15,597 research outputs found
Acceleration of stereo-matching on multi-core CPU and GPU
This paper presents an accelerated version of a
dense stereo-correspondence algorithm for two different parallelism
enabled architectures, multi-core CPU and GPU. The
algorithm is part of the vision system developed for a binocular
robot-head in the context of the CloPeMa 1 research project.
This research project focuses on the conception of a new clothes
folding robot with real-time and high resolution requirements
for the vision system. The performance analysis shows that
the parallelised stereo-matching algorithm has been significantly
accelerated, maintaining 12x and 176x speed-up respectively
for multi-core CPU and GPU, compared with non-SIMD singlethread
CPU. To analyse the origin of the speed-up and gain
deeper understanding about the choice of the optimal hardware,
the algorithm was broken into key sub-tasks and the performance
was tested for four different hardware architectures
Guided Filtering based Pyramidal Stereo Matching for Unrectified Images
Stereo matching deals with recovering quantitative
depth information from a set of input images, based on the visual
disparity between corresponding points. Generally most of the
algorithms assume that the processed images are rectified. As
robotics becomes popular, conducting stereo matching in the
context of cloth manipulation, such as obtaining the disparity
map of the garments from the two cameras of the cloth folding
robot, is useful and challenging. This is resulted from the fact of
the high efficiency, accuracy and low memory requirement under
the usage of high resolution images in order to capture the details
(e.g. cloth wrinkles) for the given application (e.g. cloth folding).
Meanwhile, the images can be unrectified. Therefore, we propose
to adapt guided filtering algorithm into the pyramidical stereo
matching framework that works directly for unrectified images.
To evaluate the proposed unrectified stereo matching in terms of
accuracy, we present three datasets that are suited to especially
the characteristics of the task of cloth manipulations. By com-
paring the proposed algorithm with two baseline algorithms on
those three datasets, we demonstrate that our proposed approach
is accurate, efficient and requires low memory. This also shows
that rather than relying on image rectification, directly applying
stereo matching through the unrectified images can be also quite
effective and meanwhile efficien
Near real-time stereo vision system
The apparatus for a near real-time stereo vision system for use with a robotic vehicle is described. The system is comprised of two cameras mounted on three-axis rotation platforms, image-processing boards, a CPU, and specialized stereo vision algorithms. Bandpass-filtered image pyramids are computed, stereo matching is performed by least-squares correlation, and confidence ranges are estimated by means of Bayes' theorem. In particular, Laplacian image pyramids are built and disparity maps are produced from the 60 x 64 level of the pyramids at rates of up to 2 seconds per image pair. The first autonomous cross-country robotic traverses (of up to 100 meters) have been achieved using the stereo vision system of the present invention with all computing done onboard the vehicle. The overall approach disclosed herein provides a unifying paradigm for practical domain-independent stereo ranging
Parallel stereo vision algorithm
Integrating a stereo-photogrammetric robot
head into a real-time system requires software
solutions that rapidly resolve the stereo correspondence
problem. The stereo-matcher presented in this
paper uses therefore code parallelisation and was
tested on three different processors with x87 and AVX.
The results show that a 5mega pixels colour image can
be matched in 5,55 seconds or as monochrome in 3,3
seconds
Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture
Deep neural networks are applied to a wide range of problems in recent years.
In this work, Convolutional Neural Network (CNN) is applied to the problem of
determining the depth from a single camera image (monocular depth). Eight
different networks are designed to perform depth estimation, each of them
suitable for a feature level. Networks with different pooling sizes determine
different feature levels. After designing a set of networks, these models may
be combined into a single network topology using graph optimization techniques.
This "Semi Parallel Deep Neural Network (SPDNN)" eliminates duplicated common
network layers, and can be further optimized by retraining to achieve an
improved model compared to the individual topologies. In this study, four SPDNN
models are trained and have been evaluated at 2 stages on the KITTI dataset.
The ground truth images in the first part of the experiment are provided by the
benchmark, and for the second part, the ground truth images are the depth map
results from applying a state-of-the-art stereo matching method. The results of
this evaluation demonstrate that using post-processing techniques to refine the
target of the network increases the accuracy of depth estimation on individual
mono images. The second evaluation shows that using segmentation data alongside
the original data as the input can improve the depth estimation results to a
point where performance is comparable with stereo depth estimation. The
computational time is also discussed in this study.Comment: 44 pages, 25 figure
Calibration and Sensitivity Analysis of a Stereo Vision-Based Driver Assistance System
Az http://intechweb.org/ alatti "Books" fĂŒl alatt kell rĂĄkeresni a "Stereo Vision" cĂmre Ă©s az 1. fejezetre
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