Fast simulation of the forward tracking detector of HPLUS

The necessity of installing a forward tracking detector stack is discussed for the Hadron Physics LanzhoU Spectrometer(HPLUS). A local tracker is developed to solve the multi-track finding problem. The track candidates are searched iteratively via Hough Transform. The fake tracks are removed by a least square fitting process. With this tracker we have studied the feasibility of     

用Hough变换提高激光光斑中心定位精度的算法

在激光扫描大型三维曲面测量中, 激光光斑中心的准确定位是提高测量分辨力的关键。当测距范围较大时, 受多种因素的影响, 激光光强分布严重不均致使其几何中心与强度中心发生了偏离。此时, 传统的光斑中心提取方法不能适用。为此, 根据光斑图像仍能给出圆形光斑的大部分轮廓这一特点, 提出了基于Ｈｏｕｇｈ 变换的激光光斑中心读取方法。实验结果表明该方法对提高大型三维曲面测量精度是有效的。     

基于切比雪夫多项式描述子的复杂背景下输电线路提取算法

在电力系统中,利用计算机视觉和图像处理技术对输电线路的进行检测,在保障电力系统的安全运行方面具有非常重要的作用；针对复杂背景下的输电线路,提出了一种基于切比雪夫多项式描述子输电线路提取算法,该算法首先对输入图像进行预处理,其次利用随机Hough 变换提取图像中的直线和曲线,然后利用切比雪夫多项式描述子的曲线描述优势,对所提取的对象进行特征描述,为了提高提取的精确度,通过K-means聚类分析方法来训练视觉字典,通过视觉字典的比对,最终确定图像中的输电线路；实验结果说明了所提出的算法可以精确地提取复杂背景下的输电线路。     

A local voting and refinement method for circle detection

Improving the accuracy of circle detection in images is important for feature detection in remote sensing imagery. The standard Hough transform detects circles from the whole image space, which inclines to be disturbed by noise and the imperfection of the edge information. In this paper, a method of voting and determining circles locally is proposed. This method improves the accuracy and stability of circle detection in images with noise, incomplete edge information and complex backgrounds. Synthetic and real satellite images are used to demonstrate the effectivity of circle detection of the proposed method.     

复杂工况下视觉AGV导航标识带中心线的提取研究

针对光照不均匀、导航标识带破损、少量杂物干扰的复杂工况条件下,对视觉AGV采集到的路面导航标识带图片进行特征提取,采用灰度化、中值滤波进行了图像预处理,研究了Otsu法和迭代法等动态阈值分割算法以及形态学在图像分割中的应用,并对预处理图像作了分割对比实验。然后对分割后的图像进行边缘提取,分析了基于最小二乘法和Hough变换算法的直线拟合原原理,提出了Hough算法下基于边缘线的中心线拟合算法,并作了直线拟合对比试验。实验表明,复杂工况下,采用基于形态学和Otsu算法相结合的方法对图像进行分割,得到的二值图像边界更完整,效果更好,基于Hough变换算法较最小二乘法能更精确有效地提取出导航标识带中心线及其方程。     

Adaptive contrast enhancement of two-dimensional electrophoretic protein gel images facilitates visualization, orientation and alignment

2-DE is a powerful technique to discriminate post-translationally modified protein isoforms. However, all steps of 2-DE preparation and gel-staining may introduce unwanted artefacts, including inconsistent variation of background intensity over the entire 2-DE gel image. Background intensity variations limit the accuracy of gel orientation, overlay alignment and spot detection methods. We present a compact and efficient denoising algorithm that adaptively enhances the image contrast and then, through thresholding and median filtering, removes the gray-scale range covering the background. Applicability of the algorithm is demonstrated on immunoblots, isotope-labeled gels, and protein-stained gels. Validation is performed in contexts of (i) automatic gel orientation based on Hough transformation, (ii) overlay alignment based on cross correlation and (iii) spot detection. In gel stains with low background variability, e.g. Sypro Ruby, denoising will lower the spot detection sensitivity. In gel regions with high background levels denoising enhances spot detection. We propose that the denoising algorithm prepares images with high background for further automatic analysis, without requiring manual input on a gel-to-gel basis.     

利用广义霍夫变换对相交目标进行轨迹预测

提出一种广义霍夫变换方法对相交目标进行轨迹预测,该方法利用概率分布原理,在参数空间中寻找点分布最集中的区域,以确定目标轨迹二次曲线方程的系数。为了提高搜索速度,提出采用投影法定位二次曲线方程的系数,从而使该方法满足实时性要求。该方法抗噪性强,克服了最小二乘轨迹预测法对噪声敏感的缺点。实验中采用最小二乘法和广义霍夫变换对相交目标进行轨迹预测,目标分离时正确匹配率分别为87.5%和95%。     

自由曲面索承网格结构的合理布索位置研究

为了给出自由曲面索承网格结构的合理布索位置,首先,将上部网格结构作为连续体,基于鲁棒性定量评价指标,采用改进的大爆炸算法,进行拓扑优化,获得自由曲面的鲁棒构型;其次,利用Hough变换对鲁棒构型进行检测,在布索数量确定后,提取决定布索曲线方向的参数;最后,对布索曲线上的全部节点进行试算,优化出布索曲线端点。算例表明,该方法可为复杂自由曲面索承网格结构优化出基于力学原理的合理布索方案,同时,通过与直观布索方案的对比,证明了本文求得的布索方案具有更好的力学性能,可为实际工程应用提供参考。