Showing papers by "Anupam Agrawal published in 2006"

Geometry-based Mapping and Rendering of Vector Data over LOD Phototextured 3D Terrain Models

Abstract: Interactive three-dimensional (3D) visualization of very large-scale grid digital elevation models coupled with corresponding high-resolution remote-sensing phototexture images is a hard problem. The graphics load must be controlled by an adaptive view-dependent surface triangulation and by taking advantage of different levels of detail (LODs) using multiresolution modeling of terrain geometry. Furthermore, the display of vector data over the level of detail terrain models is a challenging task. In this case, rendering artifacts are likely to occur until vector data is mapped consistently and exactly to the current level-of-detail of terrain geometry. In our prior work, we have developed a view-dependent dynamic block-based LOD mesh simplification scheme and out-ofcore management of large terrain data for real-time rendering on desktop PCs. In this paper, we have proposed a new rendering algorithm for the combined display of multiresolution 3D terrain and polyline vector data representing the geographical entities such as roads, state or country boundaries etc. Our algorithm for multiresolution modeling of vector data allows the system to adapt the visual mapping without rendering artifacts to the context and the user needs while maintaining interactive frame rates. The algorithms have been implemented using Visual C++ and OpenGL 3D API and successfully tested on different real-world terrain raster and vector data sets.

Out-of-core construction and 3d visualization of level-of-detail terrains populated with large collection of heterogeneous objects

Abstract: A footing box has notches for receiving an end of grooved grade beams. The end of each beam includes an aperture for receiving a reinforcing rod extending lengthwise from a grooved column supported on the ends of the beams in the footing box, to enable the grade beams and column to be unified when grout is poured into the footing box. Grooved roof beams are supported at their ends by the column and connected to the column by a reinforcing rod extending through an aperture in the end of each roof beam. Wall panels are positioned in the grooves of the column and the beams.

Nonparametric neural network model based on rough-fuzzy membership function for classification of remotely sensed images

Abstract: A nonparametric neural network model based on Rough-Fuzzy Membership function, multilayer perception, and back-propagation algorithm is described. The described model is capable to deal with rough uncertainty as well as fuzzy uncertainty associated with classification of remotely sensed multi-spectral images. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of rough fuzzy class membership values. This allows efficient modeling of indiscernibility and fuzziness between patterns by appropriate weights being assigned to the back-propagated errors depending upon the Rough-Fuzzy Membership values at the corresponding outputs. The effectiveness of the model is demonstrated on classification problem of IRS-P6 LISS IV images of Allahabad area. The results are compared with statistical (Minimum Distance), conventional MLP, and FMLP models.

Nonparametric neural network model based on rough-fuzzy membership function for classification of remotely sensed images

Abstract: A nonparametric neural network model based on Rough-Fuzzy Membership function, multilayer perceptron, and back-propagation algorithm is described. The described model is capable to deal with rough uncertainty as well as fuzzy uncertainty associated with classification of remotely sensed multi-spectral images. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of rough fuzzy class membership values. This allows efficient modeling of indiscernibility and fuzziness between patterns by appropriate weights being assigned to the back-propagated errors depending upon the Rough-Fuzzy Membership values at the corresponding outputs. The effectiveness of the model is demonstrated on classification problem of IRS-P6 LISS IV images of Allahabad area. The results are compared with statistical (Minimum Distance), conventional MLP, and FMLP models.