Effect of grape seed proanthocyanidins on activity of HaCaT cells in mice based on deep learning image processing

2020 ◽  
pp. 1-11
Author(s):  
Feng Xu ◽  
Jia Huang
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Grape Seed ◽  
Hacat Cells ◽  
Image Processing Algorithm ◽  
Grape Seed Proanthocyanidins ◽  
Uva Irradiation ◽  
Deep Learning Algorithm ◽  
Comparison Results

BACKGROUND: Grape seed proanthocyanidin extract (GSPE) has a certain resistance to contrast light, which makes the boundary of the imaging image unclear. OBJECTIVE: Because of this, an image processing algorithm is needed to process the contrast image to study the role of GSPE in the process of anti-ultraviolet. METHODS: In this paper, the fuzzy edges of contrast images were processed by deep learning algorithm, and the changes of VEGF and PEDF expression in HaCaT cells before and after UVA irradiation and after GSPE intervention were studied. RESULTS: The experiment results show that after processing, the edge and boundary of the image become clear and separable, which can be used to compare and analyze the test process. The image comparison results show that GSPE can down regulate the expression of VEGF gene and protein, and up regulate the expression of PEDF gene and protein. The synergistic effect of GSPE and GSPE can inhibit angiogenesis. It is confirmed that GSPE has the effect of anti-ultraviolet ray induced early angiogenesis.

Using geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs

GEOMATICA ◽  
2021 ◽  
pp. 1-23
Author(s):  
Roholah Yazdan ◽  
Masood Varshosaz ◽  
Saied Pirasteh ◽  
Fabio Remondino
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Automatic Segmentation ◽  
Geometric Constraints ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Set ◽  
Traffic Signs ◽  
Deep Learning Algorithm ◽  
Comparison Results

Automatic detection and recognition of traffic signs from images is an important topic in many applications. At first, we segmented the images using a classification algorithm to delineate the areas where the signs are more likely to be found. In this regard, shadows, objects having similar colours, and extreme illumination changes can significantly affect the segmentation results. We propose a new shape-based algorithm to improve the accuracy of the segmentation. The algorithm works by incorporating the sign geometry to filter out the wrong pixels from the classification results. We performed several tests to compare the performance of our algorithm against those obtained by popular techniques such as Support Vector Machine (SVM), K-Means, and K-Nearest Neighbours. In these tests, to overcome the unwanted illumination effects, the images are transformed into colour spaces Hue, Saturation, and Intensity, YUV, normalized red green blue, and Gaussian. Among the traditional techniques used in this study, the best results were obtained with SVM applied to the images transformed into the Gaussian colour space. The comparison results also suggested that by adding the geometric constraints proposed in this study, the quality of sign image segmentation is improved by 10%–25%. We also comparted the SVM classifier enhanced by incorporating the geometry of signs with a U-Shaped deep learning algorithm. Results suggested the performance of both techniques is very close. Perhaps the deep learning results could be improved if a more comprehensive data set is provided.

scholarly journals A Four Stage Image Processing Algorithm for Detecting and Counting of Bagworm, Metisa plana Walker (Lepidoptera: Psychidae)

Agriculture ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1265
Author(s):  
Mohd Najib Ahmad ◽  
Abdul Rashid Mohamed Shariff ◽  
Ishak Aris ◽  
Izhal Abdul Halin
Keyword(s):  
Image Processing ◽  
Oil Palm ◽  
Learning Algorithm ◽  
Insect Pest ◽  
Processing Algorithm ◽  
Training Dataset ◽  
Detection Accuracy ◽  
Color Analysis ◽  
Image Processing Algorithm ◽  
Deep Learning Algorithm

The bagworm is a vicious leaf eating insect pest that threatens the oil palm plantations in Malaysia. The economic impact from defoliation of approximately 10% to 13% due to bagworm attack might cause about 33% to 40% yield loss over 2 years. Due to this, monitoring and detecting of bagworm populations in oil palm plantations is required as the preliminary steps to ensure proper planning of control actions in these areas. Hence, the development of an image processing algorithm for detection and counting of Metisa plana Walker, a species of Malaysia’s local bagworm, using image segmentation has been researched and completed. The color and shape features from the segmented images for real time object detection showed an average detection accuracy of 40% and 34%, at 30 cm and 50 cm camera distance, respectively. After some improvements on training dataset and marking detected bagworm with bounding box, a deep learning algorithm with Faster Regional Convolutional Neural Network (Faster R-CNN) algorithm was applied leading to the percentage of the detection accuracy increased up to 100% at a camera distance of 30 cm in close conditions. The proposed solution is also designed to distinguish between the living and dead larvae of the bagworms using motion detection which resulted in approximately 73–100% accuracy at a camera distance of 30 cm in the close conditions. Through false color analysis, distinct differences in the pixel count based on the slope was observed for dead and live pupae at 630 nm and 940 nm, with the slopes recorded at 0.38 and 0.28, respectively. The higher pixel count and slope correlated with the dead pupae while the lower pixel count and slope, represented the living pupae.

scholarly journals Study on a Risk Model and Target Detection for Prediction and Avoidance of Unmanned Environmental Hazard

2021 ◽  
Author(s):  
Chengqun Qiu ◽  
Yuan Zhong ◽  
Jie Ji ◽  
Shuai Zhang ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Prediction ◽  
Target Detection ◽  
Environmental Risk ◽  
Risk Model ◽  
Learning Algorithm ◽  
Unmanned Vehicles ◽  
Deep Learning Algorithm ◽  
Detection Technology ◽  
Comparison Results

Abstract Comprehensive research is conducted on the design and control of the unmanned systems for electric vehicles. The environmental risk prediction and avoidance system is divided into the prediction part and the avoidance part. The prediction part is divided into environmental perception, environmental risk assessment, and risk prediction. In the avoidance part, the conservative driving strategy based on speed restriction is adopted according to the results of risk prediction. Additionally, the core function is achieved through the target detection technology based on deep learning algorithm and the data conclusion based on deep learning method. Moreover, the location of bounding box is further optimized to improve the accuracy of SSD target detection method based on solving the problem of unbalanced sample categories. Software such as MATLAB and Carsim are applied in the system. From the comparison results of the simulations of unmanned vehicles with or without a system, it that the system can provide effective safety guarantee for unmanned driving.

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