Three-dimensional structured light measurement of highly reflective surface is a challenge faced in industrial metrology. The high dynamic range (HDR) technique provides a solution by fusing images under multiple exposures; however, the process is highly time-consuming. This paper reports a new SL-based method to measure parts with highly reflective surfaces from only a single exposure. A new quantitative metric was defined to optimally select camera exposure for capturing input single-exposure images. Different from existing image gradient or entropy-based metrics, the new metric incorporates both intensity modulation and overexposure. A skip pyramid context aggregation network (SP-CAN) was proposed to enhance the single exposure-captured images. Compared with existing image enhancement methods, SP-CAN effectively preserves detailed encoded phase information near edges and corners during enhancement. Experiments with various industrial parts demonstrate that the average time cost of the proposed method was 0.6 second, which was only one tenth of the HDR method (ten exposures), and the two methods achieved similar coverage rates (97.6% vs. 98.0%) and measurement accuracy (0.040 mm vs. 0.038 mm).

The particle matter inspection for pharmaceutical injection is inevitable in the field of pharmaceutical manufacturing, as it has the direct impact on the quality of the drugs. It is a challenge to inspect the contaminated injection online using an imaging system. This paper introduces a novel and effective inspection machine consisting of three modules, a mechanical system with 120 carousel grips, an image acquisition system with multihigh resolution cameras and a multilight sources station, and a distributed industrial electrical computer control system. Particle visual inspection machine first acquires image sequence using the high-speed image acquisition system. The image capture process at each camera module is alternately synchronized with different LED illumination techniques (light transmission method and light reflection method), enabling independent capture of particle images from the same container. Then, a set of novel algorithms for image registration and fast segmentation are proposed to minimize false rejections even in sensitive conditions, which enable the identification of all the tiny potential defects. Finally, a particle tracking and classification algorithm based on an adaptive local weighted-collaborative sparse model is also presented. The experiments demonstrate that the proposed inspection system can effectively detect the particles in the pharmaceutical infusion solution online, and achieve a performance rate of above 97% average accuracy.

This article proposes a new eye-in-hand 3-D scanner-robot calibration approach to realize low data stitching errors during long-term continuous measurement. Eye-in-hand 3-D scanner–robot systems are commonly used for the complete measurement of an object under test (OUT) from multiple fields of view (FOVs). To align the multiple FOVs into a single coordinate system, marker-free stitching assisted by robot's positioning is attractive since it bypasses the cumbersome traditional fiducial marker-based method. Based on periodically capturing calibration images from a 2-D calibration target, scanner's and robot kinematic model's parameters are optimized. The challenges overcome in this article include: how to compensate for the center-detection error in scanner calibration; how to avoid the dependence of hand-eye calibration on DH parameters; and how to calculate an accurate world-to-robot transformation. These challenges were tackled by several new techniques including accurate scanner calibration with iterative refinement of control points, virtual arm-based scanner-robot kinematic modeling, and trajectory-based world-to-robot transformation calculation. Experimental results demonstrated a low-initial stitching error similar to the fiducial marker-based method (0.0446 mm versus 0.0542 mm) was achieved. The mean stitching error was effectively maintained to be $< $ 0.1 mm during the continuous measurement with an average intermittent downtime of 78 s for recalibration.

The Kleihauer–Betke (KB) test is the standard method for quantitating fetal-maternal hemorrhage in maternal care. In hospitals, the KB test is performed by a certified technologist to count a minimum of 2000 fetal and maternal red blood cells (RBCs) on a blood smear. Manual counting suffers from inherent inconsistency and unreliability. This paper describes a system for automated counting and distinguishing fetal and maternal RBCs on clinical KB slides. A custom-adapted hardware platform is used for KB slide scanning and image capturing. Spatial-color pixel classification with spectral clustering is proposed to separate overlapping cells. Optimal clustering number and total cell number are obtained through maximizing cluster validity index. To accurately identify fetal RBCs from maternal RBCs, multiple features including cell size, roundness, gradient, and saturation difference between cell and whole slide are used in supervised learning to generate feature vectors, to tackle cell color, shape, and contrast variations across clinical KB slides. The results show that the automated system is capable of completing the counting of over 60 000 cells (versus $\sim$ 2000 by technologists) within 5 min (versus $\sim$ 15 min by technologists). The throughput is improved by approximately 90 times compared to manual reading by technologists. The counting results are highly accurate and correlate strongly with those from benchmarking flow cytometry measurement.

Sickle cell trait (SCT) is a condition in which an individual inherits one sickle hemoglobin gene (HbS) and one normal beta hemoglobin gene (HbA). It has been hypothesized that under extreme physical stress, the compromised mechanical properties of the red blood cells (RBCs) may be the underlying mechanism of clinical complications of sickle cell trait individuals. However, whether sickle cell trait (SCT) should be treated as physiologically normal remains controversial. In this work, the mechanical properties (i.e., shear modulus and viscosity) of individual RBCs were quantified using a microsystem capable of precisely controlling the oxygen level of RBCs' microenvironment. Individual RBCs were deformed under shear stress. After the release of shear stress, the dynamic cell recovery process was captured and analyzed to extract the mechanical properties of single RBCs. The results demonstrate that RBCs from sickle cell trait individuals are inherently stiffer and more viscous than normal RBCs from healthy donors, but oxygen level variations do not alter their mechanical properties or morphology.

Nanomanipulation under scanning electron microscopy (SEM) enables direct interactions of a tool with a sample. We recently developed a nanomanipulation technique for the extraction and identification of DNA contained within sub-nuclear locations of a single cell nucleus. In nanomanipulation of sub-cellular structures, a key step is to identify targets of interest through correlating fluorescence and SEM images. The DNA extraction task must be conducted with low accelerating voltages resulting in low imaging resolutions. This is imposed by the necessity of preserving the biochemical integrity of the sample. Such poor imaging conditions make the identification of nanometer-sized fiducial marks difficult. This paper presents an affine scale-invariant feature transform (ASIFT) based method for correlating SEM images and fluorescence microscopy images. The performance of the image correlation approach under different noise levels and imaging magnifications was quantitatively evaluated. The optimal mean absolute error (MAE) of correlation results is 68±34 nm under standard conditions. Compared with manual correlation by skilled operators, the automated correlation approach demonstrates a speed that is higher by an order of magnitude. With the SEM-fluorescence image correlation approach, targeted DNA was successfully extracted via nanomanipulation under SEM conditions.