A common method for finding an object's pose is the generalized Hough transform, which accumulates evidence for possible coordinate transformations in a parameter space and takes large clusters of similar transformations as evidence of a correct solution. We analyze this approach by deriving theoretical bounds on the set of transformations consistent with each data-model feature pairing, and by deriving bounds on the likelihood of false peaks in the parameter space, as a function of noise, occlusion, and tessellation effects. We argue that blithely applying such methods to complex recognition tasks is a risky proposition, as the probability of false positives can be very high.

This thesis deals with automatic localization and tracking of surgical tools such as needles in Magnetic Resonance Imaging(MRI). The accurate and precise localization of needles is very important for medical interventions such as biopsy, brachytherapy, anaesthesia and many other needle based percutaneous interventions. Needle tracking has to be really precise, because the target may reside adjacent to organs which are sensitive to injury. More over during the needle insertion, Magnetic Resonance Imaging(MRI) scan plane must be aligned such that needle is in the field of view (FOV) for surgeon. Many approaches were proposed for needle tracking and automatic MRI scan plane control over last decade that use external markers, but they are not able to account for possible needle bending. Significant amount of work has already been done by using the image based approaches for needle tracking in Image Guided Therapy (IGT) but the existing approaches for surgical robots under MRI guidance are purely based on imaging information; they are missing the important fact that, a lot of important information (for example, depth of insertion, entry point and angle of insertion) is available from the kinematic model of the robot. The existing approaches are also not considering the fact that the needle insertion results in a time sequence of images. So the information about needle positions from the images seen so far can be used to make an approximate estimate about the needle position in the subsequent images. During the course of this thesis we have investigated an image based approach for needle tracking in real-time MR images that leverages additional information available from robot's kinematics model, supplementing the acquired images. The proposed approach uses Standard Hough Transform(SHT) for needle detection in 2D MR image and uses Kalman Filter for tracking the needle over the sequence of images. We have demonstrated experimental validation of the method on Real MRI data using gel phantom and artificially created test images. The results proved that the proposed method can track the needle tip position with root mean squared error of 1.5 mm for straight needle and 2.5mm for curved needle.

Tracking multiple targets in a high cluttered environment where multiple receivers are used is a challenging task due to the high level of false alarms and uncertainty in the track hypothesis. The multi-static active sonar scenario is an example for such systems where multiple source-receiver combinations are deployed. Due to the nature of the underwater environment and sound propagation characteristics, tracking targets in the underwater environment becomes a complex operation. Conventional tracking approaches (such as the Kalman and particle filter) require a predetermined kinematic model of the target. Moreover, tracking an unknown and changing number of targets within a certain search area requires complex mathematical association filters to identify the number of targets and associate measurements to different target tracks. As the number of false detections increases, the computational complexity of conventional tracking system grows introducing further challenges for real-time target tracking situations. The methodology presented in this thesis provides a rapid and reliable tracking system capable of tracking multiple targets without depending on a kinematic model of the target movement. In this algorithm, Self Organizing Maps, Dynamic Programming and the Hough transform are combined to produce tracks of possible targets’ paths and estimate of targets’ locations. Evaluation of the performance of the tracking algorithm is performed using three types of simulations and a set of real data obtained from a sea trial. This research documents the results of experimental testing and analysis of the tracking system. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2009-08-07 13:21:06.869

No description available.

No description available.

No description available.

Techniques, suitable for parallel implementation, for robust 2D model-based object recognition in the presence of sensor error are studied. Models and scene data are represented as local geometric features and robust hypothesis of feature matchings and transformations is considered. Bounds on the error in the image feature geometry are assumed constraining possible matchings and transformations. Transformation sampling is introduced as a simple, robust, polynomial-time, and highly parallel method of searching the space of transformations to hypothesize feature matchings. Key to the approach is that error in image feature measurement is explicitly accounted for. A Connection Machine implementation and experiments on real images are presented.

Neste trabalho é proposto a técnica da Transformada de Hough para a Inspeção Visual Automática de placas retangulares, visando a determinação de características tais como perímetro, ortogonalidade, centro de massa, área, independente de rotação e translação do objeto dentro da imagem. Os resultados obtidos por essa técnica são avaliados pela comparação com o método dos Momentos, o recurso mais utilizado para a determinação de área e centro de massa para imagens bi-dimensionais. A análise estatística dos dados mostra a robustez da técnica da Transformada Hough para imagens ruidosas. / In this work, is proposed the Hough transform method for the Automated Visual lnspection of rectangular boards. The aim is to extract features such as position, perimeter and area, despite of object translation and rotation. The results obtained by this method are evaluated by comparing them with the method of Moments, the most used method to determine the area and center of mass for bi-dimensional images. Statistical analysis show the robustness of Hough transform for noisy images visual inspection.

Neste trabalho é proposto a técnica da Transformada de Hough para a Inspeção Visual Automática de placas retangulares, visando a determinação de características tais como perímetro, ortogonalidade, centro de massa, área, independente de rotação e translação do objeto dentro da imagem. Os resultados obtidos por essa técnica são avaliados pela comparação com o método dos Momentos, o recurso mais utilizado para a determinação de área e centro de massa para imagens bi-dimensionais. A análise estatística dos dados mostra a robustez da técnica da Transformada Hough para imagens ruidosas. / In this work, is proposed the Hough transform method for the Automated Visual lnspection of rectangular boards. The aim is to extract features such as position, perimeter and area, despite of object translation and rotation. The results obtained by this method are evaluated by comparing them with the method of Moments, the most used method to determine the area and center of mass for bi-dimensional images. Statistical analysis show the robustness of Hough transform for noisy images visual inspection.

This theses presents an image processing approach to estimating the length of cynobacteria strands in digitally acquired images. An algorithm utilizing the Hough transform to determine strand continuity at strand intersections is presented. The algorithm is demonstrated on selected images, the examined strands are separated and their lengths are estimated. A Delphi implementation of the algorithm is included.