Proc. SPIE. 5292, Human Vision and Electronic Imaging IX
KEYWORDS: Visual process modeling, Data modeling, Databases, Computer simulations, Data processing, Human vision and color perception, Virtual reality, Motion models, Solid modeling, Vision-based navigation
Intelligent virtual human is widely required in computer games, ergonomics software, virtual environment and so on. We present a vision-based behavior modeling method to realize smart navigation in a dynamic environment. This behavior model can be divided into three modules: vision, global planning and local planning. Vision is the only channel for smart virtual actor to get information from the outside world. Then, the global and local planning module use A* and
D* algorithm to find a way for virtual human in a dynamic environment. Finally, the experiments on our test platform (Smart Human System) verify the feasibility of this behavior model.
Object-order volume rendering algorithms play important part in many visualization applications for their excellent performances. Though many volume rendering algorithms have been proposed during the past two decades, most of them are image-order algorithms. Splatting, one of the classical object-order algorithms, suffers from several kinds of aliasing artifacts for inaccuracy reasons. A much accurate object-order volume rendering algorithm is presented in this paper. By defining a set of data structures to serve as two step reconstruction lookup tables, together with using a simple voxel traversal and resample strategy, the new algorithm can not only get rid of inaccuracy of traditional splatting, but also have the features including high cache hit rate, easy to implement of parallelism and high speedup from pre-processing.
This paper described the design of the DAVRS system. This system not only provides a 3D design environment for architects, but also realizes distributed collaboration between the designers through the internet. The DAVRS system used Java3D to construct virtual sense, XML to pocket sense controlling data, and Java MQ to transmit data. Moreover, a three-level distributed collaboration model is designed in order to confirm the safety of collaboration based on internet.
Volume rendering has been a key technology in the visualization of data sets from various disciplines. However, real-time volume rendering of large scale data sets is still a challenging field due to the vast memory, bandwidth and computational requirements. In this paper, to visualize small to medium scale data set in real-time, we first proposed a new kind of volume rendering graphic processor based on object-order splatting algorithm in which flexible transfer function configuration and software optimization such as early opacity termination and transparent voxel occlusion can be achieved. At the same time, the processor also integrates an eight-way interleaved memory system and an efficient address calculation module to accelerate the voxel traversal process and maintain high cache hit rate. Multiple parallel rendering pipelines embedded also can achieve local parallelism on board. Second, in order to render large scale data sets, a real-time and general-purpose volume rendering architecture is also presented in this paper. By utilizing graphic processors on PC clusters, large scale data sets can be visualized resulted from the high parallel speedup among graphic processors.
This paper presents a simple method to calibrate the intrinsic parameters of zoom-lens digital cameras. This method combines the classical calibration algorithm using a planar pattern and the Exchangeable Image File Format (EXIF) metadata of image files captured by digital cameras. The EXIF metadata records many information about the camera’s setting such as the focal length of zooming lens. So we can use the focal length from EXIF to know the zoom lens setting. Firstly, a pre-calibration should be done to know the relationship between zoom lens settings and the intrinsic camera parameters. We take some sample lens settings from the minimum focal length to the maximum one by changing the lens zooming positions, and perform the mono focal calibration for each lens setting configuration. Then we get the coefficients of the polynomial function through curve fitting. After that we can get the intrinsic parameters correspond with the zoom lens setting of new image files shoot by this digital camera. Our experiments show the proposed method can provide accurate intrinsic camera parameters for all the lens settings continuously.
Splatting is a one of the most important object-order volume rendering algorithm. In this paper, a new run length encoding (RLE) accelerated, pre-classification and pre-shade volume splatting algorithm is presented, which enhances the speed of splatting without trading off image quality. This new technique saves rendering time by employing RLE mechanism so that only voxels of interest are processed in splatting. Data structures are defined to fully exploit spatial coherence of volume, including a slice scanline pointer array, a data pointer array, a scanline RLE array and an array storing all data of the non-transparent voxels. And a much fast and accurate sheet buffer splatting method is used in the rendering process, which accelerates the splatting by traversing both the voxel scanline and the image scanline in sheet buffer simultaneously. Experiments practice proves that RLE can efficiently skip over transparent voxels and high speedup can be obtained by using the proposed algorithm. Analysis on speed and memory cost of the algorithm is also conducted. This algorithm may be particularly used in situation where transfer function seldom changes.
Three-dimensional medical reconstruction has been a powerful technique in medical diagnosis, especially by using volume visualization of medical datasets such as those obtained from computed tomography (CT), magnetic resonance imaging (MRI) in recent years. A new medical volume reconstruction algorithm is presented in this paper. By examining the relations among three eigenvalues of local block based moment (LBBM) inertia matrix, the method defines transfer function in the domain of these eigenvalues. The eigenvalues and eigenvectors of the LBBM inertia matrix form a local coordinate system, which measures the local features such as flat, round, and elongated shapes of the object. The optimal window size of local voxel block is determined by experiments, and then two popular volume visualization algorithms are implemented to test the proposed transfer function design method. The proposed method can efficiently depict trivial features in medical datasets, especially useful in the rendering of structures with obvious shapes, such as round, flat, and elongated shapes. The new algorithm can not only result informative rendering results to the doctors, but also can efficiently reduce the time previously spent in trial-and-error process.
Three-dimensional volume reconstruction has gained great popularity as a powerful technique for the visualization of volume datasets such as those obtained from X-ray, computed tomography, and magnetic resonance imaging in recent years. Local features play important part in the classification process for a variety of medical image analysis, computer-aided diagnosis, and three-dimensional reconstruction and visualization applications. By using high-order local statistic features detected by local block based moments, such as flat, round, elongated shapes, together with the local spectral histogram of textures, to act as classification criteria, a three-dimensional medical reconstruction method is proposed in this paper. A volume splatting algorithm by using the proposed classification method is implemented and relatively high-quality rendering results can be obtained when the proposed method is applied in medical reconstructions.