提出了一种非线性相位误差的全场补偿方法,通过对参考面相位的多次测量,提高了参考面的解相精度,提取出一个逼近理想值的期望相位面,并用该期望相位面检测非线性相位误差。重构待测物体时,可直接用物体展开相位值在查找表中查找对应的非线性相位误差,并以此对物体的展开相位进行全场补偿。采用所提方法重构已知具体高度的平面,平均绝对误差的最大值从0.48 mm减小到0.06 mm,方均根误差的最大值从0.55 mm减小到0.07 mm。

提出了一种基于不等占空比二元光栅的相位测量轮廓术(PMP)。所用的光栅只有0和255两个灰度,消除了投影仪的伽玛非线性对正弦光栅灰度的影响,同时可将投影系统的刷新频率提高一个数量级。所提方法的测量精度高于罗奇光栅离焦投影傅里叶变换轮廓术的,重复精度高于传统正弦光栅PMP。

像素匹配是在线三维测量中的关键技术之一, 提出了基于归一化等相面的在线三维测量像素匹配方法。仅投一帧正弦光栅条纹到在线运动的物体上, 由CCD再依次移动相同步距时刻同步采集受物体调制的变形条纹图。利用FTP方法预测物体不同位置的相位信息并进行归一化, 再以二值化等相面协助像素匹配, 不仅实现了物体在各帧条纹图中的像素坐标一一对应, 而且归一化减少了由于物体运动产生的不同位置相位展开起始点不一致导致的不同位置相位展开的差异性而引入的误差, 同时节省了像素匹配计算量。对最大高度为8 mm的peaks函数型物体的模拟结果表明: 均方差为0.021 mm, 像素匹配时间上, 该方法较直接用FTP方法预测得到的相位为模板进行像素匹配缩短了近2倍, 同是实物测量也验证了该方法的有效性和可行性。所提方法不仅可以保证在线三维测量的精度, 而且有效地提高了测量速度。

A leukocyte image fast scanning method based on max-min distance clustering is proposed. Because of the lower proportion and uneven distribution of leukocytes in human peripheral blood, there will not be any leukocyte in lager quantity of the captured images if we directly scan the blood smear along an ordinary zigzag scanning routine with high power (100^x) objective. Due to the larger field of view of low power (10^x) objective, the captured low power blood smear images can be used to locate leukocytes. All of the located positions make up a specific routine, if we scan the blood smear along this routine with high power objective, there will be definitely leukocytes in almost all of the captured images. Considering the number of captured images is still large and some leukocytes may be redundantly captured twice or more, a leukocyte clustering method based on max–min distance clustering is developed to reduce the total number of captured images as well as the number of redundantly captured leukocytes. This method can improve the scanning efficiency obviously. The experimental results show that the proposed method can shorten scanning time from 8.0–14.0 min to 2.5–4.0 min while extracting 110 nonredundant individual high power leukocyte images.

Red blood cell (RBC) counting is a standard medical test that can help diagnose various conditions and diseases. Manual counting of blood cells is highly tedious and time consuming. However, new methods for counting blood cells are customary employing both electronic and computer-assisted techniques. Image segmentation is a classical task in most image processing applications which can be used to count blood cells in a microscopic image. In this research work, an approach for erythrocytes counting is proposed. We employed a classification before counting and a new segmentation idea was implemented on the complex overlapping clusters in a microscopic smear image. Experimental results show that the proposed method is of higher counting accuracy and it performs much better than most counting algorithms existed in the situation of three or more RBCs overlapping complexly into a group. The average total erythrocytes counting accuracy of the proposed method reaches 92.9%.