6 research outputs found
Fast MRF Optimization with Application to Depth Reconstruction
We describe a simple and fast algorithm for optimizing Markov random fields over images. The algorithm performs block coordinate descent by optimally updating a horizon-tal or vertical line in each step. While the algorithm is not as accurate as state-of-the-art MRF solvers on traditional benchmark problems, it is trivially parallelizable and pro-duces competitive results in a fraction of a second. As an application, we develop an approach to increasing the ac-curacy of consumer depth cameras. The presented algo-rithm enables high-resolution MRF optimization at multiple frames per second and substantially increases the accuracy of the produced range images. 1
A Dynamic Programming Solution to Bounded Dejittering Problems
We propose a dynamic programming solution to image dejittering problems with
bounded displacements and obtain efficient algorithms for the removal of line
jitter, line pixel jitter, and pixel jitter.Comment: The final publication is available at link.springer.co
RSGM: Real-time Raster-Respecting Semi-Global Matching for Power-Constrained Systems
Stereo depth estimation is used for many computer vision applications. Though
many popular methods strive solely for depth quality, for real-time mobile
applications (e.g. prosthetic glasses or micro-UAVs), speed and power
efficiency are equally, if not more, important. Many real-world systems rely on
Semi-Global Matching (SGM) to achieve a good accuracy vs. speed balance, but
power efficiency is hard to achieve with conventional hardware, making the use
of embedded devices such as FPGAs attractive for low-power applications.
However, the full SGM algorithm is ill-suited to deployment on FPGAs, and so
most FPGA variants of it are partial, at the expense of accuracy. In a non-FPGA
context, the accuracy of SGM has been improved by More Global Matching (MGM),
which also helps tackle the streaking artifacts that afflict SGM. In this
paper, we propose a novel, resource-efficient method that is inspired by MGM's
techniques for improving depth quality, but which can be implemented to run in
real time on a low-power FPGA. Through evaluation on multiple datasets (KITTI
and Middlebury), we show that in comparison to other real-time capable stereo
approaches, we can achieve a state-of-the-art balance between accuracy, power
efficiency and speed, making our approach highly desirable for use in real-time
systems with limited power.Comment: Accepted in FPT 2018 as Oral presentation, 8 pages, 6 figures, 4
table
Discrete graphical models -- an optimization perspective
This monograph is about discrete energy minimization for discrete graphical
models. It considers graphical models, or, more precisely, maximum a posteriori
inference for graphical models, purely as a combinatorial optimization problem.
Modeling, applications, probabilistic interpretations and many other aspects
are either ignored here or find their place in examples and remarks only. It
covers the integer linear programming formulation of the problem as well as its
linear programming, Lagrange and Lagrange decomposition-based relaxations. In
particular, it provides a detailed analysis of the polynomially solvable
acyclic and submodular problems, along with the corresponding exact
optimization methods. Major approximate methods, such as message passing and
graph cut techniques are also described and analyzed comprehensively. The
monograph can be useful for undergraduate and graduate students studying
optimization or graphical models, as well as for experts in optimization who
want to have a look into graphical models. To make the monograph suitable for
both categories of readers we explicitly separate the mathematical optimization
background chapters from those specific to graphical models.Comment: 270 page