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Purpose - Film is an important recording medium in industry. How to ensure film quality is a problem, because the film is produced in a dark room. Aims to focus on this. Design/methodology/approach - An inspection system based on machine vision is presented which could assure illumination and imaging at 940?nm which is safe for film. Findings - An objective evaluation is given using an image processing method and satisfactory results are obtained. Now, the inspection system is used by the biggest film company in China. Originality/value - This work focuses on an important issue in film production. [ABSTRACT FROM AUTHOR]

The development of computer tomography and image processing has made it possible to establish virtual 3D reconstruction and non-invasive dissection of fossil specimens. We used these methods to reconstruct a virtual 3D skull of a Pleistocene cheetah skull from the Tuozi cave, Tangshan, Nanjing, and virtually dissected it for anatomic studies, and measured the volumes of different parts of the endocranium. The endocranium of the cheetah skull has showed that its frontal sinus is beehive-like, the frontal lobe of cerebra is relatively large but the temporal lobe is relatively small, its cerebral sulcus and gyrus are more complicated than those of the domestic cat, similar to those of the domestic dog, but simpler than those of giant panda, pig, cattle and horse. The technology of virtual 3D reconstruction and non-invasive dissection of fossil specimens can extend the morphological study from the exterior to the interior, and it can also help to study fragile specimens and virtually backup rare and precious specimens. [ABSTRACT FROM AUTHOR]

China frequently experiences natural disasters, of which flooding is the most serious. How to monitor and control natural disasters, assess damage, and provide relief is the most urgent problem for the Chinese government and disaster experts. A national integrated system using remote sensing, geographic information systems, the Global Positioning System, and other technology for monitoring and evaluating flood disasters has been assembled and tried out for 3 years. The system has played an important role in flood mitigation during the trial and has become a key part of the flood management system at China''s National Flood Control Headquarters. This paper presents an overview of the system and its use in China. [Copyright &y& Elsevier]

A hyperspectral image processing and analysis system (HIPAS) has been developed by the Institute of Remote Sensing Applications of the Chinese Academy of Sciences. The HIPAS, built on Interactive Data Language (IDL) and implemented on Windows NT workstations, meets the requirements for the rapid preprocessing of imaging spectrometer data and easy prototyping of algorithms. Integrated with a spectral library, which was implemented on the FoxPro, a popular database environment in the Windows NT platform, the spectral analysis model was established to support hyperspectral image analyses. Based on the HIPAS, some hyperspectraI remote sensing application studies were completed in China. These included mineral identification, agriculture investigation, urban mapping, and the study of wetland vegetation. [ABSTRACT FROM AUTHOR]

• An automatic winter-jujube grading robot based on machine vision was designed. • A method combining YOLOv3 algorithm and hand-engineered features was developed to calculate the maturity of winter-jujube. • This method can detect overlapping winter-jujubes accurately and has a high stability under different lighting conditions. • The grading device has high ability to sort winter-jujube into three maturity categories. Winter-jujube is a kind of fresh fruit in China. After harvest, winter-jujubes require to grade into different categories according to their maturity levels. Mature winter-jujube can be recognized by their red colour. In this study, a winter-jujube grading robot is designed. Moreover, a method combining YOLOv3 algorithm and hand-engineered features is developed to calculate the maturity of winter-jujube. The grading robot is composed of a transmission unit, an image acquisition unit, and an actuator unit. Based on YOLOv3 algorithm, a detection model is trained, and compared with SSD and Faster R-CNN algorithms. When the IoU are 0.7, 0.8, and 0.9, the F1 scores of the model are 100%, 100%, and 93.66%, respectively. The mAP (IoU = 0.50:0.05:0.95) of the model is 94.78%, and the detection time of single image is 0.042 s. The detection model exhibits a high stability under different lighting conditions. In addition, overlapping winter-jujubes in the image can be detected accurately. After image distortion correction and object detection, an image processing flow for spatial positioning, size measurement and maturity calculation for winter-jujubes is designed. Finally, a real-time grading device for winter-jujube is built to perform grading experiments. The maturity grading accuracy is 97.28%, and the average grading time of each winter-jujube is 1.39 s. [ABSTRACT FROM AUTHOR]

The pharmacological effects of Angelicae Sinensis Radix from different producing areas are uneven. Accurate identification of its producing areas by computer vision and machine learning(CVML) is conducive to evaluating the quality of Angelicae Sinensis Radix. This paper collected the high-definition images of Angelicae Sinensis Radix from different producing areas using a digital camera to construct an image database, followed by the extraction of texture features based on the grayscale relationship of adjacent pixels in the image. Then a support vector machine(SVM)-based prediction model for predicting the producing areas of Angelicae Sinensis Radix was built. The experimental results showed that the prediction accuracy reached up to 98.49% under the conditions of the model training set occupying 80%, the test set occupying 20%, and the sampling radius(r) of adjacent pixels being 2. When the training set was set to 10%, the prediction accuracy was still over 93%. Among the three producing areas of Angelicae Sinensis Radix, Huzhu county, Qinghai province exhibited the highest error rate, while Heqing county, Yunnan province the lowest error rate. Angelicae Sinensis Radix from Minxian county, Gansu province and Huzhu county, Qinghai province were both wrongly attributed to Heqing county, Yunnan province, while most of those from Huzhu county, Qinghai province were misjudged as the samples produced in Minxian county, Gansu province. The method designed in this paper enabled the rapid and non-destructive prediction of the producing areas of Angelicae Sinensis Radix, boasting high accuracy and strong stability. There were definite morphological differences between Angelicae Sinensis Radix samples from Minxian county, Gansu province and those from Huzhu county, Qinghai province. The wrongly predicted samples from Minxian county, Gansu province and Huzhu city, Qinghai province shared similar morphological characteristics with those from Heqing county, Yunnan province. Most wrongly predicted samples from Heqing county, Yunnan province were similar to the ones from Minxian county, Gansu province in morphological characteristics.

• An automated Kashin Beck Disease (KBD) diagnosis algorithm was proposed based on deep learning. • The method focuses on multi-features fusion for detection. The features are extracted from hand X-ray images by DCNN, including global and local features. • Global features represent structures of hand bones, and local features represent subtle edge information from critical regions of metaphysis based on the domain knowledge of KBD. • The model can effectively diagnose KBD and provide substantial benefits to reduce largescale screening costs and missed diagnosis rate. Kashin-Beck Disease (KBD) is a serious endemic bone disease leading to short stature. The early radiological examinations are crucial for potential patients. However, many children in rural China cannot be diagnosed in time due to the shortage of professional orthopedists. In this paper, an algorithm is developed to automatically screening KBD based on hand X-ray images of subjects, which can help the government reducing human resources investment and assisting the poor precisely. The KBD diagnosis method focuses on multi-feature fusion for classification. Two kinds of features presented in X-ray images are extracted by a deep convolutional neural network (DCNN). One is the global features that represent shapes and structures of the whole hand bone. The other is local features that represent edge and texture information from critical regions of the metaphysis. The global features tend to sketch the major informative parts, whereas other fine local features can provide supplementary information. Then both kinds of features are combined and fed into the KBD classifier of a fully connected neural network (FCNN) to obtain diagnostic results. Our research team collected 960 samples in KBD endemic areas of Tibet from 2017 to 2018. The dataset contains 219 KBD positive images and 741 negative images. Experiments indicate that the method based on multi-feature achieves the best average accuracy and sensitivity rate of of 98.5% and 97.6% for diagnosis, which is 4.0% and 7.6% higher than the method with only the global features respectively. The KBD diagnosis method shows that our proposed multi-feature fusion helps to achieve higher diagnosis performance and stability compared with only using global features for detection. The automated KBD diagnosis algorithm provides substantial benefits to reduce large-scale screening costs and missed diagnosis rate. [ABSTRACT FROM AUTHOR]

• An arc approximation algorithm for cucumber curvature is proposed. • The center line of the cucumber coincides with the approximated arc curve. • The new algorithm has higher classification advantages than elliptic approximation. The cucumber is one of the most important consumer vegetables, and due to high freshness and appearance standards, it must be graded according to quality. One key metric for grading cucumbers is curvature (arch height) relative to length. To date, this classification standard has not been implemented in a commercial automatic system because the curvature of the cucumber is related to its length. This paper presents and tests a circular arc approximation algorithm for measuring the curvature of a cucumber using image processing. By fitting the central axis of each cucumber to establish a curve equation, the approximate eigenvalue radius, R, and the radius of the section perpendicular to the curvature of cucumber, r, could be obtained quickly and efficiently. The curve of the cucumber was then transformed into a new characteristic, R' (R' = R – r), which was used as a threshold to classify each cucumber. The method was verified by theory and experiment, and the average error of 0.71 % corresponding to the coefficient R2 = 0.9958 of the similarity index between the curve and central axis of the cucumber was verified. It was found that the fitted curve and the central axis of the cucumber were coincident. Moreover, we designed an algorithm that can detect S-shaped cucumbers based on the analysis of the contour, which can avoid the influence of S-shaped cucumbers on the results. In our tests, 148 cucumbers were classified using the circular arc approximation method, the elliptic approximation method, and a manual method. The results of the classifications were compared. According to China's standard evaluation, the classification error rate of the circular arc approximation method was 10.1 % lower than that of the elliptic approximation method. The incidence of cucumbers being classified into an adjacent grade was 0.7 % using the circular approximation method, and there were no cases of cucumbers being classified into non-adjacent grades. The classification error rate of the elliptic approximation was 10.8 %, of which 2.7 % was classified into non-adjacent grades. Under the European standard, the cucumber classification accuracy using the circular arc approximation algorithm reached 100 %. Therefore, the proposed method offers a more accurate classifier than the elliptic approximation method, and the circular arc approximation algorithm can be fully applied in the commercial cucumber classification process. [ABSTRACT FROM AUTHOR]

Open surface freshwater is an important resource for terrestrial ecosystems. However, climate change, seasonal precipitation cycling, and anthropogenic activities add high variability to its availability. Thus, timely and accurate mapping of open surface water is necessary. In this study, a methodology based on the concept of spatial autocorrelation was developed for automatic water extraction from Landsat series images using Taihu Lake in south-eastern China as an example. The results show that this method has great potential to extract continuous open surface water automatically, even when the water surface is covered by floating vegetation or algal blooms. The results also indicate that the second shortwave-infrared band (SWIR2) band performs best for water extraction when water is turbid or covered by surficial vegetation. Near-infrared band (NIR), first shortwave-infrared band (SWIR1), and SWIR2 have consistent extraction success when the water surface is not covered by vegetation. Low filter image processing greatly overestimated extracted water bodies, and cloud and image salt and pepper issues have a large impact on water extraction using the methods developed in this study. [ABSTRACT FROM AUTHOR]

Wang, H. and Zhao, J., 2020. The research in digital slope information technology: Evidences from coastal region of China. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 798–801. Coconut Creek (Florida), ISSN 0749-0208. The continuous development of Internet and big database accelerate the data infiltration of constructions in various aspects. Three-dimensional Slope Information System which is based on web has auxiliary decisive significance on both planning slope engineering and constructions. Based on the two modules which are three-dimensional visualization and image processing in digital video surveillance, a slope information system is developed. The main conclusions are shown as follows: Based on UAV photogrammetry technology and the application of both BIM and GIS, three-dimensional visualization and its application can be realized economically. Based on advanced development of PFC3D, digital elevation model MySQL database and BP neural network, the rapid modeling and analysis of complex slope's numerical simulation are realized. Based on video image processing technology, the interaction between landslide movement and three-dimensional visualization are realized. [ABSTRACT FROM AUTHOR]

Image classification is a fundamental task in remote sensing image processing. In recent years, deep convolutional neural networks (DCNNs) have experienced significant breakthroughs in natural image recognition. The remote sensing field, however, is still lacking a large-scale benchmark similar to ImageNet. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsourced data. Using crowdsourced data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively using points of interest, vector data from OSM, or other crowdsourced data. These annotated images can, then, be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has large-scale geographical distribution and large total image number. It contains six categories with 35 sub-classes of more than 24,000 images of size 256 × 256 pixels. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct numerous experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced data sets. The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification tasks than other benchmarks in the big data era and has many potential applications. [ABSTRACT FROM AUTHOR]

As cyanobacteria blooms occur in many types of inland water, routine monitoring that is fast and accurate is important for environment and drinking water protection. Compared to field investigations, satellite remote sensing is an efficient and effective method for monitoring cyanobacteria blooms. However, conventional remote sensing monitoring methods are labor intensive and time consuming, especially when processing long-term images. In this study, we embedded related processing procedures in Google Earth Engine, developed an operational cyanobacteria bloom monitoring workflow. Using this workflow, we measured the spatiotemporal patterns of cyanobacteria blooms in China's Taihu Lake from 2000 to 2018. The results show that cyanobacteria bloom patterns in Taihu Lake have significant spatial and temporal differentiation: the interannual coverage of cyanobacteria blooms had two peaks, and the condition was moderate before 2006, peaked in 2007, declined rapidly after 2008, remained moderate and stable until 2015, and then reached another peak around 2017; bays and northwest lake areas had heavier cyanobacteria blooms than open lake areas; most cyanobacteria blooms primarily occurred in April, worsened in July and August, then improved after October. Our analysis of the relationship between cyanobacteria bloom characteristics and environmental driving factors indicates that: from both monthly and interannual perspectives, meteorological factors are positively correlated with cyanobacteria bloom characteristics, but as for nutrient loadings, they are only positively correlated with cyanobacteria bloom characteristics from an interannual perspective. We believe reducing total phosphorous, together with restoring macrophyte ecosystem, would be the necessary long-term management strategies for Taihu Lake. Our workflow provides an automatic and rapid approach for the long-term monitoring of cyanobacteria blooms, which can improve the automation and efficiency of routine environmental management of Taihu Lake and may be applied to other similar inland waters. [ABSTRACT FROM AUTHOR]

Improving health-related physical fitness plays an important role in enhancing the comprehensive physical fitness of college students. The traditional test of health-related physical fitness is characterized by complicated operations and low efficiency. A set of test methods for health-related physical fitness of college students based on an intelligent mobile terminal is designed in this work. The intelligent test method calculates the health-related physical fitness level of users through image acquisition and analysis by combining intelligent terminal software and test items of key elements. To verify the validity of the proposed method, a total of 116 college students (59 males and 57 females) aged between 18 and 22 are chosen as test respondents. The health-related physical fitness of respondents is tested and compared by using the traditional and proposed methods. The traditional test method reports the cardiorespiratory fitness scores of 69.6 ± 9.5 (M) and 77.1 ± 9.9 (F), and the proposed method reports 70.9 ± 9.7 (M) and 77.7 ± 9.8 (F). Twenty samples are chosen randomly to calculate the correlation coefficients r = 0.944 (M) and 0.965 (F) and significance p = 0.145 (M) and 0.489 (F). The flexibility fitness scores in the traditional method are 74.6 ± 11.9 (M) and 73.3 ± 11.5 (F), and the scores of the proposed method are 74.8 ± 11.5 (M) and 75.0 ± 11.2 (F). Twenty samples are chosen randomly to calculate r = 0.944 (M) and 0.938 (F) and p = 0.941 (M) and 0.098 (F). In the proposed method, muscle strength/muscular endurance fitness and a body composition module are tested by the traditional method, and test data are input into an artificial input system. The experimental results verify the accuracy of the proposed method in evaluating the health-related physical fitness of college students. Application of the proposed method can effectively reduce the cost of physical fitness testing, increase the convenience of testing, and direct substantial attention to the health-related physical fitness of college students.

China and Korea have introduced biometrics at the border for 2012. According to local reports, all foreigners will have their fingerprints scanned and their photo taken when they enter Korea from 1 January. Previously, biometric scanning was only used for people planning longer stays. [ABSTRACT FROM AUTHOR]