1,085 research outputs found
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
Low-level Vision by Consensus in a Spatial Hierarchy of Regions
We introduce a multi-scale framework for low-level vision, where the goal is
estimating physical scene values from image data---such as depth from stereo
image pairs. The framework uses a dense, overlapping set of image regions at
multiple scales and a "local model," such as a slanted-plane model for stereo
disparity, that is expected to be valid piecewise across the visual field.
Estimation is cast as optimization over a dichotomous mixture of variables,
simultaneously determining which regions are inliers with respect to the local
model (binary variables) and the correct co-ordinates in the local model space
for each inlying region (continuous variables). When the regions are organized
into a multi-scale hierarchy, optimization can occur in an efficient and
parallel architecture, where distributed computational units iteratively
perform calculations and share information through sparse connections between
parents and children. The framework performs well on a standard benchmark for
binocular stereo, and it produces a distributional scene representation that is
appropriate for combining with higher-level reasoning and other low-level cues.Comment: Accepted to CVPR 2015. Project page:
http://www.ttic.edu/chakrabarti/consensus
A Hardware-Oriented Dynamically Adaptive Disparity Estimation Algorithm and its Real-Time Hardware
The computational complexity of disparity estimation algorithms and the need of large size and bandwidth for the external and internal memory make the real-time processing of disparity estimation challenging, especially for High Resolution (HR) images. This paper proposes a hardware-oriented adaptive window size disparity estimation (AWDE) algorithm and its real time reconfigurable hardware implementation that targets HR video with high quality disparity results. The proposed algorithm is a hybrid solution involving the Sum of Absolute Differences and the Census cost computation methods to vote and select the best suitable disparity candidates. It utilizes a pixel intensity based refinement step to remove faulty disparity computations. The AWDE algorithm dynamically adapts the window size considering the local texture of the image to increase the disparity estimation quality. The proposed reconfigurable hardware of the AWDE algorithm enables handling 60 frames per second on Virtex-5 FPGA at a 1024Ă768 XGA video resolution for a 120 pixel disparity range
Dynamically adaptive real-time disparity estimation hardware using iterative refinement
The computational complexity of disparity estimation algorithms and the need of large size and bandwidth for the external and internal memory make the real-time processing of disparity estimation challenging, especially for High Resolution (HR) images. This paper proposes a hardware-oriented adaptive window size disparity estimation (AWDE) algorithm and its real-time reconfigurable hardware implementation that targets HR video with high quality disparity results. Moreover, an enhanced version of the AWDE implementation that uses iterative refinement (AWDE-IR) is presented. The AWDE and AWDE-IR algorithms dynamically adapt the window size considering the local texture of the image to increase the disparity estimation quality. The proposed reconfigurable hardware architectures of the AWDE and AWDE-IR algorithms enable handling 60 frames per second on a Virtex-5 FPGA at a 1024Ă768 XGA video resolution for a 128 pixel disparity range
Selected Papers from the First International Symposium on Future ICT (Future-ICT 2019) in Conjunction with 4th International Symposium on Mobile Internet Security (MobiSec 2019)
The International Symposium on Future ICT (Future-ICT 2019) in conjunction with the 4th International Symposium on Mobile Internet Security (MobiSec 2019) was held on 17â19 October 2019 in Taichung, Taiwan. The symposium provided academic and industry professionals an opportunity to discuss the latest issues and progress in advancing smart applications based on future ICT and its relative security. The symposium aimed to publish high-quality papers strictly related to the various theories and practical applications concerning advanced smart applications, future ICT, and related communications and networks. It was expected that the symposium and its publications would be a trigger for further related research and technology improvements in this field
Compressed look-up-table based real-time rectification hardware
Stereo image rectification is a pre-processing step of disparity estimation intended to remove image distortions and to enable stereo matching along an epipolar line. A real-time disparity estimation system needs to perform real-time rectification which requires solving the models of lens distortions, image translations and rotations. Look-up-table based rectification algorithms allow image rectification without demanding high complexity operations. However, they require an external memory to store large size look-up-tables. In this work, we present an intermediate solution that compresses the rectification information to fit the look-up-table into the onchip memory of a Virtex-5 FPGA. The low-complexity decompression process requires a negligible amount of hardware resources for its real-time implementation. The proposed image rectification hardware consumes 0.28% of the DFF and 0.32% of the LUT resources of the Virtex-5 XCUVP-110T FPGA, it can process 347 frames per second for a 1024Ă768 pixels image resolution, and it does not need the availability of an external memory
GPGPU Implementation of a Genetic Algorithm for Stereo Refinement
During the last decade, the general-purpose computing on graphics processing units Graphics (GPGPU) has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques. This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement. The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state- of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance
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