Congratulations to Hyunsuk Ko, an MCL member, for passing his defense this afternoon. His thesis title is “Advanced Techniques for Stereoscopic image rectification and quality assessment”. His thesis guidance committee includes Jay Kuo (Chair), Sandy Sawchuk and Aiichiro Nakano (Outside Member). The committee gave a lot of praise to the quality of Hyunsuk’s thesis and his excellent presentation. The following is the abstract of Hyunsuk’s thesis.
“New frameworks for an objective quality evaluation and an image rectification of stereoscopic image pairs are presented in this work. First, quality assessment of stereoscopic image pairs is more complicated than that for 2D images since it is a multi-dimensional problem where the quality is affected by distortion types as well as the relation between the left and right views such as different types/levels of distortion in two views. In our work, we first introduce a novel formula-based metric that provide better results than several existing methods. However, the formula-based metric still has its limitation. For further improvement, we propose a parallel boosting system based quality index. That is, we classify distortion types into groups and design a set of scorer to handle them separately. At stage 1, each scorer generates its own score for a specific distortion type. At stage 2, all intermediate scores are fused to predict the final quality index with nonlinear regression. Experimental results demonstrate that the proposed quality index outperforms most of state-of-the art quality assessment methods by a significant margin over different databases. Secondly, a novel algorithm for uncalibrated stereo image-pair rectification under the constraint of geometric distortion, called USR-CGD, is presented in this work. Although it is straightforward to define a rectifying transformation (or homography) given the epipolar geometry, many existing algorithms have unwanted geometric distortions as a side effect. To obtain rectified images with reduced geometric distortions while maintaining a small rectification error, we parameterize the homography by considering the influence of various kinds of geometric distortions. Next, we define several geometric measures and incorporate them into a new cost function for parameter optimization. Finally, we propose a constrained adaptive optimization scheme to allow a balanced performance between the rectification error and the geometric error. Extensive experimental results are provided to demonstrate the superb performance of the proposed USR-CGD method, which outperforms existing algorithms by a significant margin.”
We wish Hyunsuk the very best in his job hunting.