Data produced out of microarray experiments are of great use for the physician when it is presented in a meaningful manner. This paper proposes hybrid intelligent methods for addressing the challenges in analyzing the microarray data. The concept of fuzzy and rough set is hybridized with FInformation (FRFI) for gene selection. An optimal fuzzy logic based classifier (FLC) is developed for sample classification using a hybrid Genetic Swarm Algorithm (GSA). Detailed experiments are conducted using microarray data related to Cancer and Rheumatoid Arthritis. From the simulation study, it is found that the proposed FRFI-FLC-GSA produces compact classification system with reasonably good informative genes that can be used for disease diagnosis.

Given vehicle images, we suggest a way to recognize the color of the vehicle contained in the image. The color feature of a vehicle is represented by a color histogram, and we decide the appropriate number of color histogram bins, which mainly affects the successful recognition rate. After generating the histograms, template matching is used to decide the vehicle color. In HSI (hue saturation intensity) color space, experimental results show that the partition of H, S, and I into 8, 4, 4, respectively, achieves the highest success rate up to 88.34%.

In this paper, we suggest a method to identify the clothing as one approach for identifying a person in the grayscale image captured by a CCTV camera. We use the edge information of the clothing area for extracting the textural feature of the clothing. The edge filter is applied to the clothing area, and the histograms on the edge values are constructed. The feature vector is composed from the histograms. We used Euclidean distance as a similarity measure between feature vectors, and successfully identified the clothing with 77.8% of success rate.

OpenVG 1.0 is a 2D vector graphics standard and its API (Application Programming Interface) was released by the Khronos Group. In this paper, we introduce our OpenVG 1.0 implementation, accelerated by OpenGL ES 1.x hardware. Our implementation is an efficient and cost-effective way of accelerating OpenVG, fully utilizing the existing hardware in current embedded systems. Conclusively, our OpenVG implementation shows dramatically outstanding performance with low power consumption.

We present a method to reconstruct a pipe or a canal surface from a point cloud (a set of unorganized points). A pipe surface is defined by a spine curve and a constant radius of a swept sphere, while a variable radius may be used to define a canal surface. In this paper, by using the shrinking and moving least-squares methods, we reduce a point cloud to a thin curve-like point set which will be approximated to the spine curve of a pipe or canal surface. The distance between a point in the thin point cloud and a corresponding point in the original point set represents the radius of the pipe or canal surface.

We present efficient and robust algorithms for intersecting a freeform surface with a ringed surface or a ruled surface. A ringed surface is given as a one-parameter family of circles. By computing the intersection between a freeform surface and each circle in the family, we can solve the intersection problem. We propose two approaches which are closely related to each other. The first approach detects certain critical points; and the intersection curve is constructed by connecting them in a correct topology. The second approach converts the intersection problem to that of finding the zero set of two polynomial equations in the parameter space. The intersection between a freeform surface and a ruled surface can be computed in a similar way.

Abstract We present an efficient and robust algorithm for parameterizing the perspective silhouette of a canal surface and detecting each connected component of the silhouette. A canal surface is the envelope of a moving sphere with varying radius, defined by the trajectory C(t) of its center and a radius function r(t) . This moving sphere, S(t) , touches the canal surface at a characteristic circle K(t) . We decompose the canal surface into a set of characteristic circles, compute the silhouette points on each characteristic circle, and then parameterize the silhouette curve. The perspective silhouette of the sphere S(t) from a given viewpoint consists of a circle Q(t) ; by identifying the values of t at which K(t) and Q(t) touch, we can find all the connected components of the silhouette curve of the canal surface. ACM CSS: I.3.7 Computer Graphics–Three Dimensional Graphics and Realism