In order for robots to become more useful they must be able to adapt and operate in foreign or unpredictable environments. The goal of this thesis is to present an algorithm that will enable a robot to autonomously explore its environment by touch and then estimate the shape of objects it encounters. To demonstrate the feasibility and functionality of such an algorithm, it was fully implemented on a MERLIN 6540 industrial robot. A unique compliant end-effector (consisting of a trackball mounted to a force/torque sensor on a sliding mechanism) and a fuzzy logic force controller were developed to overcome the difficulties inherent in force control on a stepper motor robot. A Kalman filter based quadric shape estimator was then used to describe the objects encountered in the MERLIN's workspace. The minimization of a cost function based on the shape estimator's uncertainty guided the robot along an exploration trajectory designed to produce the fastest converging shape estimate. Results of various exploration trials using autonomous and preprogrammed trajectories are presented. In addition to shape estimates, surface curvature measurements were also obtained. The unique end-effector that provided compliance for the force controller was also able to measure the arc length traversed on the object's surface. Arc length combined with surface orientation makes it possible to determine local surface curvature. / Master of Science

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Detecting and tracking moving objects are important topics in computer vision research. Classical methods perform well in applications of steady cameras. However, these techniques are not suitable for the applications of moving cameras because the unconstrained nature of realistic environments and sudden camera movement makes cues to object positions rather fickle. A major difficulty is that every pixel moves and new background keeps showing up when a handheld or car-mounted camera moves. In this dissertation, a novel estimation method of camera motion parameters will be discussed first. Based on the estimated camera motion parameters, two detection algorithms are developed using Bayes' rule and belief propagation. Next, an MCMC-based feature-guided particle filtering method is presented to track detected moving objects. In addition, two detection algorithms without using camera motion parameters will be further discussed. These two approaches require no pre-defined class or model to be trained in advance. The experiment results will demonstrate robust detecting and tracking performance in object sizes and positions.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2006. / When designing electronic circuits, engineers frequently make hand-drawn sketches of circuits. These are then captured with a computerised design. This study aims to create an alternative to the common schematic capture process through the use of an interactive pen-based interface to the capturing software. Sketches are interpreted through a process of vectorising the user’s strokes into primitive shapes, extracting information on intersections between primitives and using a naive Bayesian classifier to identify symbol components. Various alternative approaches were also considered. It is concluded that it is feasible to use a pen-based interface and underlying recognition engine to capture circuit diagrams. It is hoped that this would provide an attractive early design environment for the engineer and enhance productivity.

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

Approved for public release; distribution in unlimited. / In recent years there has been an increased interest in effective individual control and enhanced security measures, and face recognition schemes play an important role in this increasing market. In the past, most face recognition research studies have been conducted with visible imaging data. Only recently have IR imaging face recognition studies been reported for wide use applications, as uncooled IR imaging technology has improved to the point where the resolution of these much cheaper cameras closely approaches that of cooled counterparts. This study is part of an on-going research conducted at the Naval Postgraduate School which investigates the feasibility of applying a low cost uncooled IR camera for face recognition applications. This specific study investigates whether nonlinear kernel-based classifiers may improve overall classification rates over those obtained with linear classification schemes. The study is applied to a 50 subject IR database developed in house with a low resolution uncooled IR camera. Results show best overall mean classification performances around 98.55% which represents a 5% performance improvement over the best linear classifier results obtained previously on the same database. This study also considers several metrics to evaluate the impacts variations in various user-specified parameters have on the resulting classification performances. These results show that a low-cost, low-resolution IR camera combined with an efficient classifier can play an effective role in security related applications. / Captain, Hellenic Air Force

Design of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based algorithms in a sequential, single processor machine like Pascal, C or C++ takes several hours or even days. But in this thesis, the GMDH algorithm was modified and implemented into a software tool written in Verilog HDL and tested with specific application (XOR) to make the simulation faster. The purpose of the development of this tool is also to keep it general enough so that it can have a wide range of uses, but robust enough that it can give accurate results for all of those uses. Most of the applications of neural networks are basically software simulations of the algorithms only but in this thesis the hardware design is also developed of the algorithm so that it can be easily implemented on hardware using Field Programmable Gate Array (FPGA) type devices. The design is small enough to require a minimum amount of memory, circuit space, and propagation delay. / by Mukul Gupta. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

In the past few years, violence detection has become an increasingly rele- vant topic in computer vision with many proposed solutions by researchers. This thesis proposes a solution called Criminal Aggression Recognition Engine (CARE), an OpenCV based Java implementation of a violence detection system that can be trained with video datasets to classify action in a live feed as non-violent or violent. The algorithm extends existing work on fast ght detection by implementing violence detection of live video, in addition to prerecorded video. The results for violence detection in prerecorded videos are comparable to other popular detection systems and the results for live video are also very encouraging, making the work proposed in this thesis a solid foundation for improved live violence detection systems. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Digital video is being used widely in a variety of applications such as entertainment, surveillance and security. Large amount of video in surveillance and security requires systems capable to processing video to automatically detect and recognize events to alleviate the load on humans and enable preventive actions when events are detected. The main objective of this work is the analysis of computer vision techniques and algorithms used to perform automatic detection of events in video sequences. This thesis presents a surveillance system based on optical flow and background subtraction concepts to detect events based on a motion analysis, using an event probability zone definition. Advantages, limitations, capabilities and possible solution alternatives are also discussed. The result is a system capable of detecting events of objects moving in opposing direction to a predefined condition or running in the scene, with precision greater than 50% and recall greater than 80%. / by Ricardo Augusto Castellanos Jimenez. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.