Handbook of Approximation: Algorithms and Metaheuristics

Legendre moments and their invariants for 2D and 3D image/objects are widely used in image processing, computer vision, and pattern recognition applications. Reconstruction of digital images by nature required higher-order moments to get high-quality reconstructed images. Different applications such as classification of bacterial contamination images utilize high-order moments for feature extraction phase. For big size images and 3D objects, Legendre moments computation is very time-consuming and compute-intensive. This problem limits the use of Legendre moments and makes them impractical for real-time applications. Multi-core CPUs and GPUs are powerful processing parallel architectures. In this paper, new parallel algorithms are proposed to speed up the process of exact Legendre moments computation for 2D and 3D image/objects. These algorithms utilize multi-core CPUs and GPUs parallel architectures where each pixel/voxel of the input digital image/object can be handled independently. A detailed profile analysis is presented where the weight of each part of the entire computational process is evaluated. In addition, we contributed to the parallel 2D/3D Legendre moments by: (1) a modification of the traditional exact Legendre moment algorithm to better fit the parallel architectures, (2) we present the first parallel CPU implementation of Legendre moment, and (3) we present the first parallel CPU and GPU acceleration of the reconstruction phase of the Legendre moments. A set of numerical experiments with different gray-level images are performed. The obtained results clearly show a very close to optimal parallel gain. The extreme reduction in execution times, especially for 8-core CPUs and GPUs, makes the parallel exact 2D/3D Legendre moments suitable for real-time applications.

Fast computation of 2D and 3D Legendre moments using multi-core CPUs and GPU parallel architectures

Quaternion moments are widely used in several applications, such as image classification, object recognition, and multimedia security The computation of these moments requires a vast computational time, especially for big-size images Several attempts to accelerate quaternion moments are not enough to process big-size color images with the desired speedup In this work, we proposed a new parallel framework for fast computation of quaternion moments in Cartesian coordinates using multi-core CPUs and many-core graphics processing units (GPUs) with the Compute Unified Device Architecture (CUDA) We called the proposed unified computational framework “CUDAQuat” This framework was tested by eleven sets of quaternion moments Several applications executed using the proposed parallel framework where the CPU times, execution-time-improvement ratio, and speedup were reported The evaluation outlined significant speedup over the single-core CPU implementation, where the proposed framework accelerated several sets of quaternion moments with speedup 600x

CUDAQuat: new parallel framework for fast computation of quaternion moments for color images applications

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 ...

Legendre moments as high performance bone biomarkers: computational methods and GPU acceleration

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. Regions of either existing bone, cartilage or new bone-forming cells are characterized at tile level to quantify the degree of bone regeneration depending on culture conditions. Legendre moments are analyzed 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 (PCA) to reduce the dimensionality of the problem from 2 to 6 dimensions. (3) the use of the (α-β)-k-feature set problem to identify a k = 4 number of features which are more relevant to our analysis from a combinatorial optimization 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 for this biomedical image processing application.

José Antonio Lachiondo

Papers

Legendre moments as high performance bone biomarkers: computational methods and GPU acceleration

Quantifying the regeneration of bone tissue in biomedical images via Legendre moments