视频图像在三维模型中的渲染

视频监控是大型安防工作中最重要的一个环节,传统的视频监控系统展示给用户的只是独立的二维平面图像,图像脱离环境,整体性不足,为了解决该问题,提出了将视频图像渲染显示于三维模型的思路.研究了纹理映射技术并结合视频图像渲染的特殊性分析研究了Z缓冲算法、Shadow map算法,根据实际应用提出Extend Shadow map算法以实现将视频图像与三维场景无缝贴合.实验结果表明,视频图像渲染显示于三维模型中所体现的优越性.     

基于SOM神经网络聚类的车牌号码倾斜校正算法

经过摄像机摄入的图像会发生倾斜,给车牌的准确识别带来了困难.针对此问题,利用SOM神经网络良好的聚类性能,在水平倾斜校正时,把车牌号码图像中的像素坐标聚成两类,拟合成一条直线,计算出该直线倾斜角,完成水平校正;按照以上同样方法进行垂直倾斜校正.实验结果表明,该方法能准确获取车牌号码的倾斜角,算法结构简单,抗干扰能力较强,符合汽车牌照图像的特点,具有较好的处理效果.     

水平面显示屏虚拟斜置算法

为使人们在观看水平面显示屏时保持舒适的姿势,设计1种使水平面显示屏虚拟斜置的方法. 根据斜置平面与水平面显示屏的投影关系,提出上置法和下置法两种实现方式,得出其相应的虚拟算法. 比较两种算法的优缺点表明,上置法对屏幕尺寸有一定要求,但可以保持视点处于固定的位置,下置法对屏幕尺寸无过高要求,但视点要移动. 最后通过具体实验证明两种算法的可行性和可靠性.     

基于离散区间的频繁嵌入式子树挖掘算法

针对频繁嵌入式子树挖掘,利用离散区间来构造投影库,给出一种基于离散区间的频繁嵌入式子树挖掘算法。该算法通过离散区间消除冗余投影,有效地压缩投影库的规模,提高了子树节点计数效率,减低了算法的时空复杂性。实验结果表明该算法具有较高的挖掘效率。     

彩色图像中纯脸检测与定位的优化算法研究

人脸识别是当前模式识别应用的一个重要领域.在理解当前广泛使用的各种人脸识别算法的基础上,提出了一种基于多方法融合的彩色图像纯脸检测与定位的优化算法.该算法首先通过肤色检测及预处理技术缩小彩色图像人脸检测的搜索区域,然后应用基于物体区域方向的检测平面内任意旋转角度人脸计算方法;并通过二次计算旋转角度的方法来准确确定人脸区域的旋转角度;最后利用积分投影函数找到人脸候选区域中双眼的位置,结合人脸特征在人脸中的比例关系准确确定纯脸的位置.该算法还考虑了人脸侧偏时的情况.实验表明,本方法对平面内任意旋转及双眼存在的侧偏人脸有较好的检测效果,对不同光照条件有较好的鲁棒性.     

基于投影匹配的钢坯端面字符快速识别方法

由于高温及复杂光照的干扰等成像环境的影响,钢坯端面字符自动识别存在很多困难,其实时自动识别难度大。现存的字符识别方法难以适应实际生产现场的需求。文章提出了一种基于投影匹配的快速识别方法,通过将图象二值化后进行垂直投影,直接将一维投影结果进行模板匹配,降低运算维数。大量的现场实验证明,该方法可有效的减少识别时间,提高生产效率,字符的拒识率和误识率较低,可满足生产线的实时要求。     

Matlab遗传算法工具箱在非线性优化中的应用

投影寻踪是一种降维处理技术,通过它可以将多维分析问题通过投影方向转化为一维问题分析。应用该法的关键在于寻求最佳投影方向,这可以转化为一个复杂的非线性优化问题来进行解决。选取某地区大气环境质量评价的投影寻踪评价模型,编制相关的目标函数和约束函数,应用基于MATLAB的遗传算法和直接搜索工具箱进行优化求解。结果表明：该工具箱在求解此类非线性优化问题上的有效性和方便性,从而为各领域应用投影寻踪模型提供了强有力的优化工具。     

LPP solution schemes for use with face recognition

Locality preserving projection (LPP) is a manifold learning method widely used in pattern recognition and computer vision. The face recognition application of LPP is known to suffer from a number of problems including the small sample size (SSS) problem, the fact that it might produce statistically identical transform results for neighboring samples, and that its classification performance seems to be heavily influenced by its parameters. In this paper, we propose three novel solution schemes for LPP. Experimental results also show that the proposed LPP solution scheme is able to classify much more accurately than conventional LPP and to obtain a classification performance that is only little influenced by the definition of neighbor samples.     

Weighted and extended total variation for image restoration and decomposition

In various information processing tasks obtaining regularized versions of a noisy or corrupted image data is often a prerequisite for successful use of classical image analysis algorithms. Image restoration and decomposition methods need to be robust if they are to be useful in practice. In particular, this property has to be verified in engineering and scientific applications. By robustness, we mean that the performance of an algorithm should not be affected significantly by small deviations from the assumed model. In image processing, total variation (TV) is a powerful tool to increase robustness. In this paper, we define several concepts that are useful in robust restoration and robust decomposition. We propose two extended total variation models, weighted total variation (WTV) and extended total variation (ETV). We state generic approaches. The idea is to replace the TV penalty term with more general terms. The motivation is to increase the robustness of ROF (Rudin, Osher, Fatemi) model and to prevent the staircasing effect due to this method. Moreover, rewriting the non-convex sublinear regularizing terms as WTV, we provide a new approach to perform minimization via the well-known Chambolle's algorithm. The implementation is then more straightforward than the half-quadratic algorithm. The behavior of image decomposition methods is also a challenging problem, which is closely related to anisotropic diffusion. ETV leads to an anisotropic decomposition close to edges improving the robustness. It allows to respect desired geometric properties during the restoration, and to control more precisely the regularization process. We also discuss why compression algorithms can be an objective method to evaluate the image decomposition quality.     

The evaluation of speckle contrast with variable speckle generator

Abstract— While laser projection has many advantages, there is a problem with speckle patterns generated as a result of interference of the laser beam and results in bad effects to observers. In 2010, a variable speckle generator, which produces an angular shift of incident light to the screen and generates variable speckle patterns, was suggested. In this study, the performance of a variable speckle generator by using a volume phase holographic beam shaper and scanning mirror was investigated in detail by evaluating both the objective and subjective speckle contrast. The morphology of the speckle pattern was also investigated when the variable speckle generator was activated. With a scanning VPH beam shaper, the objective speckle was effectively reduced because each point of the VPH beam shaper generated different speckle patterns and coherency among each pattern disappeared by using the scan process. On the other hand, subjective speckle was also dramatically reduced by changing the incident angle on the screen, which resulted in generating variable subjective speckle. It was also shown that the speckle reduction rate by using a variable speckle generator did not depend on the coherent length of a laser by evaluating the normalized speckle contrast against the angular shift on the screen.