A scalable architecture for geometric correction of multi-projector display systems

Multi-projector displays allow the realization of large and immersive projection environments by allowing the tiling of projections from multiple projectors. Such tiled displays require real time geometrical warping of the content that is being projected from each projector. This geometrical warping is a computationally intensive operation and is typically applied using high-end graphics processing units (GPUs) that are able to process a defined number of projector channels. Furthermore, this limits the applicability of such multi-projector display systems only to the content that is being generated using desktop based systems. In this paper we propose a platform independent FPGA based scalable hardware architecture for geometric correction of projected content that allows addition of each projector channel at a fractional increase in logic area. The proposed scheme provides real time correction of HD quality video streams and thus enables the use of this technology for embedded and standalone devices.     

Graph-based video fingerprinting using double optimal projection

A double optimal projection method that involves projections for intra-cluster and inter-cluster dimensionality reduction are proposed for video fingerprinting. The video is initially set as a graph with frames as its vertices in a high-dimensional space. A similarity measure that can compute the weights of the edges is then proposed. Subsequently, the video frames are partitioned into different clusters based on the graph model. Double optimal projection is used to explore the optimal mapping points in a low-dimensional space to reduce the video dimensions. The statistics and geometrical fingerprints are generated to determine whether a query video is copied from one of the videos in the database. During matching, the video can be roughly matched by utilizing the statistics fingerprint. Further matching is thereafter performed in the corresponding group using geometrical fingerprints. Experimental results show the good performance of the proposed video fingerprinting method in robustness and discrimination.     

任意曲线透视投影的逆变换

透视曲线的逆变换是透视图象三维逆变换的重要基础。本文在给出透视投影逆变换基本方程的基础上，分别提出了圆、椭圆、二次曲线、任意平面曲线的透视投影逆变换的解析方法或计算机反求方法。同时给出灭点信息不足明图象的透视参数的准确反求方法。     

扇形束投影域重建—再投影迭代法

本文所提出的投影域重建——再投影迭代算法可以恢复不完整的扇形束投影数据,从而实现从有限的投影数据重建图象。这种算法把滤波——反投影图象重建过程和再投影的过程纳入一个公式,仅在投影域上估算未知的投影数据,因此避免了重建过程和反投影过程中的插值运算,改善了收敛性,提高了计算精度,缩短了运算时间。模拟实验表明:即使有70％的投影数据丢失,经过两次迭代就能以令人满意的精度恢复丢失的投影数据。     

Projection center calibration by motion

Camera calibration is the first step of three-dimensional machine vision. A fundamental parameter to be calibrated is the position of the camera projection center with respect to the image plane. This paper presents a method for the computation of the projection center position using images of a translating rigid object, taken by the camera itself.

Many works have been proposed in literature to solve the calibration problem, but this method has several desirable features. The projection center position is computed directly, independently of all other camera parameters. The dimensions and position of the object used for calibration can be completely unknown.

This method is based on a geometric relation between the projection center and the focus of expansion. The use of this property enables the problem to be split into two parts. First a suitable number of focuses of expansion are computed from the images of the translating object. Then the focuses of expansion are taken as landmarks to build a spatial back triangulation problem, the solution of which gives the projection center position.     

关于透视投影逆变换基本方程及有关问题的讨论

对《任意曲线透视投影的逆变换》中作出的以下３项研究结果进行了探讨并提出了不同见解：（１）透视投影的逆变换基本方程；（２）根据长方体上一个矩形的投影确定其长宽高之比；（３）由实体的透视图象准确重建其三维信息。     

稠油油藏的注汽量预测

利用HCZ预测模型，可对已开发油田的年产量和累计产量进行全程预测，并且在实际应用中取得了较好的预测结果。本文结合稠油田的实际情况，推导出HXZ预测模型在热采中注汽参数的预测公式。结合曙光油田曙1－7－5块的开发数据进行了预测，效果较好。     

Facial detection and location based on skin color and eye characteristic

This paper comes true a system of facial detection and location. Firstly, it realizes the facial detection of candidate region used skin color for the given image, and considers the influence of similar skin. Secondly, it realizes the location for eye region used the gray characteristic of eye and correction of central measurement, and makes further selection for facial candidate region. The end, it normalizes the facial image and makes it as front standard image, so realizes the facial characteristic location.     

Steady-state weights solution to affine projection algorithm

A new expression of the weights update equation for the affine projection algorithm (APA) is proposed that improves the convergence rate of an adaptive filter,particularly for highly colored input sign...