1,510 research outputs found
Color Homography Color Correction
Homographies -- a mathematical formalism for relating image points across different camera viewpoints -- are at the foundations of geometric methods in computer vision and are used in geometric camera calibration, image registration, and stereo vision and other tasks. In this paper, we show the surprising result that colors across a change in viewing condition (changing light color, shading and camera) are also related by a homography. We propose a new color correction method based on color homography. Experiments demonstrate that solving the color homography problem leads to more accurate calibration
Color homography
We show the surprising result that colors across a change in viewing
condition (changing light color, shading and camera) are related by a
homography. Our homography color correction application delivers improved color
fidelity compared with the linear least-square.Comment: Accepted by Progress in Colour Studies 201
Root-Polynomial Color Homography Color Correction
Homographies are at the heart of computer vision and they are used in geometric camera calibration, image registration, and stereo vision and other tasks. In geometric computer vision, two images of the same 3D plane captured in two different viewing locations are related by a planar (2D) homography. Recent work showed that the concept of a planar homography mapping can be applied to shading-invariant color correction. In this paper, we extend the color homography color correction idea by incorporating higher order root-polynomial terms into the color correction problem formulation. Our experiments show that our new shading-invariant color correction method can obtain yet more accurate and stable performance compared with the previous 2D color homography method
GPU Accelerated Color Correction and Frame Warping for Real-time Video Stitching
Traditional image stitching focuses on a single panorama frame without
considering the spatial-temporal consistency in videos. The straightforward
image stitching approach will cause temporal flicking and color inconstancy
when it is applied to the video stitching task. Besides, inaccurate camera
parameters will cause artifacts in the image warping. In this paper, we propose
a real-time system to stitch multiple video sequences into a panoramic video,
which is based on GPU accelerated color correction and frame warping without
accurate camera parameters. We extend the traditional 2D-Matrix (2D-M) color
correction approach and a present spatio-temporal 3D-Matrix (3D-M) color
correction method for the overlap local regions with online color balancing
using a piecewise function on global frames. Furthermore, we use pairwise
homography matrices given by coarse camera calibration for global warping
followed by accurate local warping based on the optical flow. Experimental
results show that our system can generate highquality panorama videos in real
time
Recovering Homography from Camera Captured Documents using Convolutional Neural Networks
Removing perspective distortion from hand held camera captured document
images is one of the primitive tasks in document analysis, but unfortunately,
no such method exists that can reliably remove the perspective distortion from
document images automatically. In this paper, we propose a convolutional neural
network based method for recovering homography from hand-held camera captured
documents.
Our proposed method works independent of document's underlying content and is
trained end-to-end in a fully automatic way. Specifically, this paper makes
following three contributions: Firstly, we introduce a large scale synthetic
dataset for recovering homography from documents images captured under
different geometric and photometric transformations; secondly, we show that a
generic convolutional neural network based architecture can be successfully
used for regressing the corners positions of documents captured under wild
settings; thirdly, we show that L1 loss can be reliably used for corners
regression. Our proposed method gives state-of-the-art performance on the
tested datasets, and has potential to become an integral part of document
analysis pipeline.Comment: 10 pages, 8 figure
Piecewise planar underwater mosaicing
A commonly ignored problem in planar mosaics, yet often present in practice, is the selection of a reference homography reprojection frame where to attach the successive image frames of the mosaic. A bad choice for the reference frame can lead to severe distortions in the mosaic and can degenerate in incorrect configurations after some sequential frame concatenations. This problem is accentuated in uncontrolled underwater acquisition setups as those provided by AUVs or ROVs due to both the noisy trajectory of the acquisition vehicle - with roll and pitch shakes - and to the non-flat nature of the seabed which tends to break the planarity assumption implicit in the mosaic construction. These scenarios can also introduce other undesired effects, such as light variations between successive frames, scattering and attenuation, vignetting, flickering and noise. This paper proposes a novel mosaicing pipeline, also including a strategy to select the best reference homography in planar mosaics from video sequences which minimizes the distortions induced on each image by the mosaic homography itself. Moreover, a new non-linear color correction scheme is incorporated to handle strong color and luminosity variations among the mosaic frames. Experimental evaluation of the proposed method on real, challenging underwater video sequences shows the validity of the approach, providing clear and visually appealing mosaic
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