1,111 research outputs found
Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy
In this paper we present a simple and robust method for self-correction of
camera distortion using single images of scenes which contain straight lines.
Since the most common distortion can be modelled as radial distortion, we
illustrate the method using the Harris radial distortion model, but the method
is applicable to any distortion model. The method is based on transforming the
edgels of the distorted image to a 1-D angular Hough space, and optimizing the
distortion correction parameters which minimize the entropy of the
corresponding normalized histogram. Properly corrected imagery will have fewer
curved lines, and therefore less spread in Hough space. Since the method does
not rely on any image structure beyond the existence of edgels sharing some
common orientations and does not use edge fitting, it is applicable to a wide
variety of image types. For instance, it can be applied equally well to images
of texture with weak but dominant orientations, or images with strong vanishing
points. Finally, the method is performed on both synthetic and real data
revealing that it is particularly robust to noise.Comment: 9 pages, 5 figures Corrected errors in equation 1
Going Further with Point Pair Features
Point Pair Features is a widely used method to detect 3D objects in point
clouds, however they are prone to fail in presence of sensor noise and
background clutter. We introduce novel sampling and voting schemes that
significantly reduces the influence of clutter and sensor noise. Our
experiments show that with our improvements, PPFs become competitive against
state-of-the-art methods as it outperforms them on several objects from
challenging benchmarks, at a low computational cost.Comment: Corrected post-print of manuscript accepted to the European
Conference on Computer Vision (ECCV) 2016;
https://link.springer.com/chapter/10.1007/978-3-319-46487-9_5
Maximum likelihood estimation of circle parameters via convolution
Copyright © 2006 IEEEThe accurate fitting of a circle to noisy measurements of circumferential points is a much studied problem in the literature. In this paper, we present an interpretation of the maximum-likelihood estimator (MLE) and the Delogne–Kåsa estimator (DKE) for circle-center and radius estimation in terms of convolution on an image which is ideal in a certain sense. We use our convolution-based MLE approach to find good estimates for the parameters of a circle in digital images. In digital images, it is then possible to treat these estimates as preliminary estimates into various other numerical techniques which further refine them to achieve subpixel accuracy. We also investigate the relationship between the convolution of an ideal image with a “phase-coded kernel” (PCK) and the MLE. This is related to the “phase-coded annulus” which was introduced by Atherton and Kerbyson who proposed it as one of a number of new convolution kernels for estimating circle center and radius. We show that the PCK is an approximate MLE (AMLE). We compare our AMLE method to the MLE and the DKE as well as the Cramér–Rao Lower Bound in ideal images and in both real and synthetic digital images.Emanuel E. Zelniker, Student Member, IEEE, and I. Vaughan L. Clarkso
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