The depth information is a fundamental factor in stereo imaging. The depth information of an object can be associated to the horizontal shift (with respect to the location in the parallax image) in a simple linear relation. Therefore, we can generate a stereo video by combining a regular monoscopic video with an artificial parallax video implemented with a designed horizontal shift. In this research, the manmade horizontal shift is designed according to the pixel translations of the monoscopic video obtained by the optical flow computation algorithm. Optical flow proposed by Horn and Schunck was originally developed in the field of computer vision for the application of moving detection. Following this flow computation algorithm, a vector segmentation algorithm is applied to the flow field for grouping effects associated to the foreground. Finally, some merging procedures are applied to get a better foreground mask. With this foreground mask,the parallax video is then generated for creating our artificial stereo video.

<p>The analysis of the health condition in Rheumatoid Arthritis (RA) remains a qualitative process dependent on visual inspection by a clinician. Fully automatic techniques that can accurately classify the health of the muscle have yet to be developed. The intended purpose of this work is to develop a novel spatio-temporal technique to assist in a rehabilitation program framework, by identifying motion features inherited in the muscles in order to classify them as either healthy or diseased. Experiments are based on ultrasound image sequences during which the muscles were undergoing contraction. The proposed system uses an optical flow technique to estimate the velocity of contraction. Analyzing and manipulating the velocity vectors reveal valuable information which encourages the extraction of motion features to discriminate the healthy against the sick. Experimental results for classification prove helpful in essential developments of therapy processes and the performance of the system has been validated by the cross-validation technique “leave-one-out”. The method leads to an analytical description of both the global and local muscle’s features in a way which enables the derivation of an appropriate strategy for classification. To our knowledge this is the first reported spatio-temporal method developed and evaluated for RA assessment. In addition, the progress of physical therapy to improve strength of muscles in RA patients has also been evaluated by the features used for classification.</p>

Partial constraints are often available in visual processing tasks requiring the matching of contours in two images. We propose a non- iterative scheme to determine contour matches using locally affine transformations. The method assumes that contours are approximated by the orthographic projection of planar patches within oriented neighborhoods of varying size. For degenerate cases, a minimal matching solution is chosen closest to the minimal pure translation. Performance on noisy synthetic and natural contour imagery is reported.

The analysis of the health condition in Rheumatoid Arthritis (RA) remains a qualitative process dependent on visual inspection by a clinician. Fully automatic techniques that can accurately classify the health of the muscle have yet to be developed. The intended purpose of this work is to develop a novel spatio-temporal technique to assist in a rehabilitation program framework, by identifying motion features inherited in the muscles in order to classify them as either healthy or diseased. Experiments are based on ultrasound image sequences during which the muscles were undergoing contraction. The proposed system uses an optical flow technique to estimate the velocity of contraction. Analyzing and manipulating the velocity vectors reveal valuable information which encourages the extraction of motion features to discriminate the healthy against the sick. Experimental results for classification prove helpful in essential developments of therapy processes and the performance of the system has been validated by the cross-validation technique “leave-one-out”. The method leads to an analytical description of both the global and local muscle’s features in a way which enables the derivation of an appropriate strategy for classification. To our knowledge this is the first reported spatio-temporal method developed and evaluated for RA assessment. In addition, the progress of physical therapy to improve strength of muscles in RA patients has also been evaluated by the features used for classification.

Conventional cameras are usually small in their field of view (FOV) and make the observable region limited. Applications by such a vision system may also limit motion capabilities for robots when it comes to object tracking. Omnidirectional camera has a wide FOV which can obtain environmental data from all directions. In comparison with conventional cameras, the wide FOV of omnidirectional cameras reduces blind regions and improves tracking ability. In this thesis, we assume an omnidirectional camera is mounted on a moving platform, which travels with planar motion. By applying optical flow and CAMShift algorithm to track an object which is non-propelled and only subjected to gravity. Then, by parabolic fitting, least-square method and Levenberg-Marquardt method to predict the 3D coordinate of the object at the current instant and the next instant, we can finally predict the position of the drop point and drive the moving platform to meet the object at the drop point. The tracking operation and drop point prediction can be successfully achieved even if the camera is under planar motion and rotation.

The topic of stereo video is getting more attention among these days due to its high quality of visual effect. However, the large volume of data is the problem of its application. The topic of this thesis is to investigate a compression technique by Wavelet compression on the stereo video data. There is much similarity between the parallax videos. This similarity is obtained by a motion compensation technique: the optical flow computing. Optical flow proposed by Horn and Schunck was originally developed in the field of computer vision for the application of moving detection. In this thesis we apply the optical flow to compress the similarity information between the parallax stereo video. On the other hand, the Wavelet transformation has been proved to be a successful technique for multiscale modeling. We therefore applying the Wavelet transform combined with the zerotree compression to compress the fields of optical flow. Experimental results in this thesis have demonstrated different effects in different situations.

Optical flow, caused by relative motion of the object and the viewer, is the distribution of apparent velocities of brightness pattern in an image. The advantage of the optical-flow-based visual servo method is that feature of the object does not need to be defined or known in advance. This research plans to build an image servo technique to deal with the problem of 3D relative motion of the viewer and the environment. The images are treated as input and output signals of the control system and are fed back to extract the relative velocity information between contiguous image patterns. Then the video camera will automatically follow the motion to maintain the target image unchanged.

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Optical flow and its extensions have been widely used in motion detection and computer vision. In the study, principal component analysis (PCA) is applied to analyze optical flows for better motion detection performance. The joint optical flow and PCA approach can efficiently detect moving objects and suppress small turbulence. It is effective in both static and dynamic background. It is particularly useful for motion detection from outdoor videos with low quality and small moving objects. Experimental results demonstrate that this approach outperforms other existing methods by extracting the moving objects more completely with lower false alarms. Saving strategies are developed to reduce computational complexity of optical flow calculation and PCA. Graphic processing unit (GPU)-based parallel implementation is developed, which shows excellent speed up performance.

Understanding the motion of a camera from only the image(s) it captures is a di cult problem. At best we might hope to estimate the relative motion between camera and scene if we assume a static subject, but once we start considering scenes with dynamic content it becomes di cult to di↵erentiate between motion due to the observer or motion due to scene movement. In this thesis we show how the invaluable cues provided by inertial sensor data can be used to simplify motion analysis and relax requirements for several computer vision problems. This work was funded by the University of Bath.