Journal ArticleDOI
Legendre moments as high performance bone biomarkers: computational methods and GPU acceleration
TLDR
A powerful combination of software and hardware methods which deliver a much faster response in contrast with existing solutions coming from conventional programming on multicore CPU platforms, thus offering a high performance alternative in clinical practice for real-time imaging.Abstract:
We investigate the use of Legendre moments as biomarkers for an efficient and accurate classification of bone tissue on images coming from stem cell regeneration studies. Legendre moments are analysed from three different perspectives: (1) their discriminant properties in a wide set of preselected vectors of features based on our clinical and computational experience, providing solutions whose accuracy exceeds 90%; (2) the amount of information to be retained when using principal component analysis to reduce the dimensionality of the problem to either 2, 3, 4, 5 or 6 dimensions and (3) the use of the -k-feature set problem to identify a k = 4 number of features which are more relevant to our analysis from a combinatorial optimisation approach. These techniques are compared in terms of computational complexity and classification accuracy to assess the strengths and limitations of the use of Legendre moments. The second contribution of this work goes to reduce the computational complexity by using graphics ...read more
Citations
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Journal ArticleDOI
Fast computation of 2D and 3D Legendre moments using multi-core CPUs and GPU parallel architectures
TL;DR: New parallel algorithms are proposed to speed up the process of exact Legendre moments computation for 2D and 3D image/objects and the first parallel CPU and GPU acceleration of the reconstruction phase of the Legendre moment is presented.
Journal ArticleDOI
CUDAQuat: new parallel framework for fast computation of quaternion moments for color images applications
TL;DR: In this article, a new parallel framework for fast computation of quaternion moments in Cartesian coordinates using multi-core CPUs and many-core GPUs with the Compute Unified Device Architecture (CUDA) is proposed.
References
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Journal ArticleDOI
Image analysis via the general theory of moments
TL;DR: Two-dimensional image moments with respect to Zernike polynomials are defined, and it is shown how to construct an arbitrarily large number of independent, algebraic combinations of zernike moments that are invariant to image translation, orientation, and size as discussed by the authors.
Book
Texture analysis
Mihran Tuceryan,Anil K. Jain +1 more
TL;DR: The geometric, random field, fractal, and signal processing models of texture are presented and major classes of texture processing such as segmentation, classification, and shape from texture are discussed.
Journal ArticleDOI
Invariant image recognition by Zernike moments
Alireza Khotanzad,Y.H. Hong +1 more
TL;DR: A systematic reconstruction-based method for deciding the highest-order ZERNike moments required in a classification problem is developed and the superiority of Zernike moment features over regular moments and moment invariants was experimentally verified.