THESIS
2014
xv, 146 pages : illustrations ; 30 cm
Abstract
Stereo matching is one of the most active research topics in computer vision.
The generated depth maps are widely used in many applications such as 3D reconstruction,
3DTV, object recognition, autonomous systems, etc. Stereo matching
generally performs four steps: 1. matching cost computation; 2. cost aggregation;
3. disparity computation; and 4. disparity refinement. In this thesis, we propose
some approaches to improve the performance of each step. Meanwhile, specially
designed matching algorithms are proposed for the applications to HDR imaging
and free view generation.
Firstly, we propose a novel matching cost computation algorithm named Optimal
Local Adaptive Radiometric Compensation (LARAC), which is robust to
the radiometric differences caused by illumination and exposu...[
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Stereo matching is one of the most active research topics in computer vision.
The generated depth maps are widely used in many applications such as 3D reconstruction,
3DTV, object recognition, autonomous systems, etc. Stereo matching
generally performs four steps: 1. matching cost computation; 2. cost aggregation;
3. disparity computation; and 4. disparity refinement. In this thesis, we propose
some approaches to improve the performance of each step. Meanwhile, specially
designed matching algorithms are proposed for the applications to HDR imaging
and free view generation.
Firstly, we propose a novel matching cost computation algorithm named Optimal
Local Adaptive Radiometric Compensation (LARAC), which is robust to
the radiometric differences caused by illumination and exposure variations. In
LARAC, we approximate the spatially varying Pixel Value Correspondence Function
(PVCF) as a locally consistent polynomial with an optimal window size,
where the polynomial coefficients are estimated optimally with a closed-form solution.
The simulation results suggest that the proposed LARAC outperforms
other state-of-the-art stereo algorithms.
Secondly, we improve the performance of the cost aggregation step in stereo
matching. While many existing cost aggregation algorithms trade off between
accuracy and computational complexity, we propose a novel algorithm named
Stereo Matching by Adaptive and Recursive Technologies (SMART) which has
low complexity while maintaining high accuracy. In SMART, a novel cost aggregation algorithm is proposed to increase the accuracy and a recursive cost aggregation
algorithm is invented to reduce the aggregation complexity from O(n
2) to
O(1).
Thirdly, a novel disparity refinement algorithm, named hybrid plane fitting,
with low complexity and high accuracy is proposed. We propose a novel robust
cross checking algorithm to exclude outliers in the disparity maps. According to
the outlier percentage of each plane, we propose a hybrid method, either RANSAC
based plane fitting, or the proposed weighted LSE based plane fitting, to estimate
the plane parameters and refine the disparity maps accordingly.
Fourthly, a multi-exposed stereo camera system for high dynamic range imaging
is proposed. The stereo setup captures two images simultaneously and allows
for HDR image composition for dynamic scenes and HDR videos. A specially
designed algorithm named Multi-exposed stereo matching (MEX) is proposed to
generate accurate disparity maps from two images with different exposure times.
Image warping is performed afterwards to generate two aligned images, with the
warping errors detected by the proposed warping error detection algorithm. A
HDR image is recovered by the two aligned images, with the unreliable pixels
interpolated by the proposed radiance consistent hole filling algorithm.
Finally, a novel ray-space based free view generation algorithm based on
Radon transform is proposed to generate virtual views in-between adjacent real
views to realize free viewpoint television (FTV) applications. In the proposed
method, the correspondence searching problem in the two-view configuration is
extended to a ray space correspondence search problem in the multi-view configuration.
The corresponding pixels in the neighboring real views for each pixel are
detected by the proposed robust block matching algorithm, in which the smoothness
property of the disparity field and the correlation among the neighboring
views are explored.
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