The use of computer graphics in many areas allows a real object to be transformed into a three‐dimensional computer model (3D) by developing tools to improve the visualization of two‐dimensional (2D) and 3D data from series of data point. The tools involved the representation of 2D and 3D primitive entities and parameterization method using B‐spline interpolation. However, there is no parameterization method which can handle all types of data points such as collinear data points and large distance of two consecutive data points. Therefore, this paper presents a new parameterization method that is able to solve those drawbacks by visualizing the 2D primitive entity of scanned data point of a real object and construct 3D computer model. The new method has improved a hybrid method by introducing exponential parameterization method in the beginning of the reconstruction process, followed by computing B‐spline basis function to find maximum value of the function. The improvement includes solving a linear system of the B‐spline basis function using numerical method. Improper selection of the parameterization method may lead to the singularity matrix of the system linear equations. The experimental result on different datasets show that the proposed method performs better in constructing the collinear and two consecutive data points compared to few parameterization methods.

Genetic algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) are always implemented to solve different kinds of complex optimization problems. Each method contains its own advantages and the performance varies based on different case studies. There are many Soft Computing (SC) methods which can generate different result for the same optimization problems. However, no exact result is produced because random function is usually applied in SC methods. The performance maybe is affected by the parameter setting or operations inside each method. Therefore, the motivation of this paper is to compare the performance of GA, DE and PSO by using the same parameters setting and optimization problems. The experiments can prove that although same parameters setting are applied, but different fitness and time can be obtained. Based on the result, GA was proven to perform better compared to DE and PSO in obtaining highest number of best minimum fitness and faster than both methods.

Surface reconstruction by using 3-D data is used to represent the surface of an object and perform important tasks. The type of data used is important and can be described as either structured or unstructured. For unstructured data, there is no connectivity information between data points. As a result, incorrect shapes will be obtained during the imaging process. Therefore, the data should be reorganized by finding the correct topology so that the correct shape can be obtained. Previous studies have shown that the Kohonen self-organizing map (KSOM) could be used to solve data organizing problems. However, 2-D Kohonen maps are limited because they are unable to cover the whole surface of closed 3-D surface data. Furthermore, the neurons inside the 3-D KSOM structure should be removed in order to create a correct wireframe model. This is because only the outside neurons are used to represent the surface of an object. The aim of this paper is to use KSOM to organize unstructured data for closed surfaces. KSOM isused in this paper by testing its ability to organize medical image data because KSOM is mostly used in constructing engineering field data. Enhancements are added to the model by introducing class number and the index vector, and new equations are created. Various grid sizes and maximum iterations are tested in the experiments. Based on the results, the number of redundancies is found to be directly proportional to the grid size. When we increase the maximum iterations, the surface of the image becomes smoother. An area formula is used and manual calculations are performed to validate the results. This model is implemented and images are created using Dev C++ and GNUPlot.