In this paper, we propose an accurate estimation of the camera motion in a dynamic environment from RGB-D videos. To better exclude the moving object portion of the scene from the stationary background, we use image segmentation. Next, dense pixel matching between the current and reference color images is performed to construct the 3D point cloud for dense motion estimation. At the end, we perform motion optimization, i.e., to find the combination of motion parameters that minimizes the remainder difference between the reference and the current image. We validate our proposed method across two benchmark sequences and show that our approach is more accurate than the existing solutions. We show that our method reduces the RMSE by 6.55% and 7.16% for stationary and dynamic scenes, respectively.

Crosstalk is the most critical artifact in stereoscopic 3D displays. Circularly polarized (CP) 3D LCDs with passive glasses has the issue of crosstalk especially when the viewer moves vertically away from the screen center. In this work, a new display model for CP LCD is proposed which considers both the intrinsic optical system including the polarizer and the wave retarder, and the vertical misalignment (VM) of light between the liquid crystal (LC) and the patterned retarder (PR) as the two major factors causing crosstalk in CP LCDs. The optical modeling for the display uses Mueller calculus and the characterization of VM is realized by the proposed user-calibration method. The display model is proved to be accurate by both measuring and subjective test in predicting the crosstalk resulted from different viewing scenarios. Additionally, we proposed considering image texture for the estimation of crosstalk perception. The proposed estimation is shown to be consistent with the viewer's observation.

We propose a fast and robust 2D-affine global motion estimation algorithm based on phase-correlation in the Fourier-Mellin domain and robust least square model fitting of sparse motion vector field and its application for digital image stabilization. Rotation-scale-translation (RST) approximation of affine parameters is obtained at the coarsest level of the image pyramid, thus ensuring convergence for a much larger range of motions. Despite working at the coarsest resolution level, using subpixel-accurate phase correlation provides sufficiently accurate coarse estimates for the subsequent refinement stage of the algorithm. The refinement stage consists of RANSAC based robust least-square model fitting for sparse motion vector field, estimated using block-based subpixel-accurate phase correlation at randomly selected high activity regions in finest level of image pyramid. Resulting algorithm is very robust to outliers such as foreground objects and flat regions. We investigate the robustness of the proposed method for digital image stabilization application. Experimental results show that the proposed algorithm is capable of estimating larger range of motions as compared to another phase correlation method and optical flow algorithm.