35208191
Pattern generation with color map gouraud shading
Abstract This paper introduces a simple and fast method for generating intricate and aesthetically pleasing patterns. The essence of the method is rendering color map Gouraud-shaded polygons. In color map Gouraud shading, the interpolated values are not the RGB color components but the indices of entries in a color map. The method can produce a great variety of complex patterns, and the generation time depends almost solely on the speed of rendering Gouraud-shaded polygons.
8107037
Forward Shadow Mapping
Forward shadow mapping is a new approach to real-time shadow generation. The traditional shadow map algorithm maps the pixels in the eye’s view backward into the depth buffers of light sources (i.e. shadow maps), which is similiar to and often implemented as an extension to texture mapping. Our algorithm reverses this process by using 3-D image warping techniques to transform shadow map pixels forward into the eye’s view to directly indicate which pixels are lit; it does not interfere with normal texture mapping and easily supports antialiased shadow edges and projective textures. Access to shadow maps and projective textures is in pixel-sequential order. This algorithm has advantages when speed of texture mapping becomes the performance bottleneck, which is often the case in visual simulation and game applications.
11427190
Exploiting the IPID Field to Infer Network Path and End-System Characteristics
In both active and passive network Internet measurements, the IP packet has a number of important header fields that have played key roles in past measurement efforts, e.g., IP source/destination address, protocol, TTL, port, and sequence number/acknowledgment. The 16-bit identification field (IPID) has only recently been studied to determine what information it might yield for network measurement and performance characterization purposes. We explore several new uses of the IPID field, including how it can be used to infer: (a) the amount of internal (local) traffic generated by a server; (b) the number of servers in a large-scale, load-balanced server complex and; (c) the difference between one-way delays of two machines to a target computer. We illustrate and validate the use of these techniques through empirical measurement studies.
2491447
Unsupervised image segmentation using local homogeneity analysis
In this paper, a novel method is presented for unsupervised image segmentation based on local homogeneity analysis. First, a criterion for homogeneity of a certain pattern is proposed. Applying the criterion to local windows in the original image results in the "H-image". The high and low values of the H-image correspond to possible region boundaries and region interiors respectively. Then, a region growing method is used to segment the image based on the H-image. Finally, visually similar regions are merged together to avoid over-segmentation. Experimental results on real images show the effectiveness and robustness of the method.
13620764
Multi-level differential surface representation based on local transformations
One crucial issue of multi-resolution surface representations is how to effectively record and reconstruct geometric details among surface levels. Standard multi-resolution techniques encode details directly as local displacements in the vertices, and may produce unplausible results when the base level endures large deformations. In this paper we propose an alternative detail representation and reconstruction scheme, based on local transformations on a per-triangle basis. While more storage is required, recording details as local transformations favors global coupling of geometric details and allows for large-scale surface manipulations. By modeling the scale components of the surface modifications as a set of deforming factors, we achieved detail-preserving reconstruction results naturally under very large deformations. Comprehensive experimental results verify the efficiency and robustness of our approach.
10184956
Mesh Simplification Based on Shading Characteristic
Mesh simplification is a key research area in scientific visualization and virtual reality. The paper presents a new method of polyhedral model simplification based on vision characteristic and edge collapse. This method determines model ’s vision characteristic by computing the maximum product of angle between normal of vertex and adjacent triangles with area of these triangles. It can achieve a high decimation rate, while keeping the details of vision. This algorithm can also be extended to triangle decimation criterion easily.
140083992
Boosting image classification with LDA-based feature combination for digital photograph management
Image classification is of great importance for digital photograph management. In this paper we propose a general statistical learning method based on boosting algorithm to perform image classification for photograph annotation and management. The proposed method employs both features extracted from image content (i.e., color moment and edge direction histogram) and features from the EXIF metadata recorded by digital cameras. To fully utilize potential feature correlations and improve the classification accuracy, feature combination is needed. We incorporate linear discriminant analysis (LDA) algorithm to implement linear combinations between selected features and generate new combined features. The combined features are used along with the original features in boosting algorithm for improving classification performance. To make the proposed learning algorithm more efficient, we present two heuristics for selective feature combinations, which can significantly reduce training computation without losing performance. The proposed image classification method has several advantages: small model size, computational efficiency and improved classification performance based on LDA feature combination.