode mapping. The main challenge of this project was the development of a barcode reader, which fulfills all the referred capabilities and the comparison with a commercial reader in order to evaluate the performance of the system. In this project a barcode reader was developed using software libraries and a camera for industrial use. The performance of the system was compared with a commercial barcode reader. Moreover, an algorithm was implemented, for estimation of the position of each detected barcode, with reference to the position of the camera's lens.  According to the results of all the investigations the performance of the developed system was quiet satisfying and promising. The comparison of the two systems proved that the commercial barcode reader had a better performance than the implemented system. However, it lacked the ability to provide the required information for mapping also the flexibility for integration with other systems. Overall, the developed system proved to be suitable for integration with a warehouse management system for barcode mapping of the environment.

"Stereovision has been applied in many fields including UGV (Unmanned Ground Vehicle) navigation and surgical robotics. Traditionally most stereovision applications are binocular which uses information from a horizontal 2-camera array to perform stereo matching and compute the depth image. Trinocular stereovision with a 3-camera array has been proved to provide higher accuracy in stereo matching which could benefit application like distance finding, object recognition and detection. However, as a result of an extra camera, additional information to be processed would increase computational burden and hence not practical in many time critical applications like robotic navigation and surgical robot. Due to the nature of GPUÂ’s highly parallelized SIMD (Single Instruction Multiple Data) architecture, GPGPU (General Purpose GPU) computing can effectively be used to parallelize the large data processing and greatly accelerate the computation of algorithms used in trinocular stereovision. So the combination of trinocular stereovision and GPGPU would be an innovative and effective method for the development of stereovision application. This work focuses on designing and implementing a real-time trinocular stereovision algorithm with GPU (Graphics Processing Unit). The goal involves the use of Open Source Computer Vision Library (OpenCV) in C++ and NVidia CUDA GPGPU Solution. Algorithms were developed with many different basic image processing methods and a winner-take-all method is applied to perform fusion of disparities in different directions. The results are compared in accuracy and speed to verify the improvement."

The usage of Unmanned Aerial Vehicles (UAV) for several applications has in-creased during the past years. One of the possible applications are aerial imagecapturing for detection and surveillance purposes. In order to make the captur-ing process more efficient, multiple, cameraequipped UAV:s could fly in a for-mation and as a result cover a larger area. To be able to receive several imagesequences and stitch those together, resulting in a panoramavideo, a softwareapplication has been developed and tested for this purpose.All functionality are developed in the language C++ by using the software li-brary OpenCV. All implementations of different techniques and methods hasbeen done as generic as possible to be able to add functionality in the future.Common methods in computervision and object recognition such as SIFT, SURF and RANSAC have been tested. / Användningen av UAV:er för olika tillämpningar har ökat under senare år. Ett avmöjliga användningsområden är flygfotografering och övervakning. För att gö-ra bildupptagningen mer effektiv kan flera UAV:er flyga i formation och på så viskunna fotografera ett avsevärt större område. För att kunna ta in flera bildsekven-ser och foga samman dessa till en panoramavideo, har ett program utvecklats ochtestats för denna uppgift.All funktionalitet för inläsning av bilder och video har utvecklats i C++ medprogrambiblioteket OpenCV. Implementeringen av dessa tekniker och metoderhar gjort så generiskt som möjligt för att det ska vara lättare att lägga till and-ra tekniker och utöka programmets funktioner. Olika tekniker som har testatsinkluderar: SIFT, SURF och RANSAC

Šiame darbe yra tiriamos atrankiojo aplinkos 3D geometrijos atstatymo galimybės panaudojant Kinect jutiklio kombinuotą vaizdo-gylio kamerą: pateikiamas matematinis atstatymo modelis, jo parametrizavimui reikalingi koeficientai, apibūdinama tikėtinų paklaidų apimtis, siūloma aktualių scenos duomenų išskyrimo iš scenos procedūra, tiriamas gaunamo modelio triukšmas ir jo pašalinimo galimybės ir metodai. Atstatyta geometrija yra pateikiama metrinėje matų sistemoje, ir kiekvienas 3D scenos taškas papildomai saugo savo spalvinę informaciją. Praktinėje dalyje pateikiama sukurta taikomoji programa yra įgyvendinta naudojant C++ ir OpenCV matematines programavimo bibliotekas. Ji atlieka 3D geometrijos atstatymą pagal pateiktą teorinį modelį, išskiria aktualius scenos duomenis, pašalina triukšmą ir gali išsaugoti gautus duomenis į 3D modeliavimo programoms suprantamą PLY formato bylą. Darbą sudaro: įvadas, 3 skyriai, išvados ir literatūros sąrašas. Darbo apimtis – 61 p. teksto be priedų, 43 paveikslai, 4 lentelės, 22 bibliografiniai šaltiniai. / The purpose of this thesis is to investigate the possibilities of selective 3D geometry reconstruction using Kinect combined image-depth camera: a mathematical reconstruction model is provided, as well as coefficients to parametrize it and estimates on expected precision; a procedure on filtering out the background from depth image is proposed, depth image noise and possibilities for its removal are studied. Resulting reconstructed geometry is provided using metric system of measurement, and each 3D point also retains it's color data. Resulting application is implemented in C++ programming language and uses OpenCV programming library. It implements 3D geometry reconstruction as described in theory section, removes background from depth image, as well as noise, and is able to save the resulting 3D geometry to a 3D modeling applications readable file format. Structure: introduction, 3 chapters, conclusions, references. Thesis consists of – 61 p. of text, 43 figures, 4 tables, 22 bibliographical entries.