Prostate cancer is prevalent among men and one of the few cancer sites where local therapies currently target the entire organ instead of tumour. MRI holds promise in accurately depicting regions of cancer burden within the prostate gland and guiding tumour-targeted diagnostics and therapeutics. The clinical performance of a novel stereotactic MRI-guided needle navigation system for prostate cancer targeting was evaluated. Mean absolute in-plane stereotactic needle-targeting error for 10 patients was 2.2 mm and mean absolute depth error was 6.5 mm, highlighting a need to improve technical accuracy of the system. Consequently, alternative stereotactic registration techniques were investigated. Metrics of performance were in-plane stereotactic needle-targeting error, depth error, and registration time. A Z-shaped fiducial motif using automated registration performed best in phantom experiments with an in-plane error of 2.0 mm and depth error of 1.0 mm. These results will guide further software and hardware development to improve clinical performance.

The research accomplished in this dissertation is focused on the design of effective solutions to the problem that error codes occur in the ZigBee-based meeting attendance registration system. In this work, several different check algorithms are compared, and the powerful error-detecting Cyclic Redundancy Check (CRC) algorithm is studied. In view of the features of the meeting attendance registration system, we implement the check module of CRC-8. This work also considers the data reliability. We assume use retransmission mechanism to ensure the validity and completeness of transmission data. Finally, the potential technical improvement and future work are presented.

Perceptual-cognitive skills are important characteristics for sport participants, which have been shown to contribute to the expert advantage (Abernethy, Baker & Côté, 2005; Mann, Williams, Ward, & Janelle, 2004; McPherson, 2000). One such skill is visual attention, which is beneficial for athletes, but less commonly researched for sport officials. For this dissertation, three data collection procedures assisted in examining the visual behaviors of elite, intermediate and novice ice hockey referees. In phase one, 2 elite, 2 intermediate, and 2 novice referees wore helmet cameras for one game and subsequently participated in stimulated recall interviews to address visual behaviors that occurred during that game. The four resultant themes that emerged were: Divided Attention, Selective Attention, Positioning and Context, and Influences of Visual Attention. Within each of these major themes there were several similarities and differences amongst the referees. In phase two, 2 elite, 2 intermediate, and 2 novice focus groups watched one elite and one intermediate helmet camera videotape and discussed what they thought the referee was attending to and where they would direct their visual attention. The focus group transcripts were deductively coded to search for potential differences between the elite and intermediate referees based on the themes identified in phase one. It was evident that the elite referee was superior to the intermediate in several areas including: Maintaining a focus on the majority of players, knowing when to focus away from the puck, having better post-whistle attention, and being better positioned. Discussion related to how these advantages might be gained by learning through experience. For phase three, 10 elite, 10 intermediate, and 10 novice referees wore an eye-tracking device and made penalty decisions on ice hockey infractions presented on a computer screen. In this experiment, decision accuracy, decision type, number of fixations, and fixation duration were calculated. MANOVA results indicated that there were no significant differences across participant groups. The global discussion includes data excluded from the three main papers, alternative methods for further interpretation of the results, integration of the results of the three papers, and proposals for future research.

Object tracking is an important problem with wide ranging applications. The purpose is to detect object contours and track their motion in a video. Issues of concern are to be able to map objects correctly between two frames, and to be able to track through occlusion. This thesis discusses a novel framework for the purpose of object tracking which is inspired from image registration and segmentation models. Occlusion of objects is also detected and handled in this framework in an appropriate manner. The main idea of our tracking framework is to reconstruct the sequence of images in the video. The process involves deforming all the objects in a given image frame, called the initial frame. Regularization terms are used to govern the deformation of the shape of the objects. We use elastic and viscous fluid model as the regularizer. The reconstructed frame is formed by combining the deformed objects with respect to the depth ordering. The correct reconstruction is selected by parameters that minimize the difference between the reconstruction and the consecutive frame, called the target frame. These parameters provide the required tracking information, such as the contour of the objects in the target frame including the occluded regions. The regularization term restricts the deformation of the object shape in the occluded region and thus gives an estimate of the object shape in this region. The other idea is to use a segmentation model as a measure in place of the frame difference measure. This is separate from image segmentation procedure, since we use the segmentation model in a tracking framework to capture object deformation. Numerical examples are presented to demonstrate tracking in simple and complex scenes, alongwith occlusion handling capability of our model. Segmentation measure is shown to be more robust with regard to accumulation of tracking error.

Object tracking is an important problem with wide ranging applications. The purpose is to detect object contours and track their motion in a video. Issues of concern are to be able to map objects correctly between two frames, and to be able to track through occlusion. This thesis discusses a novel framework for the purpose of object tracking which is inspired from image registration and segmentation models. Occlusion of objects is also detected and handled in this framework in an appropriate manner. The main idea of our tracking framework is to reconstruct the sequence of images in the video. The process involves deforming all the objects in a given image frame, called the initial frame. Regularization terms are used to govern the deformation of the shape of the objects. We use elastic and viscous fluid model as the regularizer. The reconstructed frame is formed by combining the deformed objects with respect to the depth ordering. The correct reconstruction is selected by parameters that minimize the difference between the reconstruction and the consecutive frame, called the target frame. These parameters provide the required tracking information, such as the contour of the objects in the target frame including the occluded regions. The regularization term restricts the deformation of the object shape in the occluded region and thus gives an estimate of the object shape in this region. The other idea is to use a segmentation model as a measure in place of the frame difference measure. This is separate from image segmentation procedure, since we use the segmentation model in a tracking framework to capture object deformation. Numerical examples are presented to demonstrate tracking in simple and complex scenes, alongwith occlusion handling capability of our model. Segmentation measure is shown to be more robust with regard to accumulation of tracking error.

One of the most important problems in real-time, mobile augmented reality is *registration error* -- the misalignment between the computer generated graphics and the physical world the application is trying to augment. Such misalignment may either cause the information presented by the application to be misleading to the user or make the augmentation meaningless. In this work, we question the implied assumption that registration error must be eliminated for AR to be useful. Instead, we take the position that registration error will never be eliminated and that application developers can build useful AR applications if they have an estimate of registration error. We present a novel approach to AR application design: *Spatially Adaptive Augmented Reality* (i.e., applications that change their displays based on the quality of the alignment between the physical and virtual world). The computations used to change the display are based on real-time estimates of the registration error. The application developer uses these estimates to build applications that function under a variety of conditions independent of specific tracking technologies. In addition to introducing Spatially Adaptive AR, this research establishes a theoretical model for AR. These theoretical contributions are manifested in a toolkit that supports the design of Spatially Adaptive AR applications: OSGAR. This work describes OSGAR in detail and presents examples that demonstrate how to use this novel approach to create adaptable augmentations as well as how to support user interaction in the presence of uncertainty.

This dissertation gives a general model for the estimation of shape (image segmentation), appearance, pose (image registration), and movement (tracking). The model can infer parameters for multiple objects in a dynamically changing scene. There are a number of real-world applications. In particular, in visual tracking, moving the camera to keep objects of interest in the field of view may cause the background to move. The objects can move and deform in three dimensions, but they must be captured in two-dimensional images. Each component of the image is represented by a separate layer: one for the background and a layer for each foreground object. Each layer has three components: a contour that bounds the region of the layer, a smooth function that represents the object's appearance, and a transformation that maps that layer into an image. The segmentation for each layer is a contour (embedded as the zero level set of a distance function) that is the average shape of the object computed from multiple images. The smooth function associated with a layer approximates the image data inside the contour, after the contour has been mapped into the image by a similarity transformation (rigid component) plus a vector field (non-rigid component). A practical application of having this model is that one can fix the size of a layer and then construct priors on both shape and appearance for that layer. These priors are constructed using principal components analysis (PCA), which reduces the dimensionality of the image-approximating smooth function and the vector field (non-rigid registration) and allows for more accurate modeling of an object for that layer.

In this paper, we propose an image registration algorithm to form a set of images to a high-resolution image. This algorithm employs a fringe projected scheme to perform the registration. The proposed algorithm provides several advantages, such as high precision, low computation cost, simple system configuration and robotic performance. An example which used three images to form a hight-resolution image was given. It was found that the resolution had enhanced 2.72 times.

This paper presents a set of Image Registration, Image Integration, interpolation and image restoration and other technology, the number of low-resolution images synthesized high-resolution image. Relative to the existing image fusion technology, the method provided in this paper has more advantages, such as: (1) high-precision value; (2)low computation cost; (3)a compact system; (4) applicable to noise images; (5) robotic and automatic performance.

This paper presents a novel integration technique for segmented 3D profiles measured by projected fringe profilometry. Fringe patterns are projected to the inspected surface. The projected patterns fix their positions relative to the tested object during two segmented measurements. Thus, finding two matched surface points becomes a problem of searching for two identical phases in the fused data sets. This novel integration technique can match images successfully and achieve pixel-to-pixel registration easily even in the presence of geometric deformation, illumination changes, and severe occlusions. It is superior to the other methods because of its: (1) High matching accuracy; (2) Improved robustness; (3) Reduced computational time; (4) Capability of compensating distortions of the optical system at every pixel location; (5) Suitable for images rotating or scaling; and (6) Suitable for any other projected fringe measurement method. We also propose a method to design and fabricate a 2-D fringe pattern which can be applied to the integration technique for segmented 3D profiles. Campered with using 1-D fringe patterns for image registration, using a 2-D fringe pattern saves the measurement time and further proveds more tolerence to hand the shadow and noise problems. Tests of the system performance have been carried out that the accuracy of the registration scheme is 5.96% of image pixel size. Therefore, this technique can be extensively used in modern high technology industry. Especially when it requires higher resolution close-up images or overcomes the issue of not every inspected object can be fully expressed just by a single full-field measurement, it is necessary to use this integration technique.