A review of image-warping methods

Glasbey, C. A.; Mardia, Kanti V.

doi:10.1080/02664769823151

Cited by 247 publications

(161 citation statements)

References 61 publications

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“…Those were regained by applying UV and normal maps which enable mapping textures from a separate image file onto the decimated model. UV mapping describes the manner in which 3D objects are "unfolded" onto a 2D plane, thus allowing to track each point (x,y) from the two dimensional plane and represent it on the 3D object (Alliez et al 2008;Glasbey and Mardia 1998;Heckbert 1986). A normal map contains the surface normals, which are found in the tangent space and are in accordance to the data stored in the UV map layout.…”

Section: D Modelling and Gaming Technologymentioning

confidence: 99%

Across Space and Time

Fleury¹,

Madeleine²,

Lefèvre

2015

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Section: D Modelling and Gaming Technologymentioning

confidence: 99%

Across Space and Time

Fleury¹,

Madeleine²,

Lefèvre

2015

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“…A variety of complex factors influence the geometric distortion during the imagery being captured and corrected. The external orientation errors that change for each sensor come from the available ephemeris (usually sensor position, velocity and attitude at fixed intervals) used to generate the approximate parameters of the rigorous sensor model (Okamoto, 1988, Glasbey, et al, 1998, Toutin, 2003, Poli, 2004. The internal orientation errors are due to principal point displacement, focal length variation, radial symmetric and decentering lens distortion, scale variation in CCD line direction and the CCD line rotation in the focal plane (Poli, 2004，Li & Wu, 2013.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Geometric Quality Evaluation of Remote Sensing Image Based on Information Entropy

Jiao

Long

Yang

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Geometric accuracy of the remote sensing rectified image is usually evaluated by the root-mean-square errors (RMSEs) of the ground control points (GCPs) and check points (CPs). These discrete geometric accuracy index data represent only on a local quality of the image with statistical methods. In addition, the traditional methods only evaluate the difference between the rectified image and reference image, ignoring the degree of the original image distortion. A new method of geometric quality evaluation of remote sensing image based on the information entropy is proposed in this paper. The information entropy, the amount of information and the uncertainty interval of the image before and after rectification are deduced according to the information theory. Four kind of rectification model and seven situations of GCP distribution are applied on the remotely sensed imagery in the experiments. The effective factors of the geometrical accuracy are analysed and the geometric qualities of the image are evaluated in various situations. Results show that the proposed method can be used to evaluate the rectification model, the distribution model of GCPs and the uncertainty of the remotely sensed imagery, and is an effective and objective assessment method.

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“…While this technique significantly reduced the size of VVCVs, the accuracy of the new representations was significantly compromised. Image warping [10,11], which used warping parameters as reduced representation variables, also demonstrated some efficacy but it was challenging to determine appropriate transformations for the motor curve and map. In an earlier, related ATC study [12], it was observed that implementing radial-basis function (RBF) artificial neural networks (ANN) [13,14] with input variables serving as reduced representation variables was a promising approach.…”

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confidence: 99%

Reduced representations of vector-valued coupling variables in decomposition-based design optimization

Alexander

Allison

Papalambros

2011

Struct Multidisc Optim

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Decomposition-based methods for system design optimization introduce consistency constraints, which contain coupling variables communicated between adjacent subproblems and link them together. When these variables are vector-valued (e.g., dynamic responses), the problem size can increase dramatically and make such methods impractical. Therefore, it is necessary to represent these vector-valued coupling variables with a reduced representation that will enable efficient optimization while maintaining an acceptable level of accuracy with respect to the original representation. This study investigates two representation techniques, radial-basis function artificial neural networks and proper orthogonal decomposition, and implements each in an analytical target cascading problem formulation for electric vehicle powertrain system optimization. Implementation of each representation technique is demonstrated and the techniques are assessed in terms of efficiency (decision vector dimensionality) and accuracy. Keywords:Decomposition-based design optimization, analytical target cascading, coupling variables, reduced representation, vector-valued target. IntroductionIn formulating design optimization problems for large-scale, complex systems, it is often practical to separate these systems into simpler, more manageable subsystem configurations. Decomposition-based optimization strategies are used frequently to solve these problems. These strategies introduce consistency constraints [1], which contain coupling variables and ensure feasible design solutions. Coupling variables appear in subproblem optimization formulations as decision variables, increasing subproblem dimension. If these coupling variables consist of a small, finite number of scalars, problem size does not increase appreciably, and efficient optimization of the system is still possible. However, if coupling comprises infinite-dimensional variables, such as functions, ensuring consistency becomes computationally challenging. Discretization is typically applied, transforming infinite-dimensional variables into finite-dimensional ones, which can be represented in vector form aswhere y is the independent variable and q is the number of discretized points. Although this transformation enables consistency constraints to be computed, it requires a large number of discretization points to ensure a sufficiently accurate representation of the function. The dimensionality (measured by q) of these vector-valued coupling variables (VVCVs) can become very large, resulting in high-dimension subproblem decision vectors. Thus, it is desirable to approximate VVCVs with a reduced dimension representation that preserves sufficient accuracy. The new coupling variables comprising these representations are known as reduced representation variables [2]. With the exception of the work by Sobieski [3], published literature has not addressed this issue. The majority of published techniques use simplified models of objective functions and constraints to approximate their more comp...

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A review of image-warping methods

Abstract: SUMMARY Image war ping is a transformation which maps all positions in one image

Cited by 247 publications

References 61 publications

Across Space and Time

Across Space and Time

A New Method for Geometric Quality Evaluation of Remote Sensing Image Based on Information Entropy

Reduced representations of vector-valued coupling variables in decomposition-based design optimization

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