• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 212
Next 10 →

Cross-based local stereo matching using orthogonal integral images

by Ke Zhang, Jiangbo Lu, Gauthier Lafruit - IEEE Trans. Circuits Syst. Video Technol , 2009
"... Abstract — We propose an area-based local stereo matching algorithm for accurate disparity estimation across all image regions. A well-known challenge to local stereo methods is to decide an appropriate support window for the pixel under consideration, adapting the window shape or the pixelwise supp ..."
Abstract - Cited by 18 (2 self) - Add to MetaCart
Abstract — We propose an area-based local stereo matching algorithm for accurate disparity estimation across all image regions. A well-known challenge to local stereo methods is to decide an appropriate support window for the pixel under consideration, adapting the window shape or the pixelwise

PatchMatch Stereo- Stereo Matching with Slanted Support Windows

by Michael Bleyer, Christoph Rhemann, Carsten Rother
"... Common local stereo methods match support windows at integer-valued disparities. The implicit assumption that pixels within the support region have constant disparity does not hold for slanted surfaces and leads to a bias towards reconstructing frontoparallel surfaces. This work overcomes this bias ..."
Abstract - Cited by 41 (5 self) - Add to MetaCart
Common local stereo methods match support windows at integer-valued disparities. The implicit assumption that pixels within the support region have constant disparity does not hold for slanted surfaces and leads to a bias towards reconstructing frontoparallel surfaces. This work overcomes this bias

Local Stereo Matching Using Geodesic Support Weights

by Asmaa Hosni, Michael Bleyer, Margrit Gelautz, Christoph Rhemann - Proc. Int’l Conf. Image Processing , 2009
"... Local stereo matching has recently experienced large progress by the introduction of new support aggregation schemes. These approaches estimate a pixel’s support region via color segmentation. Our contribution lies in an improved method for accomplishing this segmentation. Inside a square support wi ..."
Abstract - Cited by 29 (4 self) - Add to MetaCart
Local stereo matching has recently experienced large progress by the introduction of new support aggregation schemes. These approaches estimate a pixel’s support region via color segmentation. Our contribution lies in an improved method for accomplishing this segmentation. Inside a square support

A LOCAL ADAPTIVE APPROACH FOR DENSE STEREO MATCHING IN ARCHITECTURAL SCENE RECONSTRUCTION

by C. Stentoumis, L. Grammatikopoulos, I. Kalisperakis, E. Petsa, G. Karras
"... In recent years, a demand for 3D models of various scales and precisions has been growing for a wide range of applications; among them, cultural heritage recording is a particularly important and challenging field. We outline an automatic 3D reconstruction pipeline, mainly focusing on dense stereo-m ..."
Abstract - Add to MetaCart
cost computed on an extended census transformation of the images; the absolute difference cost, taking into account information from colour channels; and a cost based on the principal image derivatives. An efficient adaptive method of aggregating matching cost for each pixel is then applied, relying

A non-local cost aggregation method for stereo matching

by Qingxiong Yang , 2012
"... Matching cost aggregation is one of the oldest and still popular methods for stereo correspondence. While effec-tive and efficient, cost aggregation methods typically aggregate the matching cost by summing/averaging over a user-specified, local support region. This is obviously on-ly locally-optima ..."
Abstract - Cited by 19 (2 self) - Add to MetaCart
Matching cost aggregation is one of the oldest and still popular methods for stereo correspondence. While effec-tive and efficient, cost aggregation methods typically aggregate the matching cost by summing/averaging over a user-specified, local support region. This is obviously on-ly locally

1Stereo Matching Using Tree Filtering

by Qingxiong Yang
"... Abstract—Matching cost aggregation is one of the oldest and still pop-ular methods for stereo correspondence. While effective and efficient, cost aggregation methods typically aggregate the matching cost by summing/averaging over a user-specified, local support region. This is obviously only locally ..."
Abstract - Add to MetaCart
Abstract—Matching cost aggregation is one of the oldest and still pop-ular methods for stereo correspondence. While effective and efficient, cost aggregation methods typically aggregate the matching cost by summing/averaging over a user-specified, local support region. This is obviously only

IMPLEMENTING AN ADAPTIVE APPROACH FOR DENSE STEREO-MATCHING

by Christos Stentoumisa, Lazaros Grammatikopoulosb, Ilias Kalisperakisb, George Karrasa
"... Defining pixel correspondences in stereo-pairs is a fundamental process in automated image-based 3D reconstruction. In this con-tribution we report on an approach for dense matching, based on local optimization. The approach represents a fusion of state-of-the-art algorithms and novel considerations ..."
Abstract - Add to MetaCart
Defining pixel correspondences in stereo-pairs is a fundamental process in automated image-based 3D reconstruction. In this con-tribution we report on an approach for dense matching, based on local optimization. The approach represents a fusion of state-of-the-art algorithms and novel

Domain Transformation-Based Efficient Cost Aggregation for Local Stereo Matching

by Cuong Cao Pham, Jae Wook Jeon
"... Abstract—Binocular stereo matching is one of the most important algorithms in the field of computer vision. Adaptive support-weight approaches, the current state-of-the-art local methods, produce results comparable to those generated by global methods. However, excessive time consumption is the main ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
Abstract—Binocular stereo matching is one of the most important algorithms in the field of computer vision. Adaptive support-weight approaches, the current state-of-the-art local methods, produce results comparable to those generated by global methods. However, excessive time consumption

Efficient Hybrid Tree-Based Stereo Matching With Applications to Postcapture Image Refocusing

by Dung T. Vu, Benjamin Chidester, Hongsheng Yang, Minh N. Do, Jiangbo Lu
"... Abstract — Estimating dense correspondence or depth infor-mation from a pair of stereoscopic images is a fundamental problem in computer vision, which finds a range of important applications. Despite intensive past research efforts in this topic, it still remains challenging to recover the depth inf ..."
Abstract - Add to MetaCart
information both reliably and efficiently, especially when the input images contain weakly textured regions or are captured under uncontrolled, real-life conditions. Striking a desired balance between computational efficiency and estimation quality, a hybrid minimum spanning tree-based stereo matching method

Efficient Stereo with Accurate 3-D Boundaries

by Mikhail Sizintsev, Richard P. Wildes
"... This paper presents methods for recovering accurate binocular disparity estimates in the vicinity of 3D surface discontinuities. Of particular concern are methods that impact coarse-to-fine, block matching as it forms the basis of the fastest and resource efficient disparity estimation procedures. T ..."
Abstract - Cited by 5 (1 self) - Add to MetaCart
. Two advances are put forth. First, a novel approach to coarse-to-fine processing is presented that adapts match window support across scale to ameliorate corruption of disparity estimates near 3D boundaries. Second, a novel formulation of half-occlusion cues within the coarse-to-fine, block matching
Next 10 →
Results 1 - 10 of 212
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University