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Open-loop position estimation methods are commonly used in mobile robot applications. Their strength lies in the speed and simplicity with which an estimated position is determined. However, these methods can lead to inaccurate or unreliable estimates. Two methods are developed in this thesis. The first uses a single optical sensor and can accurately estimate position under ideal conditions and when wheel slip perpendicular to the axis of the wheel occurs. A second method which uses two optical sensors is developed which can accurately estimate position even when wheel slip parallel to the axis of the wheel occurs. Location of the optical sensors is investigated in order to minimize errors caused by inaccurate sensor readings. Finally, the method is implemented and tested using a potential field based navigation scheme. Estimates of position were found to be as accurate as dead-reckoning in ideal conditions and much more accurate in cases where kinematic violations occur.

Rotary disc mowers are capable of much higher throughput than traditional mowers, and as a result have much higher power demands. With the recent increasing popularity of rotary mowers and the ever-increasing size of high-capacity forage and haying equipment, manufacturers are offering larger mowers with higher power demands. A disc mower cutterbar requires a significant amount of the total implement power, and little research has been performed relating to the study of power requirements and material movement. The objectives of this research were to develop a means of measuring cutterbar power requirements and material flow, and to perform a statistical design of the mower in operation. Using these results, it may be possible to offer insight into changes that could be considered in the design of rotary mower cutterbars.<p> Two types of experiments were performed on a prototype disc mower. Both experiments were performed in both alfalfa and light grass, at three different ground speeds, and at three different disc rotational velocities. The first experiment consisted of measuring the power requirements and specific energy of three individual discs on the prototype cutterbar. The rotational direction of the three adjacent discs investigated produce converging and diverging cutting zones. Measurements were made by means of instrumented drive hubs, each with individual onboard data acquisition systems. Average power measurements recorded by each instrumented hub were found to be approximately 2.45 and 3.31 kW for alfalfa and grass, respectively. Likewise, average specific energy measurements for alfalfa and grass ranged from 1.83 to 5.74 kWh/t, respectively. The second experiment involved the optical flow field calculation from high-speed videos captured of the cutterbar in operation. A phase-based optical flow algorithm was applied to videos captured to study material flow across the cutterbar.<p> An analytical model and two regression models were developed to describe and predict the cutterbar specific energy at the converging and diverging zones. The analytical model was based on the cutting and transport processes as performed by the rotating discs, as well as the zero-load power. The model included the results of the averaged material flow vector angles. The regression models were fitted to the experimental specific energy results as a function of the different combinations of effects in the experimental design. All three models, which were produced for both the converging and diverging cutting zones, were found with coefficient of determination values between 0.79 and 0.96.

Rotary disc mowers are capable of much higher throughput than traditional mowers, and as a result have much higher power demands. With the recent increasing popularity of rotary mowers and the ever-increasing size of high-capacity forage and haying equipment, manufacturers are offering larger mowers with higher power demands. A disc mower cutterbar requires a significant amount of the total implement power, and little research has been performed relating to the study of power requirements and material movement. The objectives of this research were to develop a means of measuring cutterbar power requirements and material flow, and to perform a statistical design of the mower in operation. Using these results, it may be possible to offer insight into changes that could be considered in the design of rotary mower cutterbars.<p> Two types of experiments were performed on a prototype disc mower. Both experiments were performed in both alfalfa and light grass, at three different ground speeds, and at three different disc rotational velocities. The first experiment consisted of measuring the power requirements and specific energy of three individual discs on the prototype cutterbar. The rotational direction of the three adjacent discs investigated produce converging and diverging cutting zones. Measurements were made by means of instrumented drive hubs, each with individual onboard data acquisition systems. Average power measurements recorded by each instrumented hub were found to be approximately 2.45 and 3.31 kW for alfalfa and grass, respectively. Likewise, average specific energy measurements for alfalfa and grass ranged from 1.83 to 5.74 kWh/t, respectively. The second experiment involved the optical flow field calculation from high-speed videos captured of the cutterbar in operation. A phase-based optical flow algorithm was applied to videos captured to study material flow across the cutterbar.<p> An analytical model and two regression models were developed to describe and predict the cutterbar specific energy at the converging and diverging zones. The analytical model was based on the cutting and transport processes as performed by the rotating discs, as well as the zero-load power. The model included the results of the averaged material flow vector angles. The regression models were fitted to the experimental specific energy results as a function of the different combinations of effects in the experimental design. All three models, which were produced for both the converging and diverging cutting zones, were found with coefficient of determination values between 0.79 and 0.96.

Optical flow indicates a computing method which utilizes the brightness variation of image motion in further image disposition, without the prior understanding of field, environment, or related object. It also reflects the image variation to compute the variation of optical flow field due to the motion of time and distance. The Essay content follows the optical flow as its basis theory consideration to find the direction of image motion. It utilizes the auto-focus principle to search the corrective focus basis, to proceed the identify analysis through the target object. To obtain the visual tracking result after the auto-focus of image definition, moving direction when achieve the target object. The application method is easily to determine the movement or stationary target in the certain field.

Displacement information is important in dynamic image analysis. The method of optical flow has been well applied to compute the displacement in the field of computer vision. We apply the method of optical flow to compute the displacement information for video compression. We can predict the optical flow between picture 2 and picture 3 by the optical flow between picture 1 and picture 2 by using the principle of inertia. Using the predicted optical flow between picture 2 and picture 3, we can recover a rough version of picture 3. This version can be taken as a reference picture for encoding picture 3. This reference will decrease the compensation information in the following stage, and then improve the compression ratio of MPEG . We modified the traditional optical flow of Horn & Schunck to a regional optical flow by segmentation. Then, the displacement information could be reduced. The picture is recovered by the optical flow in a modified way because some objects couldn¡¦t move in the same direction and velocity. We propose two methods in optical flow prediction. One is the complete information: shape and value. The other is shape only with value recomputed and extra encoded. Experiments demonstrate a better compression ratio of 1% for our motion compensation than the regular motion compensation.

<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.