No description available.

Wireless capsule endoscopy (WCE) offers a patient-friendly, non-invasive and painless investigation of the entire small intestine, where other conventional wired endoscopic instruments can barely reach. As a critical component of the capsule endoscopic examination, physicians need to know the precise position of the endoscopic capsule in order to identify the position of intestinal disease after it is detected by the video source. To define the position of the endoscopic capsule, we need to have a map of inside the human body. However, since the shape of the small intestine is extremely complex and the RF signal propagates differently in the non-homogeneous body tissues, accurate mapping and localization inside small intestine is very challenging. In this dissertation, we present an in-body simultaneous localization and mapping technique (Body-SLAM) to enhance the positioning accuracy of the WCE inside the small intestine and reconstruct the trajectory the capsule has traveled. In this way, the positions of the intestinal diseases can be accurately located on the map of inside human body, therefore, facilitates the following up therapeutic operations. The proposed approach takes advantage of data fusion from two sources that come with the WCE: image sequences captured by the WCE's embedded camera and the RF signal emitted by the capsule. This approach estimates the speed and orientation of the endoscopic capsule by analyzing displacements of feature points between consecutive images. Then, it integrates this motion information with the RF measurements by employing a Kalman filter to smooth the localization results and generate the route that the WCE has traveled. The performance of the proposed motion tracking algorithm is validated using empirical data from the patients and this motion model is later imported into a virtual testbed to test the performance of the alternative Body-SLAM algorithms. Experimental results show that the proposed Body-SLAM technique is able to provide accurate tracking of the WCE with average error of less than 2.3cm.

Le deflickering consiste à supprimer le scintillement présent dans les séquences d’images afin de réduire les variations lumineuses entre chacune des images de la vidéo. Il s’agit d’une tâche essentielle, nécessaire dans plusieurs applications, en particulier dans les séquences de films archivés, les vidéos comprimées et les vidéos time-lapse. Au cours de ces dernières années, avec le développement des technologies d’acquisition à haute vitesse, il y a eu un regain d’intérêt pour le problème de suppression de flicker, en particulier le flicker périodique. Dans ce contexte, le flicker correspond à des variations indésirables de luminosité et des couleurs dues à l’interaction entre la vitesse d’acquisition d’une part, et d’autre part le courant alternatif alimentant les sources lumineuses. La présente thèse formule le problème du déflickering périodique dans les vidéos à haute vitesse, étudie les propriétés physiques du flicker et propose à la fois des solutions théoriques et expérimentales pour sa suppression des séquences d’images. Enfin, une nouvelle approche est proposée permettant d’effectuer simultanément le suivi de mouvement et la correction des couleurs / Deflickering consists of removing rapid, artifactual changes of luminosity and colorimetry from image sequences and improving luminosity consistency between video frames. It is a necessary and fundamental task in multiple applications, for instance in archived film sequences, compressed videos and time-lapse videos. In recent years, there has been a renewal of interest for improving luminosity consistency acquisition technology in the flicker removal problem, in particular for periodic flickering. In this context, flicker corresponds to undesirable intensity and chroma variations due to the interaction between the acquisition frequencies on the one hand, and the alternating current powering the light sources on the other hand. The present thesis formulates the periodic deflickering problem in high speed videos, studies the physical properties of flicker and suggests both theoretical and experimental solutions for its removal from image sequences. Finally, a new flicker removal approach is proposed performing jointly motion tracking and color correction

MRI-guided prostate needle biopsy requires compensation for organ motion between target planning and needle placement. Two questions are studied and answered in this work: is rigid registration sufficient in tracking the targets with a maximum error of 3 mm (smaller than average prostate tumor size) and how many intra-operative slices are required to obtain this accuracy? We developed rigid and deformable multislice- to-volume registration algorithms for tracking the biopsy targets within the prostate. Three orthogonal plus additional transverse intra-operative slices were acquired in the approximate center of the prostate and registered with a high-resolution target planning volume. Simulated intra-operative data, phantom data, and MRI-guided robotic prostate biopsy data were used to assess tracking accuracy. Registration tests on simulated intra-operative data with 3, 4, and 5 slices were performed to evaluate the effect on registration error and time. Results: Using three orthogonal slices pro- vides sufficient accuracy. Convergence test results on phantom images demonstrated 100% success rate for initial misalignment of 5mm. Average registration errors for the patient data were 2.55mm and 2.05mm for the rigid and deformable algorithms, respectively. The algorithm was able to capture rigid biopsy target displacements of maximum 8mm and non-rigid displacements of maximum 1.5mm. Rigid tracking appears to be promising. Deformable registration does not seem warranted. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2010-07-13 16:41:52.223

Although motion correction in medical imaging is well established and has attracted much interest and research funding, a gap still exists in that there is a lack of reliable, low-cost hardware to enable such techniques to be widely adopted in healthcare. Motion correction of brain Positron Emission Tomography (PET) data for instance is an important step in realising the potential offered by modern high resolution PET scanners. Since it is not likely that subjects can remain stationary throughout the PET scan, which can last 60 minutes or more, accurate and reliable motion tracking is needed to correct the PET data for any observed motion. A commercially available marker based motion tracking system was evaluated and found to produce unreliable data. This was due to the possibility of the tracking tool slipping from the subject. This thesis describes the investigations into alternative and novel tracking techniques for use in PET. These included a markerless tracking system using the Microsoft Kinect (a low cost depth sensor) as well as a multiple target marker tracking system. The performance characteristics of both systems (low cost, high spatial and temporal accuracy, and real-time operation) were evaluated using phantom and clinical experiments. Investigations into using these two tracking techniques in whole body PET, specifically measuring the respiratory rate during lung imaging, were developed and compared against current commercially available solutions.

This research focuses on developing an algorithm set to track the vine tendril motion of a passiflora incarnate, commonly referred to as the passion fruit plant, to facilitate research into if there is a correlation between plant motion and plant health. An evaluation was done of clustering based color segmentation with a focus on K-means, feature / texture segmenta- tion utilizing Scale Invariant Feature Transforms (SIFT), and temporal based segmentation using Gaussian Mixture Model Background Subtraction to segment out the tendril in each video frame. Morphological image processing methods, such as dilation and connected com- ponent analysis, were used to clean up the segmentation results to give an estimate of the vine tendril’s location at each frame. Kalman filtering was then used to track the tendril’s location through the different frames dealing with large jumps in tendril location, cases where the tendril remained stationary between frames, and cases where there was error in the segmentation process. The resulting algorithm set was successful at tracking the tendril during times when the tendril had large jumps in position and it almost always succeeded in keeping track of the tendril during errors in the segmentation due to lack of tendril motion. The few cases that were not successful were evaluated and suggestions were made to resolve these issues in future data collection. / Master of Science / This research focused on developing an algorithm sequence that could find the tendril of a passiflora incarnate, commonly referred to as the passion fruit plant, in a single frame of a video and then track that tendril through the different frames in the video. Having the ability to track a plant tendril through a video allows biologists to research if there is a link between the amount a plant moves and the plant’s health. The algorithms evaluated for finding the plant in the image used color, features and motion to try and distinguish the tendril from the rest of the image. After the tendril was found, a tracking algorithm that combined a prediction from a model for the tendril’s location with the measured location was used to deal with noise and errors in the measurement. It was found that using the motion based algorithm worked the best to find the tendril (with the addition of some image processing to remove noise). This combined with the tracking algorithm allowed for the tendril to be successfully tracked through the different frames with one exception. Future work and recommendations were made to deal with this exception.

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No / The authors present the Virtual Park (or VPark) system. This includes a networked virtual environment (NVE) system, called W-VLNET (Windows Virtual Life Network) and an Attraction Building System that creates and modifies attractions used in the NVE system. This article details the techniques for communication, scene management, facial and body animation, and general user interaction modules. The authors used VRML97 and MPEG-4 SHNC to stress the compatibility of the system with other similar virtual reality systems. The software provides realistic virtual actors as well as sets of applicable high-level actions in real time. Using this software, users can introduce their own scenario-based applications into a shared virtual environment.

abstract: Head movement is a natural orienting behavior for sensing environmental events around us. Head movement is particularly important for identifying through the sense of hearing the location of an out-of-sight, rear-approaching target to avoid danger or threat. This research aims to design a portable device for detecting the head movement patterns of common marmoset monkeys in laboratory environments. Marmoset is a new-world primate species and has become increasingly popular for neuroscience research. Understanding the unique patterns of their head movements will improve its values as a new primate model for uncovering the neurobiology of natural orienting behavior. Due to their relatively small head size (5 cm in diameter) and body weight (300-500 g), the device has to meet several unique design requirements with respect to accuracy and workability. A head-mount wireless tracking system was implemented based on inertial sensors that are capable of detecting motion in the Yaw, Pitch and Roll axes. The sensors were connected to the encoding station, which transmits wirelessly the 3-axis movement data to the decoding station at the sampling rate of ~175 Hz. The decoding station relays this information to the computer for real-time display and analysis. Different tracking systems, based on the accelerometer and Inertial Measurement Unit is implemented to track the head movement pattern of the marmoset head. Using these systems, translational and rotational information of head movement are collected, and the data analysis focuses on the rotational head movement in body-constrained marmosets. Three stimulus conditions were tested: 1) Alert, 2) Idle 3) Sound only. The head movement patterns were examined when the house light was turned on and off for each stimulus. Angular velocity, angular displacement and angular acceleration were analyzed in all three axes. Fast and large head turns were observed in the Yaw axis in response to the alert stimuli and not much in the idle and sound-only stimulus conditions. Contrasting changes in speed and range of head movement were found between light-on and light-off situations. The mean peak angular displacement was 95 degrees (light on) and 55 (light off) and the mean peak angular velocity was 650 degrees/ second (light on) and 400 degrees/second (light off), respectively, in response to the alert stimuli. These results suggest that the marmoset monkeys may engage in different modes of orienting behaviors with respect to the availability of visual cues and thus the necessity of head movement. This study provides a useful tool for future studies in understanding the interplay among visual, auditory and vestibular systems during nature behavior. / Dissertation/Thesis / Masters Thesis Bioengineering 2015

The new industry revolution creates a vast transformation in the manufacturing methods. Embedded Intelligence and communication technologies facilitate the execution of the smart factory. It can provide lots of features for strong customization of products. Assembly system is a critical segment of the smart factory. However, the complexity of production planning and the variety of products being manufactured, persuade the factories to use different methods to guide the workers for unfamiliar tasks in the assembly section. Motion tracking is the process of capturing the movement of human body or objects which has been used in different industrial systems. It can be integrated to a wide range of applications such as interacting with computers, games and entertainment, industry, etc. Motion tracking can be integrated to assembly systems and it has the potential to create an improvement in this industry as well. But the integration of motion tracking in industrial processes is still not widespread. This thesis work provides a fully automatic tracking solution for future systems in manufacturing industry and other fields. In general a configurable, flexible, and scalable motion tracking system is created in this thesis work to amend the tracking process. According to our environment, we have done a research between different motion tracking methods and technologies including Kinect and Leap Motion sensor, and finally the leap motion sensor is selected as the most appropriate method, because it fulfils our demands in this project. Multiple Leap motion sensors are used in this work to cover areas with different size. Data fusion between multiple leap motion sensors can be considered as another novel contribution of this thesis work. To achieve this goal data from multiple sensors are combined. This system can improve the lack of accuracy in order to creating a practical industrial application. By fusion of several sensors in order to achieve accuracies that allow implementation in practice, a motion tracking system with higher accuracy is created.