Some numerical methods for solving geodesic active contour model on image segmentation process

In this paper, we present an edge-based image segmentation technique using modified geodesic active contour model to detect the desired objects from an image. The stopping function of the proposed model has been modified from the usual geodesic active contour model. The modified geodesic active contour model is discretized using finite difference method based on the central difference formula. Then, some numerical methods such as RBGS and Jacobi methods are used for solving the linear system of equation. The accuracy and effectiveness of the proposed algorithm have been illustrated by applied to different images and some numerical methods

High performance large sparse PDEs with parabolic and elliptic types using AGE method on DPCS

The computational analysis of three case studies using parallelization of Alternating Group Explicit (AGE) solver is presented. Based on (2×2) block system and splitting strategy, AGE with Douglas-Richford and Brian variances are applied to simulate the large sparse PDEs applications with parabolic and elliptic types. The applications are heat equation, food dehydration for preservation and breast cancer growth. The AGE method has proved to be stable and suitable for parallel computing as it possesses separately and independently. The performance of AGE is compared with classical iterative methods such as Red Black Gauss Seidel (RBGS) and Jacobi (JB) methods. Since the PDEs applications are large sparse problems, we apply the AGE method in three different applications with three different mathematical models. The parallel implementation is based on SIMD model and supported by distributed memory architecture. Therefore, some numerical analysis and parallel performance indicators are used to validate the superior of parallel AGE method in terms of time execution, speedup, efficiency and effectiveness. As a result, the performances of numerical analysis and parallel evaluation of AGE are found to be effective for solving three case studies in reducing data storage accesses and minimizing communication time on a distributed parallel computer system

Parallel artificial neural network approaches for detecting the behaviour of eye movement using CUDA software on heterogeneous CPU-GPU systems

Eye movement behaviour is related to human brain activation either during asleep or awake. The aim of this paper is to measure the three types of eye movement by using the data classification of electroencephalogram (EEG) signals. It will be illustrated and train using the artificial neural network (ANN) method, in which the measurement of eye movement is based on eye blinks close and open, moves to the left and right as well as eye movement upwards and downwards. The integrated of ANN with EEG digital data signals is to train the large-scale digital data and thus predict the eye movement behaviour with stress activity. Since this study is using large-scale digital data, the parallelization of integrated ANN with EEG signals has been implemented on Compute Unified Device Architecture (CUDA) supported by heterogeneous CPU-GPU systems. The real data set from eye therapy industry, IC Herbz Sdn Bhd was carried out in order to validate and simulate the eye movement behaviour. Parallel performance analyses can be captured based on execution time, speedup, efficiency, and computational complexity

Mathematical modeling for contour identification based on medicinal leaves and GIS images

In this paper, the identification of contour medicinal leaves and GIS images has been determined. The purposes of the Geodesic Active Contour-Additive Operating Splitting (GAC-AOS) modelling are to identify an unknown type of medicinal leaves and edge detection of our images. Besides, three iterative methods such as SOR, RBGS and Jacobi method are used to solve the linear system of equations. In the implementation of the GAC-the AOS model, the experimental result demonstrates that the SOR method gives the best performance compared to the other two methods. The computation platform is based on Intel® CoreTM Duo Processor Architecture with MATLAB version R2011a. The performance analysis is based on the iteration numbers, execution time, accuracy and RMSE

Embedded system on high performance data for wearable augmented reality of eye blinks, muscle stress detection movement and observation

Eyes blinking and its movement can portray many reasons of the body and health state. Eyes can blink intentionally and sometimes randomly even in sleeping mode. Thus, the aim of this paper is to discover and observe the relationship between the frequency of eye blink and the level of eye muscle stress. The eye track data is fed directly into the electroencephalogram (EEG) record for parameter classification and identification. The EEG signal might have an artifact that has been analyzed and converted the observation into the mathematical library and repository software (HPC). The artificial neural network (ANN) is integrated with EEG digital data by the derivation of the mathematical modelling. The function of ANN is to train a large sparse digital data for future prediction of eye condition associated with the stress level. In order to validate the model and simulation, the numerical analysis and performance evaluation are compared to the real data set of eye therapy industry, IC Herbz Sdn Bhd. A library and repository software of mathematical model using EEG record data is developed to integrate with wearable augmented reality (WAR) based on EEG sensor device for predicting and monitoring the real time eye blinks, movement and muscle stress

Parallelization of modified geodesic active contour model on high-resolution satellite image for segmentation process

A modified Sign Pressure Force (SPF) function able to enhance the existing Edge Stopping Function (ESF) in terms of simulation, visualization, and segmentation of high-resolution satellite images of Nusajaya using the Geodesic Active Contour (GAC) model. The modified SPF function is formulated by integrating both the local SPF function and the global SPF function. Next, the modified GAC model is extended to a higher-order modified GAC model. The second-order and fourth-order modified GAC models are implemented using the Finite Difference Method (FDM) and developed into a tri-diagonal and Penta-diagonal Linear System of Equations (LSE). Some numerical methods such as Second-Order Alternating Group Explicit (AGE2), Second-Order Red-Black Gauss-Seidel (RBGS2), and Second-Order Jacobi (JB2) methods are used to solve the LSE of second-order modified GAC model. Meanwhile, Fourth-Order Alternating Group Explicit (AGE4), Fourth-Order Red-Black Gauss-Seidel (RBGS4), and Fourth-Order Jacobi (JB4) methods are used to solve the LSE of the fourth-order modified GAC model. The sequential algorithm is developed using Matlab R2015a software. The indicator of numerical results is analyzed based on execution time, number of iterations, maximum error, root mean square error, and computational complexity. The actual high-resolution satellite images of Nusajaya generate a large amount of data, resulting in an enormous amount of execution time and high computational complexity. Thus, the implementation of a parallel algorithm is a reliable alternative for improving the sequential computation and reduced the execution time up to 82.23%. The parallel computation obtains an extensive large scale simulation capability for high-resolution satellite image data. The domain decomposition strategy is implemented by using the Matlab parallel computing toolbox based on the shared memory architecture. Parallel performance evaluations of numerical methods are measured based on speedup, efficiency, effectiveness, temporal performance, and granularity. As a conclusion, this investigation has proven the second-order modified GAC model could be extended to a fourth-order modified GAC model to simulate and visualize edge-region segmentation of high-resolution satellite images. Consequently, the Parallel Fourth-Order Alternating Group Explicit (PAGE4) method is an alternative solution for large sparse segmentation process of high-resolution satellite images of Nusajaya as it improves the performance up to 82.26%. Based on the numerical results and parallel performance measurements, the parallel algorithm is proved to reduce the execution time and computational complexity up to 82.23% compared to the sequential algorithm

Parallel performance comparison of alternating group explicit method between parallel virtual machine and matlab distributed computing for solving large sparse partial differential equations

This paper presents the parallel implementation and performance analysis of a large sparse matrix of one dimensional parabolic Partial Differential Equation (PDE) between Parallel Virtual Machine (PVM) and Matlab Distributed Computing (MDC) platform. PVM is a software to perform parallel programming using heterogeneous or homogeneous clusters of parallel computing system connected by a logical network system to users as virtual memory parallel computer system. MDC is a scientific library programming language. Alternating Group Explicit (AGE) method is used to solve the parabolic equation with large sparse matrix. The distributed parallel algorithm of AGE method is implemented on the PVM with RedHat Linux operation and MDC Version 2011a. The parallel performance evaluation (PPE) is compared between the two platforms in terms of time execution, speedup, efficiency, effectiveness, and temporal performance. From the performance analysis, we can conclude that PVM is better compared to MDC

The chronology of geographic information system image processing: a case study for land changes at Nusajaya, Iskandar Malaysia

In the last few years, land demands in Iskandar Malaysia become important issues for stakeholders and town planners. Our study case area is at Nusajaya, Iskandar Malaysia. The motivation and the goal of this paper is to investigate four mathematical modelling in image processing integrate with GIS such as CA-GIS integration, ANN-GIS integration, neural network based cellular automata-GIS and GAC-AOS model. Geographic Information Systems (GISs) is the combination of cartography, statistical analysis and computer science technology. The general GIS have its limitation in modelling changes in the landscape overtime. Integration GIS with CA and ANN, will overcome the drawbacks of GIS. Geodesic Active Contour (GAC) model are used to detect the edges of Nusajaya images. Thus some classical numerical methods (Gauss-Seidel and Jacobi method) are used to solve the system linear of equations and the experimental results demonstrate that the Gauss-Seidel (GS) method gives the best performance compared to Jacobi (JB) method. © 2014 American Scientific Publishers All rights reserved

High performance embedded system for wearable augmented reality of eye blinks, movement, muscle stress detection and observation

Abstract—Eyes blinking and its movement can portray many reasons of the body and health state. Eyes can blink intentionally and sometimes randomly even in sleeping mode. Thus, the aim of this paper is to discover and observe the relationship between the frequency of eye blink and the level of eye muscle stress. The eye track data is fed directly into the electroencephalogram (EEG) record for parameter classification and identification. The EEG signal might have an artifact that has been analyzed and converted the observation into the mathematical library and repository software (HPC). The artificial neural network (ANN) is integrated with EEG digital data by the derivation of the mathematical modelling. The function of ANN is to train a large sparse digital data for future prediction of eye condition associated with the stress level. In order to validate the model and simulation, the numerical analysis and performance evaluation are compared to the real data set of eye therapy industry, IC Herbz Sdn Bhd. A library and repository software of mathematical model using EEG record data is developed to integrate with wearable augmented reality (WAR) based on EEG sensor device for predicting and monitoring the real time eye blinks, movement and muscle stress