2,397 research outputs found
Variational Disparity Estimation Framework for Plenoptic Image
This paper presents a computational framework for accurately estimating the
disparity map of plenoptic images. The proposed framework is based on the
variational principle and provides intrinsic sub-pixel precision. The
light-field motion tensor introduced in the framework allows us to combine
advanced robust data terms as well as provides explicit treatments for
different color channels. A warping strategy is embedded in our framework for
tackling the large displacement problem. We also show that by applying a simple
regularization term and a guided median filtering, the accuracy of displacement
field at occluded area could be greatly enhanced. We demonstrate the excellent
performance of the proposed framework by intensive comparisons with the Lytro
software and contemporary approaches on both synthetic and real-world datasets
Deep Video Color Propagation
Traditional approaches for color propagation in videos rely on some form of
matching between consecutive video frames. Using appearance descriptors, colors
are then propagated both spatially and temporally. These methods, however, are
computationally expensive and do not take advantage of semantic information of
the scene. In this work we propose a deep learning framework for color
propagation that combines a local strategy, to propagate colors frame-by-frame
ensuring temporal stability, and a global strategy, using semantics for color
propagation within a longer range. Our evaluation shows the superiority of our
strategy over existing video and image color propagation methods as well as
neural photo-realistic style transfer approaches.Comment: BMVC 201
Enabling Cross-Event Optimization in Discrete-Event Simulation Through Compile-Time Event Batching
A discrete-event simulation (DES) involves the execution of a sequence of
event handlers dynamically scheduled at runtime. As a consequence, a priori
knowledge of the control flow of the overall simulation program is limited. In
particular, powerful optimizations supported by modern compilers can only be
applied on the scope of individual event handlers, which frequently involve
only a few lines of code. We propose a method that extends the scope for
compiler optimizations in discrete-event simulations by generating batches of
multiple events that are subjected to compiler optimizations as contiguous
procedures. A runtime mechanism executes suitable batches at negligible
overhead. Our method does not require any compiler extensions and introduces
only minor additional effort during model development. The feasibility and
potential performance gains of the approach are illustrated on the example of
an idealized proof-ofconcept model. We believe that the applicability of the
approach extends to general event-driven programs
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