Hand–eye calibration with epipolar constraints: Application to endoscopy

2013 ◽  
Vol 61 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Abed Malti
Keyword(s):  
Epipolar Constraints

Conic epipolar constraints from affine correspondences

2014 ◽  
Vol 122 ◽  
pp. 105-114 ◽  
Author(s):  
Jacob Bentolila ◽  
Joseph M. Francos
Keyword(s):  
Epipolar Constraints

Detecting and tracking moving objects from a moving platform using epipolar constraints

2010 ◽  
Author(s):  
Jonah C. McBride ◽  
Andrey Ostapchenko ◽  
Howard Schultz ◽  
Magnus S. Snorrason
Keyword(s):  
Moving Objects ◽  
Moving Platform ◽  
Epipolar Constraints

3-D Sensing for Flexible Linear Object Alignment in Robot Cell Production System

2010 ◽  
Vol 22 (1) ◽  
pp. 100-111 ◽  
Author(s):  
Yukiyasu Domae ◽  
◽  
Haruhisa Okuda ◽  
Yasuo Kitaaki ◽  
Yuta Kimura ◽  
...  
Keyword(s):  
Industrial Robot ◽  
Precision Measurements ◽  
Monocular Camera ◽  
Measurement Results ◽  
Automatic Alignment ◽  
Linear Object ◽  
Epipolar Constraints ◽  
Encoding Method ◽  
Technical Features ◽  
Space Encoding

We have constructed 3-D sensing system for alignment of connector-fitted cables as flexible linear objects which used to be difficult to be automated at the production sites. In the system an industrial robot has a 3-D sensor and a monocular camera mounted at the hand. 3-D sensor, using space encoding method, allows the robot to make high-precision measurements of the order of sub-millimeters, but emphasis is placed on precision at the expense of fields of view. In addition, active sensing methods such as the space encoding method is hard to take measurements for black cables, as well as it has some difficulties with measurements of semitransparent plastic connectors depending on view-points. To cope with those problems, our system is such that the monocular camera on the robot is moved for motion stereo to take measurements on cable shapes; connector’s poses are coarsely estimated from the measurement results; and such view-points as will ensure stable measurements are computed by space encoding method to take precision measurements of connectors. Technical features of the system could be summarized as follows: 1) Determination of view-points to measure connectors, based on measurements of cable shapes, requires no more than two measurements, without repeated searches, to grab semitransparent plastic connectors. 2) Performance of stereo correspondence for plain or black cables, which tends to result in a failure with the aids of no more than irradiated slit patterns and epipolar constraints, has been improved through sequential correspondence inmotion image sequence and its stability evaluations. At the operation tests in the validation system, the robot is assigned a task to assemble the cables into industrial servo amplifiers available on the market, in which automatic alignment of 200 connector-fitted cables has successfully been accomplished in succession to confirm constant performance of the system.

scholarly journals DISPARITY REFINEMENT OF BUILDING EDGES USING ROBUSTLY MATCHED STRAIGHT LINES FOR STEREO MATCHING

2018 ◽  
Vol IV-1 ◽  
pp. 77-84
Author(s):  
X. Huang ◽  
R. Qin ◽  
M. Chen
Keyword(s):  
Least Squares ◽  
Stereo Matching ◽  
Low Cost ◽  
Point Clouds ◽  
3D Point Clouds ◽  
Dense Matching ◽  
Non Local ◽  
Epipolar Constraints ◽  
High Flexibility ◽  
Straight Lines

<p><strong>Abstract.</strong> Stereo dense matching has already been one of the dominant tools in 3D reconstruction of urban regions, due to its low cost and high flexibility in generating 3D points. However, the image-derived 3D points are often inaccurate around building edges, which limit its use in several vision tasks (e.g. building modelling). To generate 3D point clouds or digital surface models (DSM) with sharp boundaries, this paper integrates robustly matched lines for improving dense matching, and proposes a non-local disparity refinement of building edges through an iterative least squares plane adjustment approach. In our method, we first extract and match straight lines in images using epipolar constraints, then detect building edges from these straight lines by comparing matching results on both sides of straight lines, and finally we develop a non-local disparity refinement method through an iterative least squares plane adjustment constrained by matched straight lines to yield sharper and more accurate edges. Experiments conducted on both satellite and aerial data demonstrate that our proposed method is able to generate more accurate DSM with sharper object boundaries.</p>

scholarly journals DENSE IMAGE MATCHING WITH TWO STEPS OF EXPANSION

2016 ◽  
Vol XLI-B3 ◽  
pp. 143-149
Author(s):  
Zuxun Zhang ◽  
Jia’nan He ◽  
Shan Huang ◽  
Yansong Duan
Keyword(s):  
Image Matching ◽  
Geometric Constraints ◽  
Second Step ◽  
Feature Points ◽  
Sar Images ◽  
Seed Point ◽  
Line Array ◽  
Dense Image ◽  
Epipolar Constraints ◽  
Seed Points

Dense image matching is a basic and key point of photogrammetry and computer version. In this paper, we provide a method derived from the seed-and-grow method, whose basic procedure consists of the following: First, the seed and feature points are extracted, after which the feature points around every seed point are found in the first step of expansion. The corresponding information on these feature points needs to be determined. This is followed by the second step of expansion, in which the seed points around the feature point are found and used to estimate the possible matching patch. Finally, the matching results are refined through the traditional correlation-based method. Our proposed method operates on two frames without geometric constraints, specifically, epipolar constraints. It (1) can smoothly operate on frame, line array, natural scene, and even synthetic aperture radar (SAR) images and (2) at the same time guarantees computing efficiency as a result of the seed-and-grow concept and the computational efficiency of the correlation-based method.

scholarly journals FEATURE MATCHING ENHANCEMENT OF UAV IMAGES USING GEOMETRIC CONSTRAINTS

2018 ◽  
Vol XLII-1 ◽  
pp. 307-314
Author(s):  
H. M. Mohammed ◽  
N. El-Sheimy
Keyword(s):  
Fundamental Matrix ◽  
Template Matching ◽  
Feature Matching ◽  
Image Features ◽  
Geometric Constraints ◽  
Least Square ◽  
Scene Structure ◽  
Feature Correspondences ◽  
Epipolar Constraints ◽  
Uav Images

<p><strong>Abstract.</strong> Preliminary matching of image features is based on the distance between their descriptors. Matches are further filtered using RANSAC, or a similar method that fits the matches to a model; usually the fundamental matrix and rejects matches not belonging to that model. There are a few issues with this scheme. First, mismatches are no longer considered after RANSAC rejection. Second, RANSAC might fail to detect an accurate model if the number of outliers is significant. Third, a fundamental matrix model could be degenerate even if the matches are all inliers. To address these issues, a new method is proposed that relies on the prior knowledge of the images’ geometry, which can be obtained from the orientation sensors or a set of initial matches. Using a set of initial matches, a fundamental matrix and a global homography can be estimated. These two entities are then used with a detect-and-match strategy to gain more accurate matches. Features are detected in one image, then the locations of their correspondences in the other image are predicted using the epipolar constraints and the global homography. The feature correspondences are then corrected with template matching. Since global homography is only valid with a plane-to-plane mapping, discrepancy vectors are introduced to represent an alternative to local homographies. The method was tested on Unmanned Aerial Vehicle (UAV) images, where the images are usually taken successively, and differences in scale and orientation are not an issue. The method promises to find a well-distributed set of matches over the scene structure, especially with scenes of multiple depths. Furthermore; the number of outliers is reduced, encouraging to use a least square adjustment instead of RANSAC, to fit a non-degenerate model.</p>

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