Improving Mobile Augmented Reality User Experience on Smartphones

摘要

This thesis focuses on improving the user experience for computer vision-based Augmented Reality (AR) applications on smartphones. The first part shows our proposed methods to enhance image binarisation. This improves the marker detection results in mobile AR applications. The comparisons of the original ARToolKit binarization method, our proposed histogram-based automatic thresholding and our histogram equalization based thresholding show that the histogram-based automatic thresholding produces a relatively better result under extreme and normal lighting conditions but slightly reduces the ARToolKit framerate. The second part introduces a new fast painterly rendering algorithm which produces an immersive experience for mobile AR users. The proposed algorithm has low complexity and achieves a real-time performance on smartphones. In addition, this study has carried out a preliminary experiment comparing mobile GPU-based image processing algorithms with CPU-based equivalent on smartphones. The result indicates that the GPU-based implementations perform better than the CPU when processing relatively large sized images.