表面微观形貌的显微干涉检测原理及干涉显微镜发展现状

追踪分析世界上表面微观形貌检测方面显微干涉检测原理的最新进展 ,比较干涉显微镜用于检测表面微观形貌时具有的形式、结构特点 ,分析选型研制干涉显微镜可能遇到的问题及应该研究的方面。     

表面三维微观形貌检测技术及其发展

从表面特征衡量的角度阐述三维参数评定的客观性及合理性,强调三维微观形貌测量的重要民生。介绍当前表面三维微观形貌检测的多种测量方法及其特点,并阐述该测量技术的发展及趋势,提出表面形貌检测应从简单过程检测的角度扩展到完成工艺优化角色的思想。     

用普通金相显微镜拍摄彩色金相照片技术

用普通金相显微镜拍摄彩色金相照片技术华中理工大学奚素碧彩色照片与黑白照片相比具有色彩鲜艳、分辨率高等特点。由于光的薄膜干涉效应，对于显微组织中的成分偏析、晶粒位向以及应力分布状态很敏感，因此彩色照片提供更加丰富的组织细节。另外，对难于浸蚀的合金和复合...     

CK40M型倒置式金相显微镜

简单介绍CK40M型金相显微镜的特点、性能,以及实际使用效果。     

研磨表面微观形貌的三维检测及Areal表征

介绍了三维表面微观形貌的检测方法,分析了采用高斯滤波提取基准中面的原理,针对研磨表面形貌的表征选取了一组Areal表征参数。运用原子力显微镜(AFM)扫描研磨工件的表面,采用高斯滤波提取基准中面进而分离出表面微观形貌的三维信息,在此基础上计算出表征参数值。试验表明研磨表面微观形貌呈现高斯分布规律,采用高斯滤波方法及所选的Areal表征参数能够有效地表征研磨表面的三维微观形貌。     

基于单目视觉三维恢复算法的微观形貌原位测量方法研究

工件表面微观形貌分析在机械加工及测量领域有着十分重要的作用。与二维形貌分析相比,三维形貌分析能够提供更全面的表面信息。然而,现有复杂、昂贵的先进测量手段始终无法实现原位测量。为了实现对工件表面现场检测和三维形貌表征,将基于单目视觉的SFS(shape from shading,从明暗恢复形状)算法及光度立体法应用至微观形貌原位测量领域,提出一种可以进行二维图像采集并进行三维形貌重构的原位测量方法,并对比两种算法在微观形貌三维恢复上的结果。基于上述方法,进行SiC工件表面三维形貌恢复试验,同时与共聚焦显微镜扫描结果比较。试验结果表明,相比于SFS方法,光度立体法重构结果更加准确,截线特征点高度差相对误差低于27.2%,截线二维参数相对误差低于32.8%,表面三维参数相对误差低于19.6%,为解决机械零件表面微观形貌原位测量难题给出了新的解决思路。     

数码照相与计算机技术在台式金相显微镜上的应用

将普通数码照相机、计算机、激光(或喷墨)打印机应用于台式金相显微镜,实现金相显微镜照相系统的计算机化,系统构成简易经济,使金相照相更经济便捷,照片质量更好,同时使金相照片更便于进行交流。     

Multidirectional focus measure for accurate three-dimensional shape recovery of microscopic objects

Shape from focus (SFF) is a technique to recover the shape of an object from multiple images taken at various focus settings. Most of conventional SFF techniques compute focus value of a pixel by applying one of focus measure operators on neighboring pixels on the same image frame. However, in the optics with limited depth of field, neighboring pixels of an image have different degree of focus for curved objects, thus the computed focus value does not reflect the accurate focus level of the pixel. Ideally, an accurate focus value of a pixel needs to be measured from the neighboring pixels lying on tangential plane of the pixel in image space. In this article, a tangential plane on each pixel location (i, j) in image sensor is searched by selecting one of five candidate planes based on the assumption that the maximum variance of focus values along the optical axis is achieved from the neighborhood lying on tangential plane of the pixel (i, j). Then, a focus measure operator is applied on neighboring pixels lying on the searched plane. The experimental results on both the synthetic and real microscopic objects show the proposed method produces more accurate three-dimensional shape in comparison to conventional SFF method that applies focus measures on original image planes.     

MRT Letter: Guided filtering of image focus volume for 3D shape recovery of microscopic objects

In this letter, a shape from focus (SFF) method is proposed that utilizes the guided image filtering to enhance the image focus volume efficiently. First, image focus volume is computed using a conventional focus measure. Then each layer of image focus volume is filtered using guided filtering. In this work, the all‐in‐focus image, which can be obtained from the initial focus volume, is used as guidance image. Finally, improved depth map is obtained from the filtered image focus volume by maximizing the focus measure along the optical axis. The proposed SFF method is efficient and provides better depth maps. The improved performance is highlighted by conducting several experiments using image sequences of simulated and real microscopic objects. The comparative analysis demonstrates the effectiveness of the proposed SFF method. Microsc. Res. Tech. 77:959–963, 2014. © 2014 Wiley Periodicals, Inc.     

MRT Letter: Optimal composite depth function for 3D shape recovery of microscopic objects

Generally, in shape from focus techniques, a single focus measure is used in estimating the three‐dimensional structure of microscopic objects. However, the performance of a single focus measure is limited to estimate accurately the depth map of diverse type of objects. To cope with this problem, we propose genetic programming based novel approach by developing an optimal composite depth (OCD) function for accurate depth estimation. This OCD function optimally combines the initial depth and focus information extracted from individual focus measures. An improved performance of this function is reported for synthetic and real world microscopic objects. Microsc. Res. Tech., 2010. © 2010 Wiley‐Liss, Inc.     

MRT letter: Recovering weak‐textured surfaces using image focus

In nature, objects have partially weak texture and their shape reconstruction using focus based passive methods like shape from focus (SFF), is difficult. This article presents a new SFF algorithm which can compute precise depth of dense as well as weak textured objects. Segmentation is applied to discard wrong depth estimate and then later interpolating them from accurate depth values of their neighbors. The performance of the proposed method is tested, using different image sequences of synthetic and real objects, with varying textures. Microsc. Res. Tech., 2009. © 2009 Wiley‐Liss, Inc.     

PCA-based method for 3D shape recovery of microscopic objects from image focus using discrete cosine transform

This article introduces a new algorithm for shape from focus (SFF) based on discrete cosine transform (DCT) and principal component analysis (PCA). DCT is applied on a small 3D neighborhood for each pixel in the image volume. Instead of summing all focus values in a window, AC parts of DCT are collected and then PCA is applied to transform this data into eigenspace. The first feature, containing maximum variation is employed to compute the depth. DCT and PCA are computationally intensive; however, the reduced data elements and algorithm iterations have made the new approach competitive and efficient. The performance of the proposed approach is compared with other methods by conducting experiments using image sequences of a synthetic and two microscopic objects. The evaluation is gauged on the basis of unimodality, monotonicity, and resolution of the focus curve. Two other global statistical metrics, root mean square error (RMSE) and correlation have also been applied for synthetic image sequence. Besides, noise sensitivity and computational complexity are also compared with other algorithms. Experimental results demonstrate the effectiveness and the robustness of the new method.     

Searching surface orientation of microscopic objects for accurate 3D shape recovery

In this article, we propose a new shape from focus (SFF) method to estimate 3D shape of microscopic objects using surface orientation cue of each object patch. Most of the SFF algorithms compute the focus value of a pixel from the information of neighboring pixels lying on the same image frame based on an assumption that the small object patch corresponding to the small neighborhood of a pixel is a plane parallel to the focal plane. However, this assumption fails in the optics with limited depth of field where the neighboring pixels of an image have different degree of focus. To overcome this problem, we try to search the surface orientation of the small object patch corresponding to each pixel in the image sequence. Searching of the surface orientation is done indirectly by principal component analysis. Then, the focus value of each pixel is computed from the neighboring pixels lying on the surface perpendicular to the corresponding surface orientation. Experimental results on synthetic and real microscopic objects show that the proposed method produces more accurate 3D shape in comparison to the existing techniques.     

MRT letter: A total variation based method for 3D shape recovery of microscopic objects through image focus

Generally, shape from focus methods use a single focus measure to compute focus quality and to obtain an initial depth map of an object. However, different focus measures perform differently in diverse conditions. Therefore, it is hard to get accurate 3D shape based on a single focus measure. In this article, we propose a total variation based method for recovering 3D shape of an object by combining multiple depth hypothesis obtained through different focus measures. Improved performance of the proposed method is evaluated by conducting several experiments using images of synthetic and real microscopic objects. Comparative analysis demonstrates the effectiveness of the proposed approach. Microsc. Res. Tech. 76:877–881, 2013. © 2013 Wiley Periodicals, Inc.     

用于细胞三维结构观察的现代显微影像仪器及相关技术

细胞三维结构的观察分析能提供更多细胞显微水平结构和功能的信息。现代显微影像仪器及相关的三维重建等技术是研究细胞三维结构的有力工具。本文概述多种可用于细胞三维结构观察的显微工具,简单介绍其原理、优势、最新技术动态及应用领域等。