"Unmanned Aerial Vehicles (UAVs) have been widely used in modern warfare for surveillance, reconnaissance and even attack missions. They can provide valuable battlefield information and accomplish dangerous tasks with minimal risk of loss of lives and personal injuries. However, existing UAV systems are far from perfect to meet all possible situations. One of the most notable situations is the support for individual troops. Besides the incapability to always provide images in desired resolution, currently available systems are either too expensive for large-scale deployment or too heavy and complex for a single solder. Intelligent Portable Aerial Surveillance System (IPASS), sponsored by the Air Force Research Laboratory (AFRL), is aimed at developing a low-cost, light-weight unmanned aerial vehicle that can provide sufficient battlefield intelligence for individual troops. The main contributions of this thesis are two-fold (1) the development and verification of a model-based flight simulation for the aircraft, (2) comparison of image stitching techniques to provide a comprehensive aerial surveillance information from multiple vision. To assist with the design and control of the aircraft, dynamical models are established at different complexity levels. Simulations with these models are implemented in Matlab to study the dynamical characteristics of the aircraft. Aerial images acquired from the three onboard cameras are processed after getting the flying platform built. How a particular image is formed from a camera and the general pipeline of the feature-based image stitching method are first introduced in the thesis. To better satisfy the needs of this application, a homography-based stitching method is studied. This method can greatly reduce computation time with very little compromise in the quality of the panorama, which makes real-time video display of the surroundings on the ground station possible. By implementing both of the methods for image stitching using OpenCV, a quantitative comparison in the performance is accomplished."

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Šiame darbe pateikiamas apibendrintas automatinio vaizdų jungimo į panoramas algoritmas, detaliai aptariami algoritmo žingsniai ir galimas našumo didinimas lygiagretaus programavimo priemonėmis. Tyrime aprašyti vaizdų jungimo bandymai panaudojant kelias populiarų vaizdų bruožų alternatyvas. Taip pat pristatoma praktinė vaizdų jungimą panaudojanti aplikacija. / This study presents a generalization of an automated image stitching algorithm, describes it's steps in great detail and discusses possible performance improvements by use of parallel execution. Results of stitching experiments using a couple of modern and popular image feature alternatives are shown. A practical application using an implementation of automated image stitching is presented.

Calibrating multiple projectors to create a distortion free environment is required in many fields e.g. simulators and the calibration may be done in a series different ways. This report will cover an automatic single camera projector calibration algorithm.The algorithm handles multiple projectors and can handle projectors covering bigger field of view than a camera by supporting image stitching. A proof of concept blending algorithm is also presented. The algorithm includes a new developed interpolation method building on spline surfaces and an orientation calculation algorithm that calculates the orientation difference between two camera views. Using the algorithm to calibrate, gives pixel accuracy of less than 1 camera pixel after interpolation and the relation between two views are calculated accurately. The images created using the algorithm is distortion free and close to seamless. The algorithm is limited to a controlled projector environment and calibrates the projectors for a single viewpoint. Furthermore, the camera needs to be calibrated positioned in the sweet spot although it can be arbitrary rotated.

At present, one of the big challenges is to develop a precise surface measurement method for mechanical parts. Especially, to study cylindrical surface, the cause of many difficulties because of its geometry shape. This thesis presents a quite good solution for analyzing topography of cylindrical surface with White Light Interferometry optical system which is one of the important and suitable tools in optics. In the construction period, the aim was to build a system which can be easily mounted on the sample. This is done by a very simple and compact design that also enables us to use it in research laboratories. In the project, a cylindrical surface analysis is achieved by taking subsequent images with different nano-scale distance from the sample and stitched the acquired images. To achieve this implementation, subsequent images with the highest intensity are first determined and then located in a single image. In the stitching process, cross correlation method that is extremely useful to find out relative point of the images is used to merge the acquired images. Additionally, stitching process is helped us to extend the area where research can be done. In the project, MATLAB & LABVIEW are used for analyzing the images and controlling the motors, respectively.

The ultimate goal of the development of aerial photogrammery is to acquire rapidly and accurately the ground measurements. However, traditional photogrammetric technologies, particularly in the continuous digital images stitching technique, is still very limited. In the past, the ground control points were used as the references for the image registration, however, it is very time and resource consuming, as well as human visual capability constraint. Accuracy and efficiency are two key factors which need to be enhanced to meet the practical requirement for aerial imageries stitching. The SIFT (Sale Invariant Feature Transform) algorithm was used in the computer vision to perform feature extraction in good condition. The extracted SIFT features are invariant to image scale, rotation, noise and change in illumination, and it is a robust and abundant feature extraction algorithm. SIFT algorithm extracts feature points from multi-scale space. For a large scale aerial image containing huge amount of image contents, it will spend a lot of time to extract features from imagery. Therefore, this study proposes a new method, called Inter-Grid Down-Sampling (IGDS) method, to reduce the image size and relative amount of image information to improve the computing efficiency. The correspondent extracted features are matched in the adjacent images with additional RANSAC outlier removal procedure to select correct and characteristic feature points. Finally the Hugin-Panorama Photo Stitching software is used to stitch all the continuous photogrammetric images for producing a panorama imagery of all flight lines. The experiment results indicate that sub-pixel accuracy for extracted feature points can be obtained when the down-sampling factor 3 was selected for the IGDS method, and it only needs half of the computing time. Compared to the Nearest-Neighbor Interpolation and Cubic Interpolation methods to reduce the image size, the IGDS method can increase more feature extraction efficiency without scarifying the location accuracy. When threshold value for SIFT was set between 0.4 to 0.6, we can achieve the largest correct matching rate. In addition, the RANSAC outlier removal procedure can effectively select the best matching feature points both in numbers and locations. For image stitching, the Hugin-panorama photo stitching software can effectively be used to match feature points and do geometric correction and color adjustment to obtain a consistent panorama imagery. Finally, the proposed method in this study can derive a low-variant in resolution and measurements significance for a stitching image from continuous aerial images.

Persistics is an advanced framework for processing wide-area aerial surveillance video. This framework handles the tasks of data collection, stitching of multi-sensor imagery, image registration and stabilization, motion tracking, and compression. As the technology for image sensor sizes improves, significant improvements in compression techniques are necessary in order to make full use of the data. Because the information of interest in such video is naturally moving, point-like targets, the applicability of foveated coding to the compression problem is an interesting question. Foveated coding, a compression technique that was designed to be perceptually optimal for the human visual system, has several components that are appropriate to the persistics compression problem. Foveation is applied in several different scenarios and methods to persistics data. As foveation can make good use of the persistics tracker data, a problem affecting tracker performance is explored as well. The multi-sensor stitching component of persistics can generate artifacts that reduce the effectiveness of the tracker. A method for characterizing, detecting, and correcting such artifacts is desirable. These three concepts are explored, and a method for detection is developed. Components of these algorithms were absorbed into a more general framework for artifact correction. / text

In this thesis, the applications of image stitching and object insertion are considered and two gradient based approaches offering solutions are proposed. An essential part of the proposed methods is obtaining an image from a given gradient data set. This is done using an existing Haar wavelet based reconstruction technique, which consists of two main steps. First, the Haar wavelet decomposition of the image to be reconstructed is obtained directly from a given gradient. Second, the image is obtained using Haar wavelet synthesis. In both stitching and object insertion applications considered, the gradient from which the image must be reconstructed is a non-conservative vector field and this requires adding an iterative Poisson solver at each resolution level, during the synthesis step of the reconstruction technique. The performance of the reconstruction algorithm is evaluated by comparing it with other existing techniques, in terms of solution accuracy and computation speed. The proposed image stitching technique consists of three main parts: registering the images to be combined, blending their gradients over a region of interest and obtaining a composite image from a gradient. The object insertion technique considers the images registered and has two main stages: gradient blending of images in a region of interest and recovering an image from the gradient. The performance of the stitching algorithm is evaluated visually, by presenting the results produced to combine images with varying orientation, scales, illumination, and color conditions. Experimental results illustrate both the stitching and the insertion techniques proposed, and indicate that they yield seamless composite images. / Graduate

Integrated camera systems for increasing safety and maneuverability are becoming increasingly common for heavy vehicles. One problem with heavy vehicles today is that there are blind spots where the driver has no or very little view. There is a great demand on increasing the safety and helping the driver to get a better view of his surroundings. This can be achieved by a sophisticated camera system, using cameras with wide field of view, that could cover dangerous blind spots. This master thesis aims to investigate and develop a prototype solution for a camera system consisting of two fisheye cameras. The solution covers both hardware choices and software development including camera calibration and image stitching. Two different fisheye camera calibration toolboxes are compared and their results discussed, with the aim to find the most suitable for this application. The result from the two toolboxes differ in performance, and the result from only one of the toolboxes is sufficient for image stitching.

With increased mobile hardware capabilities, improved UAVs and modern algorithms, accurate maps can be created in real-time by capturing overlapping photographs of the ground. A method for mapping that can be used is to position photos by relying purely on the GPS position and altitude. However, GPS inaccuracies will be visible in the created map. In this paper, we will instead present a method for aligning the photos correctly with the help of feature matching. Feature matching is a well-known method which analyses two photos to find similar parts. If an overlap exists, feature matching can be used to find and localise those parts, which can be used for positioning one image over the other at the overlap. When repeating the process, a whole map can be created. For this purpose, we have also evaluated a selection of feature detection and matching algorithms. The algorithm found to be the best was SIFT with FLANN, which was then used in a prototype for creating a complete map of a forest. Feature matching is in many cases superior to GPS positioning, although it cannot be fully depended on as failed or incorrect matching is a common occurrence.