Ikuko Tsubaki

An adaptive video stabilization method for reducing visually induced motion sickness

Visually induced motion sickness (VIMS) is sometimes brought by watching video sequences acquired by handheld video cameras. We present a video stabilization method for the purpose of reducing VIMS. In our method, the scenes including oscillatory motion which can cause VIMS are selected to be stabilized. In the subjective evaluation results, the severity of VIMS is reduced.

Robust global motion estimation in video stabilization for reducing visually induced motion sickness

We present robust global motion estimation by MDPE algorithm for video stabilization. MDPE algorithm is a robust model fitting method, which has high breakdown point. We evaluate the performance of global motion estimation by robust model fitting methods, M-estimator, RANSAC and MDPE. An improvement of the MDPE for video stabilization is also presented. Finally, the video stabilization based on our robust global motion estimation is examined.

Depth remapping using seam carving for depth image based rendering

Depth remapping is a technique to control depth range of stereo images. Conventional remapping which uses a transform function in the whole image has a stable characteristic, however it sometimes reduces the 3D appearance too much. To cope with this problem, a depth remapping method which preserves the details of depth structure is proposed. We apply seam carving, which is an effective technique for image retargeting, to depth remapping. An extended depth map is defined as a space-depth volume, and a seam surface which is a 2D monotonic and connected manifold is introduced. The depth range is reduced by removing depth values on the seam surface from the space-depth volume. Finally a stereo image pair is synthesized from the corrected depth map and an input color image by depth image based rendering.

Motion deblurring for suppression of breathing distortions caused by camera shake

Time varying motion blur is sometimes brought by camera shake, and alternate blurred frames and sharp frames are included in video clips. This degradation is considered a kind of breathing distortion, which means oscillatory changes of blur. We propose a motion deblurring method which aims to suppress breathing distortions. A spatio-temporal regularization is introduced into the deconvolution approach to smooth the temporal change of blur.