Abstract-Tongue diagnosis is an important diagnostic method in traditional Chinese medicine (TCM). However, due to its qualitative, subjective and experience-based nature, traditional tongue diagnosis has a very limited application in clinical medicine. Moreover, traditional tongue diagnosis is always concerned with the identification of syndromes rather than with the connection between tongue abnormal appearances and diseases. This is not well understood in Western medicine, thus greatly obstruct its wider use in the world. In this paper, we present a novel computerized tongue inspection method aiming to address these problems. First, two kinds of quantitative features, chromatic and textural measures, are extracted from tongue images by using popular digital image processing techniques. Then, Bayesian networks are employed to model the relationship between these quantitative features and diseases. The effectiveness of the method is tested on a group of 455 patients affected by 13 common diseases as well as other 70 healthy volunteers, and the diagnostic results predicted by the previously trained Bayesian network classifiers are reported.

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During the production process of solar panels, it is inevitable to have some defects, such as cracks on the surface of solar panels due to extrusion or damage due to quality issues. This article improves the Serre standard model, which can simulate the ventral visual pathway with object recognition ability, based on the latest research progress and results of simulating biological visual mechanism models in computer vision, to improve the recognition effect of surface defects on solar panels. At the same time, a pre-processing scheme combining Gaussian Laplace operator operator and adaptive Wiener filter to remove noise spots is studied, and the local Gabor Binary Pattern Histogram Sequence (LGBPHS) features are obtained through pre-processing. The Percolation-Based image processing method for detecting obvious cracks was used to determine the location of the algorithm and the calculation results based on the improved standard model method. It mainly refers to the MAX value output by the C2 layer and the classification and identification results of whether there are cracks, and the crack location function is completed. The experimental results show that the proposed method has an accuracy rate of 98.86% in training and 98.64% in testing, and both the false detection rate and the missed detection rate do not exceed 1%. Therefore, the method proposed in the study has a high accuracy and can effectively identify the surface defects of solar panels.INDEX TERMS Image processing, solar panels, defect recognition, local binary mode.

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The complexity and challenging underwater environment leading to degradation in underwater image. Measuring the qualityof underwater image is a significant step for the subsequent image processing step. Existing Image Quality Assessment(IQA) methods do not fully consider the characteristics of degradation in underwater images, which limits their performance inunderwater image assessment. To address this problem, an Underwater IQA (UIQA) method based on color space multi-featurefusion is proposed to focus on underwater image. The proposed method converts underwater images from RGB color space toCIELab color space, which has a higher correlation to human subjective perception of underwater visual quality. The proposedmethod extract histogram features, morphological features, and moment statistics from luminance and color components anduse multi-feature fusion to better quantify the degradation in underwater image quality. After features extraction, support vectorregression(SVR) is employed to learn the relationship between fusion features and image quality scores, and gain the qualityprediction model. Experimental results on the SAUD dataset and UIED dataset show that our proposed method can performwell in underwater image quality assessment. The performance comparisons on LIVE dataset, TID2013 dataset and SIQADdataset demonstrate the applicability of the proposed method.