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With the development of technologies, robots have played an important role in many fields of the society. They help people to deal with a large amount of work, especially operate in the extremely dangerous environment instead of people. For a robot, effective obstacle avoidance is still a challenge in the development of robot. The existing systems sometimes combine with multi-devices to conquer this challenge so that the expensive cost has been as a negative factor that cumbers the application of robot. For this purpose, find a way with the low equipment requirement but still having the high accuracy is essential. Optic flow as another algorithm coming from bee vision has been used to help robots avoid obstacles for many years. And it owns many advantages. This study presents a system based on the optic flow is developed to avoid obstacle in the view-field of the robot. The main point in this thesis is to show how the system works under an assumed environment for robot navigation, and compare the results to Thomas’ to see whether the low equipment requirement can also achieve the purpose of avoiding obstacles.

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This thesis presents a complete self-calibrating illumination intensity-invariant colour classification system. We extend a novel fuzzy colour processing tech- nique called Fuzzy Colour Contrast Fusion (FCCF) by combining it with a Heuristic- assisted Genetic Algorithm (HAGA) for automatic fine-tuning of colour descriptors. Furthermore, we have improved FCCF’s efficiency by processing colour channels at varying colour depths in search for the optimal ones. In line with this, we intro- duce a reduced colour depth representation of a colour image while maintaining efficient colour sensitivity that suffices for accurate real-time colour-based object recognition. We call the algorithm Variable Colour Depth (VCD) and we propose a technique for building and searching a VCD look-up table (LUT). The first part of this work investigates the effects of applying fuzzy colour contrast rules to vary- ing colour depths as we extract the optimal rule combination for any given target colour exposed under changing illumination intensities. The second part introduces the HAGA-based parameter-optimisation for automatically constructing accurate colour classifiers. Our results show that for all cases, the VCD algorithm, combined with HAGA for parameter optimisation improve colour classification via a pie-slice colour classifier.For 6 different target colours, the hybrid algorithm was able to yield 17.63% higher overall accuracy as compared to the pure fuzzy approach. Fur- thermore, it was able to reduce LUT storage space by 78.06% as compared to the full-colour depth LUT.

This thesis studies rapid object detection, focusing on feature-based methods. Firstly, modifications of training and detection of the Viola-Jones method are made to improve performance and overcome some of the current limitations such as rotation, occlusion and articulation. New classifiers produced by training and by converting existing classifiers are tested in face detection and hand detection. Secondly, the nature of invariant features in terms of the computational complexity, discrimination power and invariance to rotation and scaling are discussed. A new feature extraction method called Concentric Discs Moment Invariants (CDMI) is developed based on moment invariants and summed-area tables. The dimensionality of this set of features can be increased by using additional concentric discs, rather than using higher order moments. The CDMI set has useful properties, such as speed, rotation invariance, scaling invariance, and rapid contrast stretching can be easily implemented. The results of experiments with face detection shows a clear improvement in accuracy and performance of the CDMI method compared to the standard moment invariants method. Both the CDMI and its variant, using central moments from concentric squares, are used to assess the strength of the method applied to hand-written digits recognition. Finally, the parallelisation of the detection algorithm is discussed. A new model for the specific case of the Viola-Jones method is proposed and tested experimentally. This model takes advantage of the structure of classifiers and of the multi-resolution approach associated with the detection method. The model shows that high speedups can be achieved by broadcasting frames and carrying out the computation of one or more cascades in each node.

Digital colour cameras are dramatically falling in price, making them a ordable for ubiquitous appliances in many applications. Change in colour perception with changing light conditions induce errors that may escape a user's awareness. Colour constancy algorithms are based on inferring light properties (usually the white point) to correct colour. Other attempts using more data for colour correction such as (ICC based) colour management characterise a capturing device under given conditions through an input device pro le. This pro le can be applied to correct for deviating colour perception. But this pro le is only valid for the speci c conditions at the time of the characterisation, but fails with changes in light. This research presents a solution to the problem of long time observations with changes in the scene's illumination for common natural (overcast or clear, blue sky) and arti cial sources (incandescent or uorescent lamps). Colour measurements for colour based reasoning need to be represented in a robustly de ned way. One such suitable and well de ned description is given by the CIE LAB colour space, a device-independent, visually linearised colour description. Colour transformations using ICC pro le are also based on CIE colour descriptions. Therefore, also the corrective colour processing has been based on ICC based colour management. To verify the viability of CIE LAB based corrective colour processing colour constancy algorithms (White Patch Retinex and Grey World Assumption) have been modi ed to operate on L a b colour tuples. Results were compared visually and numerically (using colour indexing) against those using the same algorithms operating on RGB colour tuples. We can take advantage of the fact that we are dealing with image streams over time, adding another dimension usable for analysis. A solution to the problem of slowly changing light conditions in scenes with a static camera perspective is presented. It takes advantage of the small (frame-to-frame) changes in appearance of colour within the scene over time. Reoccurring objects or (background) areas of the scene are tracked to gather data points for an analysis. As a result, a suitable colour space distortion model has been devised through a rst order Taylor approximation (a ne transformation). By performing a multidimensional linear regression analysis on the tracked data points, parameterisations for the a ne transformations were derived. Finally, the device pro le is updated by amalgamating the corrections from the model into the ICC pro le for a single, comprehensive transformation. Following applications of the ICC based colour pro les are very fast and can be used in real-time with the camera's capturing frame rate (for current normal web cameras and low spec desktop computers). As light conditions usually change on a much slower time scale than the capturing rate of a camera, the computationally expensive pro le adaptation generally showed to be usable for many frames. The goal was to set out and nd a solution for consistent colour capturing using digital cameras, which is capable of coping with changing light conditions. Theoretical backgrounds and strategies for such a system have been devised and implemented successfully.

Digital colour cameras are dramatically falling in price, making them a ordable for ubiquitous appliances in many applications. Change in colour perception with changing light conditions induce errors that may escape a user's awareness. Colour constancy algorithms are based on inferring light properties (usually the white point) to correct colour. Other attempts using more data for colour correction such as (ICC based) colour management characterise a capturing device under given conditions through an input device pro le. This pro le can be applied to correct for deviating colour perception. But this pro le is only valid for the speci c conditions at the time of the characterisation, but fails with changes in light. This research presents a solution to the problem of long time observations with changes in the scene's illumination for common natural (overcast or clear, blue sky) and arti cial sources (incandescent or uorescent lamps). Colour measurements for colour based reasoning need to be represented in a robustly de ned way. One such suitable and well de ned description is given by the CIE LAB colour space, a device-independent, visually linearised colour description. Colour transformations using ICC pro le are also based on CIE colour descriptions. Therefore, also the corrective colour processing has been based on ICC based colour management. To verify the viability of CIE LAB based corrective colour processing colour constancy algorithms (White Patch Retinex and Grey World Assumption) have been modi ed to operate on L a b colour tuples. Results were compared visually and numerically (using colour indexing) against those using the same algorithms operating on RGB colour tuples. We can take advantage of the fact that we are dealing with image streams over time, adding another dimension usable for analysis. A solution to the problem of slowly changing light conditions in scenes with a static camera perspective is presented. It takes advantage of the small (frame-to-frame) changes in appearance of colour within the scene over time. Reoccurring objects or (background) areas of the scene are tracked to gather data points for an analysis. As a result, a suitable colour space distortion model has been devised through a rst order Taylor approximation (a ne transformation). By performing a multidimensional linear regression analysis on the tracked data points, parameterisations for the a ne transformations were derived. Finally, the device pro le is updated by amalgamating the corrections from the model into the ICC pro le for a single, comprehensive transformation. Following applications of the ICC based colour pro les are very fast and can be used in real-time with the camera's capturing frame rate (for current normal web cameras and low spec desktop computers). As light conditions usually change on a much slower time scale than the capturing rate of a camera, the computationally expensive pro le adaptation generally showed to be usable for many frames. The goal was to set out and nd a solution for consistent colour capturing using digital cameras, which is capable of coping with changing light conditions. Theoretical backgrounds and strategies for such a system have been devised and implemented successfully.

Digital colour cameras are dramatically falling in price, making them a ordable for ubiquitous appliances in many applications. Change in colour perception with changing light conditions induce errors that may escape a user's awareness. Colour constancy algorithms are based on inferring light properties (usually the white point) to correct colour. Other attempts using more data for colour correction such as (ICC based) colour management characterise a capturing device under given conditions through an input device pro le. This pro le can be applied to correct for deviating colour perception. But this pro le is only valid for the speci c conditions at the time of the characterisation, but fails with changes in light. This research presents a solution to the problem of long time observations with changes in the scene's illumination for common natural (overcast or clear, blue sky) and arti cial sources (incandescent or uorescent lamps). Colour measurements for colour based reasoning need to be represented in a robustly de ned way. One such suitable and well de ned description is given by the CIE LAB colour space, a device-independent, visually linearised colour description. Colour transformations using ICC pro le are also based on CIE colour descriptions. Therefore, also the corrective colour processing has been based on ICC based colour management. To verify the viability of CIE LAB based corrective colour processing colour constancy algorithms (White Patch Retinex and Grey World Assumption) have been modi ed to operate on L a b colour tuples. Results were compared visually and numerically (using colour indexing) against those using the same algorithms operating on RGB colour tuples. We can take advantage of the fact that we are dealing with image streams over time, adding another dimension usable for analysis. A solution to the problem of slowly changing light conditions in scenes with a static camera perspective is presented. It takes advantage of the small (frame-to-frame) changes in appearance of colour within the scene over time. Reoccurring objects or (background) areas of the scene are tracked to gather data points for an analysis. As a result, a suitable colour space distortion model has been devised through a rst order Taylor approximation (a ne transformation). By performing a multidimensional linear regression analysis on the tracked data points, parameterisations for the a ne transformations were derived. Finally, the device pro le is updated by amalgamating the corrections from the model into the ICC pro le for a single, comprehensive transformation. Following applications of the ICC based colour pro les are very fast and can be used in real-time with the camera's capturing frame rate (for current normal web cameras and low spec desktop computers). As light conditions usually change on a much slower time scale than the capturing rate of a camera, the computationally expensive pro le adaptation generally showed to be usable for many frames. The goal was to set out and nd a solution for consistent colour capturing using digital cameras, which is capable of coping with changing light conditions. Theoretical backgrounds and strategies for such a system have been devised and implemented successfully.