Augmented Reality (AR) has become a popular area in computer Science where research studies and technological innovations are extensive. Research in AR first began in the early 1990s and thenceforth, a number of dierent tracking algorithms and methods have been developed. Tracking systems have a critical importance for AR and marker based vision tracking systems became the mostly used tracking systems due to their low cost and ease of use. Basically, marker systems consist of special patterns that are placed in the environment and detected by simple cameras. In this thesis, we propose a new marker system based on topological tracking where markers are detected and identified using their topology trees. In our proposed marker system, we create topology trees of markers as region adjacency graphs that are obtained from binary images of the markers. Similarly, camera frames are also converted into binary images and corresponding topology trees are created. We used left heavy depth sequences as the canonical convention for the topology trees. For marker tracking, we used a simplified version of subgraph isomorphism algorithm that searches marker topology trees in a frame topology tree. Finally, we tested our proposed system for performance in using spatially distinct marker parts / occlusion resolving / detection rate with respect to marker size, camera-marker angle, false positive marker detection / performance with respect marker library size. Our system achieved 90% marker detection success with 50 pixels marker size and an average of 1.1 false positive marker detection in ten dierent test videos. We made all tests in comparison with the widely used ARToolkit library. Our system surpasses the ARToolkit library for all tests performed. In addition, our system enables spatially distinct placement of marker parts and permits occlusion unless the topology of the marker is not corrupted, but the ARToolkit library does not have these features.

Aerial surveillance missions require a geographical region known as the area of interest to be inspected. The route that the aerial reconnaissance vehicle will follow is known as the flight route. Flight route planning operation has to be done before the actual mission is executed. A flight route may consist of hundreds of pre-defined geographical positions called waypoints. The optimal flight route planning manages to find a tour passing through all of the waypoints by covering the minimum possible distance. Due to the combinatorial nature of the problem it is impractical to devise a solution using brute force approaches. This study presents a strategy to find a cost effective and near-optimal solution to the flight route planning problem. The proposed approach is implemented on GPU using CUDA.

This thesis investigates how a robot can use action generation mechanisms to recognize the action of an observed actor in an on-line manner i.e., before the completion of the action. Towards this end, Dynamic Movement Primitives (DMP), an action generation method proposed for imitation, are modified to recognize the actions of an actor. Specifically, a human actor performed three different reaching actions to two different objects. Three DMP&#039 / s, each corresponding to a different reaching action, were trained using this data. The proposed method used an object-centered coordinate system to define the variables for the action, eliminating the difference between the actor and the robot. During testing, the robot simulated action trajectories by its learned DMPs and compared the resulting trajectories against the observed one. The error between the simulated and the observed trajectories were integrated into a recognition signal, over which recognition was done. The proposed method was applied on the iCub humanoid robot platform using an active motion capture device for sensing. The results showed that the system was able to recognize actions with high accuracy as they unfold in time. Moreover, the feasibility of the approach is demonstrated in an interactive game between the robot and a human.

Researchers are still far from thoroughly understanding and building accurate computational models of the mechanisms in human mind that give rise to cognitive processes such as emergence of concepts and language acquisition. As a new attempt to give an insight into this issue, in this thesis, we are concerned about developing a computational model that leads to the emergence of concepts. Specically, we investigate how a robot can acquire verb and object concepts through learning affordances, a notion first proposed by J. J. Gibson in 1986. Using the affordance formalization framework of Sahin et al. in 2007, a humanoid robot acquires concepts through interactions in an embodied environment. For the acquisition of verb concepts, we take an alternative approach to the literature, which generally links verbs to specific behaviors of the robot, by linking them to specific effects that different behaviors may generate. We show how our robot can learn effect prototypes, represented in terms of feature changes in the perception vector of the robot, through demonstrations made by a human supervisor. As for the object concepts, we use the affordance relations of objects to create object concepts based on their functional relevance. Additionally, we show that the extracted eect prototypes corresponding to verb concepts can also be utilized to discover stable and variable properties of objects which can be associated to stable and variable affordances. Moreover, we show that the acquired concepts provide a suitable basis for communication with humans or other agents, for example to understand and imitate others&#039 / behaviors or for goal specication tasks. These capabilities are demonstrated in simple interaction games on the iCub humanoid robot platform.

In this thesis the action learning and generation problem on a humanoid robot is studied. Our aim is to realize action learning, generation and recognition in one system and our inspiration source is the mirror neuron hypothesis which suggests that action learning, generation and recognition share the same neural circuitry. Dynamic Movement Primitives, an efficient action learning and generation approach, are modified in order to fulfill this aim. The system we developed (1) can learn from multiple demonstrations, (2) can generalize to different conditions, (3) generates actions in a closed-loop and online fashion and (4) can be used for online action recognition. These claims are supported by experiments and the applicability of the developed system in real world is demonstrated through implementing it on a humanoid robot.

In this thesis, a generic, scale and resolution invariant method to extract 3D features from 3D surfaces, is proposed. Features are extracted with their scale (metric size and resolution) from range images using scale-space of 3D surface curvatures. Different from previous scale-space approaches / connected components within the classified curvature scale-space are extracted as features. Furthermore, scales of features are extracted invariant of the metric size or the sampling of the range images. Geometric hashing is used for object recognition where scaled, occluded and both scaled and occluded versions of range images from a 3D object database are tested. The experimental results under varying scale and occlusion are compared with SIFT in terms of recognition capabilities. In addition, to emphasize the importance of using scale space of curvatures, the comparative recognition results obtained with single scale features are also presented.

Effectiveness of system testing, in specific phases such as design verification, requirements validation, test planning and generation, system integration and system testing are considered. Software as well as hardware test issues are reviewed. Metrics related to system testing are specified. The current system testing processes in a large Turkish military electronic systems manufacturer are reviewed, specific problems are identified and UML-based behavioral testing is proposed as an improved process. The current process and the proposed process are compared in terms of test coverage, test effectiveness and test effort metrics.

Medical image analysis is one of the most critical studies in field of medicine, since results gained by the analysis guide radiologists for diagnosis, treatment planning, and verification of administered treatment. Therefore, accuracy in analysis of medical images is at least as important as accuracy in data acquisition processes. Medical images require sequential application of several image post-processing techniques in order to be used for quantification and analysis of intended features. Main objective of this thesis study is to build up an application framework, which enables analysis and quantification of several features in medical images with minimized input-dependency over results. Intended application targets to present a software environment, which enables sequential application of medical image processing routines and provides support for radiologists in diagnosis, treatment planning and treatment verification phases of neurodegenerative diseases and brain tumors / thus, reducing the divergence in results of operations applied on medical images. In scope of this thesis study, a comprehensive literature review is performed, and a new medical image processing and analysis framework - including modules responsible for automation of separate processes and for several types of measurements such as real tumor volume and real lesion area - is implemented. Performance of the fully-automated segmentation module is evaluated with standards introduced by Neuro Imaging Laboratory, UCLA / and the fully-automated registration module with Normalized Cross-Correlation metric. Results have shown a success rate above 90 percent for both of the modules. Additionally, a number of experiments have been designed and performed using the implemented application. It is expected for an accurate, flexible, and robust software application to be accomplished on the basis of this thesis study, and to be used in field of medicine as a contributor by even non-engineer professionals.

The approach proposed in this thesis contributes to implementing source code reuse and re-engineering techniques for agile software development. This work includes an introduction to feature models and some of the Feature Oriented Software Development (FOSD) practices to achieve a lightweight way of retrieving source code. A Feature model created during the course of following FOSD practices serves as an additional layer of documentation which represents the problem space for the developed application. This thesis proposes linking source code with such a feature model for the purpose of identifying and retrieving code. This mechanism helps with accessing the code segment corresponding to a feature with minimal effort, thus suits agile development methods. At the moment, there is a gap between feature oriented approaches and agile methods. This thesis tries to close this gap between high-level approaches for software modelling (feature modelling) and agile methods for software development.

Dynamic Distributed Dependable Real-time Industrial communication Protocol family (D3RIP), has been proposed in the literature considering the periodic or event-based traffic characteristics of the industrial communication networks. D3RIP framework consists of two protocol families: Interface Layer (IL) protocol family, which is responsible for providing the accurate time-division multiple access (TDMA) on top of a shared-medium broadcast channel, and Coordination Layer (CL), which is defined to fulfill the external requirements of IL. In this thesis, the hardware adaptations of the two protocols, Real-time Access Interface Layer (RAIL) and Time-slotted Interface Layer (TSIL), of the IL protocol family, are implemented. Their performance on both personal computers (PC) and development kits (DK) are observed.