In this thesis a method is presented to provide a 3D visualization of the human kidney and surrounding tissue during kidney surgery. The method takes advantage of the high detail of 3D X-Ray Computed Tomography (CT) and the high time resolution of Ultrasonography (US). By extracting the geometry from a single preoperative CT scan and animating the kidney by tracking its position in real time US images, a 3D visualization of the surgical volume can be created. The first part of the project consisted of building an imaging phantom as a simplified model of the human body around the kidney. It consists of three parts: the shell part representing surrounding tissue, the kidney part representing the kidney soft tissue and a kidney stone part embedded in the kidney part. The shell and soft tissue kidney parts was cast with a mixture of the synthetic polymer Polyvinyl Alchohol (PVA) and water. The kidney stone part was cast with epoxy glue. All three parts where designed to look like human tissue in CT and US images. The method is a pipeline of stages that starts with acquiring the CT image as a 3D matrix of intensity values. This matrix is then segmented, resulting in separate polygonal 3D models for the three phantom parts. A scan of the model is then performed using US, producing a sequence of US images. A computer program extracts easily recognizable image feature points from the images in the sequence. Knowing the spatial position and orientation of a new US image in which these features can be found again allows the position of the kidney to be calculated. The presented method is realized as a proof of concept implementation of the pipeline. The implementation displays an interactive visualization where the kidney is positioned according to a user-selected US image scanned for image features. Using the proof of concept implementation as a guide, the accuracy of the proposed method is estimated to be bounded by the acquired image data. For high resolution CT and US images, the accuracy can be in the order of a few millimeters.

This paper deals step by step with process of stitching images taken by perspective camera rotated by its optical center into the panoramic image. There are described keypoint searching algorhytms, possibilities of calculating homography matrix and methods of eliminating unwanted seams between source images in final panoramic image. A part of this paper is also standalone application in which are implemented some algorhytms described in the work.

Motion segmentation is an important process for separating moving objects from the background. Common methods usually assume fixed camera, other approaches exist as well, but they are usually very computational intensive. This work presents an approach for scene segmentation to regions with coherent motion, which works faster than similar methods and it is capable of online processing with no prior knowledge of objects or camera. The main assumption is that the points belonging to a single objects are moving together and this applies as well in the opposite direction. The proposed method is based on tracking of feature points and searching for groups with similar motion by using RANSAC-based algorithm. Short-range repair of broken tracks is applied to increase the overall robustness of tracking. Found clusters are subsequently processed to represent separate moving objects.

This master's thesis deals with the interest point detection in images. The main goal is to create an application for making panoramic photos, which is based on this detection. The application uses the SIFT detector for finding keypoints of the image. Afterwards the geometrical consistence of image points using the RANSAC algorithm is finds out and images are transform into panorama in accordance with this geometrical consistence. Finally techniques for blending of transition between photos are used.

Multiple target tracking (MTT) is the process of localizing targets in an environment using sensors that perceive the environment. MTT has many applications such as wildlife monitoring, air traffic monitoring, and surveillance. These applications motivate further research in the different challenging aspects of MTT. One of these challenges that we will focus on in this dissertation is constructing a high fidelity target model. A common approach to target modeling is to use linear models or other simplified models that do not properly describe the target's pose (position and orientation), motion, and uncertainty. These simplified models are typically used because they are easy to implement and computationally efficient. A more accurate approach that improves tracking performance is to define the target model using a geometric representation of the target's natural configuration manifold. In essence, this geometric approach seeks to define a target model that can express every pose and motion of the target while preserving geometric properties such as distances and angles. We restrict our discussion of MTT to objects that move in physical space and can be modeled as a rigid body. This restriction allows us to construct generic geometric target models defined on Lie groups. Since not every Lie group has additional structure that permits vector space arithmetic like Euclidean space, many components of MTT such as data association, track initialization, track propagation and updating, track association and fusing, etc, must be adapted to work with Lie groups. The main contribution of this dissertation is the presentation of a novel MTT algorithm that implements the different MTT components to work with target models defined on Lie groups. We call this new algorithm, Geometric Multiple Target Tracking (G-MTT). This dissertation also serves as a guide on how other MTT algorithms can be modified to work with geometric target models. As part of the presentation there are various experimental results that strengthen the argument that a geometric approach to target modeling improves tracking performance.

This thesis deals with curve detection in images. First, current methods used in this area of image processing are summarized and described. Main topic of this thesis is a comparison of methods of parametric curve detection, such as Hough transformation and RANSAC-based methods. These methods are compared according to several criteria which are the most important for precise edge detection.

This thesis is interested in detection of corresponding points in images, which display the same object, eventually some of important elements and synchronizing these images. The aim of this thesis is to find, study and choose suitable algorithm for detecting interesting points in image. This algorithm will be apply at couple of images and in these images will find couples of corresponding points across these images. Functional output of this thesis will be application which will realize choosen interesting points detector, algorithm for finding correspondencies of regions and their synchronizing and joint them to one output image.

A lightweight, powerful, yet efficient quad-rotor platform was designed and constructed to obtain experimental results of completely autonomous control of a hovering micro-UAV using a complete on-board vision system. The on-board vision and control system is composed of a Helios FPGA board, an Autonomous Vehicle Toolkit daughterboard, and a Kestrel Autopilot. The resulting platform is referred to as the Helio-copter. An efficient algorithm to detect, correlate, and track features in a scene and estimate attitude information was implemented with a combination of hardware and software on the FPGA, and real-time performance was obtained. The algorithms implemented include a Harris feature detector, template matching feature correlator, RANSAC similarity-constrained homography, color segmentation, radial distortion correction, and an extended Kalman filter with a standard-deviation outlier rejection technique (SORT). This implementation was designed specifically for use as an on-board vision solution in determining movement of small unmanned air vehicles that have size, weight, and power limitations. Experimental results show the Helio-copter capable of maintaining level, stable flight within a 6 foot by 6 foot area for over 40 seconds without human intervention.

Multiple target tracking (MTT) is the process of identifying the number of targets present in a surveillance region and the state estimates, or track, of each target. MTT remains a challenging problem due to the NP-hard data association step, where unlabeled measurements are identified as either a measurement of an existing target, a new target, or a spurious measurement called clutter. Existing techniques suffer from at least one of the following drawbacks: divergence in clutter, underlying assumptions on the number of targets, high computational complexity, time-consuming implementation, poor performance at low detection rates, and/or poor track continuity. Our goal is to develop an efficient MTT algorithm that is simple yet effective and that maintains track continuity enabling persistent tracking of an unknown number of targets. A related field to tracking is regression analysis, where the parameters of static signals are estimated from a batch or a sequence of data. The random sample consensus (RANSAC) algorithm was developed to mitigate the effects of spurious measurements, and has since found wide application within the computer vision community due to its robustness and efficiency. The main concept of RANSAC is to form numerous simple hypotheses from a batch of data and identify the hypothesis with the most supporting measurements. Unfortunately, RANSAC is not designed to track multiple targets using sequential measurements.To this end, we have developed the recursive-RANSAC (R-RANSAC) algorithm, which tracks multiple signals in clutter without requiring prior knowledge of the number of existing signals. The basic premise of the R-RANSAC algorithm is to store a set of RANSAC hypotheses between time steps. New measurements are used to either update existing hypotheses or generate new hypotheses using RANSAC. Storing multiple hypotheses enables R-RANSAC to track multiple targets. Good tracks are identified when a sufficient number of measurements support a hypothesis track. The complexity of R-RANSAC is shown to be squared in the number of measurements and stored tracks, and under moderate assumptions R-RANSAC converges in mean to the true states. We apply R-RANSAC to a variety of simulation, camera, and radar tracking examples.

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