Quantitative phase imaging and measurement of surface topography and fluid dynamics for objects, especially for moving objects, is critical in various fields. Phase-shifting digital holography, as a highly accurate phase measurement technology applied for moving objects, is limited by some aspects, such as dynamic phase measurement, accuracy of phase shift and temporal phase sensitivity. In this study, we proposed a two-stage neural network (VY-Net) for one shot phase recovery. This Y-Net generates two holograms with specific phase shifts from a single-frame phase shifted hologram, then V-Net recovering the phase with the three holograms input. Simulation results prove that the proposed method can provide an alternative approach for systems of phase-shifting digital holography based on common-path configuration to realize rapid phase-shifted holograms acquisition and accurate phase measurement.
Automatic identification of clue cells in microscopic leucorrhea images provides important information for evaluating gynecological diseases. Traditional manual microscopic examination of Gram-stained vaginal smears is adopted by most hospitals for identifying clue cells; however, it is both complex and time-consuming. In order to solve these problems, an automatic identification of clue cells in microscopic leucorrhea images based on machine learning is proposed in this paper. First, the Otsu threshold method is used to segment regions of interest (ROI) in image preprocessing according to the morphological features of clue cells. Then, Gabor, HOG and GLCM texture features are extracted to describe irregular edges and rough surfaces of clue cells. Finally, a SVM classifier using a hybrid kernel function by linearly weighted RBF and polynomial kernels is trained to identify clue cells rapidly and conveniently. In experiments, the method using GLCM texture features and a hybrid kernel function of SVM achieved 94.64% accuracy and 94.92% recall rate, which was better than methods using Gabor or HOG texture features and a single kernel function of SVM.
Fecal microscopic examination is a routine examination item to determine whether the digestive system is normal by analyzing formed elements. Traditional method is that doctor uses microscope eyepiece to observe sample smears. The efficiency is low, and examination results depend on doctor's experience level. Therefore, intelligent identification of formed elements is the main development direction of current fully automated fecal instruments. Unlike blood or urine samples, human fecal samples contain a lot of impurities, and sample stratification phenomenon is serious. So image quality assessment methods are difficult to find the sharpest image, affecting effectiveness of intelligent identification algorithm. In this paper, the microscopic image autofocus technology for human fecal samples is studied and divided into two parts: location and photographing. In location process, we use SMD algorithm to determine sample photographing interval. In photographing process, microscope platform zigzagged move in the interval to obtain each view's successively image sequences of different focal lengths. In order to accurately find the sharpest image in image sequence, we compared the difference between human eyes with 31 types of no-reference image quality assessment methods based on entropy, gradient, color, edge, contrast, similarity, and transform domain. Finally an improved Local TV algorithm was chose. Experimental results show that the improved Local TV algorithm is insensitive to changes in sample concentration with good robustness, and the accuracy rate can reach 94.26%. Our experimental results have some reference value for other focusing problems of complex microscopic images.
Anisotropic conductive film (ACF) bonding is widely used in the liquid crystal display (LCD) industry. It implements circuit connection between screens and flexible printed circuits or integrated circuits. Conductive microspheres in ACF are key factors that influence LCD quality, because the conductive microspheres’ quantity and shape deformation rate affect the interconnection resistance. Although this issue has been studied extensively by prior work, quick and accurate methods to inspect the quality of ACF bonding are still missing in the actual production process. We propose a method to inspect ACF bonding effectively by using automated optical inspection. The method has three steps. The first step is that it acquires images of the detection zones using a differential interference contrast (DIC) imaging system. The second step is that it identifies the conductive microspheres and their shape deformation rate using quantitative analysis of the characteristics of the DIC images. The final step is that it inspects ACF bonding using a back propagation trained neural network. The result shows that the miss rate is lower than 0.1%, and the false inspection rate is lower than 0.05%.
Automatic identification of fungi in microscopic fecal images provides important information for evaluating digestive diseases. To date, disease diagnosis is primarily performed by manual techniques. However, the accuracy of this approach depends on the operator’s expertise and subjective factors. The proposed system automatically identifies fungi in microscopic fecal images that contain other cells and impurities under complex environments. We segment images twice to obtain the correct area of interest, and select ten features, including the circle number, concavity point, and other basic features, to filter fungi. An artificial neural network (ANN) system is used to identify the fungi. The first stage (ANN-1) processes features from five images in differing focal lengths; the second stage (ANN-2) identifies the fungi using the ANN-1 output values. Images in differing focal lengths can be used to improve the identification result. The system output accurately detects the image, whether or not it has fungi. If the image does have fungi, the system output counts the number of different fungi types.
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