Rendering process of digital terrain model on mobile devices

Digital Terrain Model has been used in many applications especially in Geographical Information System. However with the recent improvement in mobile devices that can support 3 Dimension (3D) content, rendering 3D based terrain on mobile devices is possible. Although mobile devices have improved its capabilities, rendering 3D terrain is tedious due to the constraint in resources of mobile devices. Furthermore, rendering DTM add more constraint and issues to the mobile devices. This paper focuses on the rendering process of DTM on mobile devices to observe some issues and current constraints occurred. Also to determine the characteristic of terrain properties that will affect the rendering performance. Experiments were performed using five datasets that derived from aerial images. The experimental results are based on speed of rendering and the appearance of the terrain surface. From these results, issues and problems that are highlighted in this paper will be the focus of future research

Comparative study of clustering algorithms in order to virtual histology (VH) image segmentation

Atherosclerosis is the deadliest type of heart disease caused by soft or “vulnerable” plaque (VP) formation in the coronary arteries. Recently, Virtual Histology (VH) has been proposed based on spectral analysis of Intravascular Ultrasound (IVUS) provides color code of coronary tissue maps. Based on pathophysiological studies, obtaining information about existence and extension of confluent pool’s component inside plaque is important. In addition, plaque components’ localization respect to the luminal border has major role in determining plaque vulnerability and plaque–stent interaction. Computational methods were applied to prognostic the pattern's structure of each component inside the plaque. The first step for post-processing of VH methodology to get further information of geometrical features is segmentation or decomposition. The medical imaging segmentation field has developed to assist cardiologist and radiologists and reduce human error in recent years as well. To perform color image clustering, several strategies can be applied which include traditional hierarchical and nonhierarchical. In this paper, we applied and compared four nonhierarchical clustering methods consists of Fuzzy C-means (FCM), Intuitionistic Fuzzy C-means (IFCM), K-means and SOM artificial neural networks in order to automate segmentation of the VH-IVUS images

A survey on various image deblurring methods

Image blur is one of the main types of degradation that reduces image quality. Image deblurring is an attempt to invert blurring process by using mathematical model to get best estimation of latent (sharp) image. Blurring can be modeled mathematically as a convolution process between two functions which are image and Point Spread Function (PSF). PSF can be classified into more than one type depending on the reason for blurring. Gaussian is the type of PSF this study will focus on, and an implementation of such PSF to compare different deblurring methods. Based on the availability of prior knowledge about the degradation kernel (PSF), the deblurring methods can be divided into two major categories which are non-blind deconvolution and blind-deconvolution. Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) are the tools used to estimate the performance of these methods