Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids

We present a global optimization approach to optical flow estimation. The approach optimizes a classical optical flow objective over the full space of mappings between discrete grids. No descriptor matching is used. The highly regular structure of the space of mappings enables optimizations that reduce the computational complexity of the algorithm's inner loop from quadratic to linear and support efficient matching of tens of thousands of nodes to tens of thousands of displacements. We show that one-shot global optimization of a classical Horn-Schunck-type objective over regular grids at a single resolution is sufficient to initialize continuous interpolation and achieve state-of-the-art performance on challenging modern benchmarks.Comment: To be presented at CVPR 201

Computation of cross-talk alignment by mixed integer linear programming

Noise analysis has been an important and difficult part of design flow of very large-scale integrated (VLSI) systems in many years. In this thesis, the problem of signal alignment resulting in possible maximum peak interconnect coupling noise and propose a variation aware technique for computing combined noise pulse taking into account timing constraints on signal transitions has been discussed. This work shows that the worst noise alignment algorithm can be formulated as mixed integer programming (MLIP) problem both in deterministic window cases and variational window cases. For deterministic window cases, it is assumed that timing windows are given for each aggressor inputs and the victim net is quite. It compares the results from proposed method with the most known and widely used method for computing the worst aggressor alignment - sweeping line algorithm, to verify its correctness and efficiency. For variation window cases, as variations of process and environmental parameters result in variation of start and end points of timing windows, linear approximation is used for approximating effect of process and environmental variations. One of the biggest advantages of MILP formulation of aggressor alignment problem has also been discussed, which is the ability to be easily extended to more complex cases such as non-triangle noise pulses, victim sensitivity window and discontinuous timing windows, this work shows that such extension can be solved by algorithm and does not require development of new algorithms. Therefore, this novel technique can handle noise alignment problem both in deterministic and variational cases and can be easily extended for more complex cases --Abstract, page iii

Fully Automatic Video Colorization with Self-Regularization and Diversity

We present a fully automatic approach to video colorization with self-regularization and diversity. Our model contains a colorization network for video frame colorization and a refinement network for spatiotemporal color refinement. Without any labeled data, both networks can be trained with self-regularized losses defined in bilateral and temporal space. The bilateral loss enforces color consistency between neighboring pixels in a bilateral space and the temporal loss imposes constraints between corresponding pixels in two nearby frames. While video colorization is a multi-modal problem, our method uses a perceptual loss with diversity to differentiate various modes in the solution space. Perceptual experiments demonstrate that our approach outperforms state-of-the-art approaches on fully automatic video colorization. The results are shown in the supplementary video at https://youtu.be/Y15uv2jnK-4Comment: Published at the Computer Vision and Pattern Recognition (CVPR), 201