2,827 research outputs found
Local Stereo Matching Using Adaptive Local Segmentation
We propose a new dense local stereo matching framework for gray-level images based on an adaptive local segmentation using a dynamic threshold. We define a new validity domain of the fronto-parallel assumption based on the local intensity variations in the 4-neighborhood of the matching pixel. The preprocessing step smoothes low textured areas and sharpens texture edges, whereas the postprocessing step detects and recovers occluded and unreliable disparities. The algorithm achieves high stereo reconstruction quality in regions with uniform intensities as well as in textured regions. The algorithm is robust against local radiometrical differences; and successfully recovers disparities around the objects edges, disparities of thin objects, and the disparities of the occluded region. Moreover, our algorithm intrinsically prevents errors caused by occlusion to propagate into nonoccluded regions. It has only a small number of parameters. The performance of our algorithm is evaluated on the Middlebury test bed stereo images. It ranks highly on the evaluation list outperforming many local and global stereo algorithms using color images. Among the local algorithms relying on the fronto-parallel assumption, our algorithm is the best ranked algorithm. We also demonstrate that our algorithm is working well on practical examples as for disparity estimation of a tomato seedling and a 3D reconstruction of a face
Cross-Scale Cost Aggregation for Stereo Matching
Human beings process stereoscopic correspondence across multiple scales.
However, this bio-inspiration is ignored by state-of-the-art cost aggregation
methods for dense stereo correspondence. In this paper, a generic cross-scale
cost aggregation framework is proposed to allow multi-scale interaction in cost
aggregation. We firstly reformulate cost aggregation from a unified
optimization perspective and show that different cost aggregation methods
essentially differ in the choices of similarity kernels. Then, an inter-scale
regularizer is introduced into optimization and solving this new optimization
problem leads to the proposed framework. Since the regularization term is
independent of the similarity kernel, various cost aggregation methods can be
integrated into the proposed general framework. We show that the cross-scale
framework is important as it effectively and efficiently expands
state-of-the-art cost aggregation methods and leads to significant
improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets.Comment: To Appear in 2013 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). 2014 (poster, 29.88%
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
Entropy-difference based stereo error detection
Stereo depth estimation is error-prone; hence, effective error detection
methods are desirable. Most such existing methods depend on characteristics of
the stereo matching cost curve, making them unduly dependent on functional
details of the matching algorithm. As a remedy, we propose a novel error
detection approach based solely on the input image and its depth map. Our
assumption is that, entropy of any point on an image will be significantly
higher than the entropy of its corresponding point on the image's depth map. In
this paper, we propose a confidence measure, Entropy-Difference (ED) for stereo
depth estimates and a binary classification method to identify incorrect
depths. Experiments on the Middlebury dataset show the effectiveness of our
method. Our proposed stereo confidence measure outperforms 17 existing measures
in all aspects except occlusion detection. Established metrics such as
precision, accuracy, recall, and area-under-curve are used to demonstrate the
effectiveness of our method
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