scholarly journals Detection of Children’s Molars Based on Noise Filter

2022 ◽  
Author(s):  
Zhiwen Yan ◽  
Ying Chen ◽  
Jinlong Song ◽  
Jia Zhu ◽  
Jianbo Li
Keyword(s):  
Feature Extraction ◽  
Metric Learning ◽  
Noise Filtering ◽  
Clustering Method ◽  
Training Process ◽  
Fissure Sealant ◽  
Noise Filter ◽  
Training Samples ◽  
Pit And Fissure Sealant ◽  
Fissure Sealing

Abstract Pit and fissure sealant is for children aged seven to twelve years to prevent molars from becoming caries. In this paper, we propose a new detection framework to identify whether children need pit and fissure sealing. We divide the framework into two parts: molar detection and molar classification. According to the characteristics of teeth, we propose to use the clustering method to filter the bounding box in the object detection part. In the region divided by clustering, we only keep one detection frame in the same category. In the classification part, we propose a noise filtering layer based on wavelet transform for feature extraction. We map the training samples to another space in the training process based on metric learning to increase the distance between categories and improve the accuracy of classification.

scholarly journals Joint Learning of the Center Points and Deep Metrics for Land-Use Classification in Remote Sensing

2019 ◽  
Vol 11 (1) ◽  
pp. 76 ◽  
Author(s):  
Zhiqiang Gong ◽  
Ping Zhong ◽  
Weidong Hu ◽  
Yuming Hua
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Metric Learning ◽  
Land Use Classification ◽  
Training Process ◽  
Pairwise Correlation ◽  
Deep Model ◽  
Training Samples ◽  
Deep Metric Learning ◽  
Learned Features

Deep learning methods, especially convolutional neural networks (CNNs), have shown remarkable ability for remote sensing scene classification. However, the traditional training process of standard CNNs only takes the point-wise penalization of the training samples into consideration, which usually makes the learned CNNs sub-optimal especially for remote sensing scenes with large intra-class variance and low inter-class variance. To address this problem, deep metric learning, which incorporates the metric learning into the deep model, is used to maximize the inter-class variance and minimize the intra-class variance for better representation. This work introduces structured metric learning for remote sensing scene representation, a special deep metric learning which can take full advantage of the training batch. However, the deep metrics only consider the pairwise correlation between the training samples, and ignores the classwise correlation from the class view. To take the classwise penalization into consideration, this work defines the center points of the learned features of each class in the training process to represent the class. Through increasing the variance between different center points and decreasing the variance between the learned features from each class and the corresponding center point, the representational ability can be further improved. Therefore, this work develops a novel center-based structured metric learning to take advantage of both the deep metrics and the center points. Finally, joint supervision of the cross-entropy loss and the center-based structured metric learning is developed for the land-use classification in remote sensing. It can joint learn the center points and the deep metrics to take advantage of the point-wise, the pairwise, and the classwise correlation. Experiments are conducted over three real-world remote sensing scene datasets, namely UC Merced Land-Use dataset, Brazilian Coffee Scene dataset, and Google dataset. The classification performance can achieve 97.30%, 91.24%, and 92.04% with the proposed method over the three datasets which are better than other state-of-the-art methods under the same experimental setups. The results demonstrate that the proposed method can improve the representational ability for the remote sensing scenes.

scholarly journals Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism

2021 ◽  
Vol 13 (14) ◽  
pp. 2686
Author(s):  
Di Wei ◽  
Yuang Du ◽  
Lan Du ◽  
Lu Li
Keyword(s):  
Feature Extraction ◽  
Supervised Learning ◽  
Target Detection ◽  
Scene Recognition ◽  
Attention Mechanism ◽  
Sar Image ◽  
Target Level ◽  
Sar Images ◽  
Training Samples ◽  
Pass Through

The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to targets in SAR images with complex scenes, making the target detection task very difficult. Therefore, a SAR target detection network based on a semi-supervised learning and attention mechanism is proposed in this paper. Since the image-level label simply marks whether the image contains the target of interest or not, which is easier to be labeled than the target-level label, the proposed method uses a small number of target-level labeled training samples and a large number of image-level labeled training samples to train the network with a semi-supervised learning algorithm. The proposed network consists of a detection branch and a scene recognition branch with a feature extraction module and an attention module shared between these two branches. The feature extraction module can extract the deep features of the input SAR images, and the attention module can guide the network to focus on the target of interest while suppressing the clutter. During the semi-supervised learning process, the target-level labeled training samples will pass through the detection branch, while the image-level labeled training samples will pass through the scene recognition branch. During the test process, considering the help of global scene information in SAR images for detection, a novel coarse-to-fine detection procedure is proposed. After the coarse scene recognition determining whether the input SAR image contains the target of interest or not, the fine target detection is performed on the image that may contain the target. The experimental results based on the measured SAR dataset demonstrate that the proposed method can achieve better performance than the existing methods.

scholarly journals Applying a Cerebellar Model Articulation Controller Neural Network to a Photovoltaic Power Generation System Fault Diagnosis

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Kuei-Hsiang Chao ◽  
Bo-Jyun Liao ◽  
Chin-Pao Hung
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Fault Diagnosis ◽  
Cerebellar Model Articulation Controller ◽  
Training Process ◽  
Cmac Neural Network ◽  
Photovoltaic Power ◽  
Training Samples ◽  
Parallel Connections ◽  
Model Features

This study employed a cerebellar model articulation controller (CMAC) neural network to conduct fault diagnoses on photovoltaic power generation systems. We composed a module array using 9 series and 2 parallel connections of SHARP NT-R5E3E 175 W photovoltaic modules. In addition, we used data that were outputted under various fault conditions as the training samples for the CMAC and used this model to conduct the module array fault diagnosis after completing the training. The results of the training process and simulations indicate that the method proposed in this study requires fewer number of training times compared to other methods. In addition to significantly increasing the accuracy rate of the fault diagnosis, this model features a short training duration because the training process only tunes the weights of the exited memory addresses. Therefore, the fault diagnosis is rapid, and the detection tolerance of the diagnosis system is enhanced.

scholarly journals Comparison of Fissure Sealant Chair Time and Patients’ Preference Using Three Different Isolation Techniques

Children ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 444
Author(s):  
Rahif E. Mattar ◽  
Ayman M. Sulimany ◽  
Saad S. Binsaleh ◽  
Ibrahim M. Al-Majed
Keyword(s):  
Fissure Sealant ◽  
King Saud University ◽  
Rubber Dam ◽  
Dental Clinics ◽  
Isolation Techniques ◽  
Permanent Molars ◽  
Pit And Fissure Sealant ◽  
The Mean ◽  
Time Required ◽  
Cotton Roll

This randomized clinical trial aimed to evaluate the patient’s preference and chair time needed during pit and fissure sealant placement under three isolation techniques (Isolite system, rubber dam isolation, and cotton roll isolation). Participants, aged 6–15 years and requiring four sealants on the first or second permanent molars, attending the pediatric dental clinics at King Saud University in Saudi Arabia were enrolled according to the inclusion criteria. Each participant received sealants on three random first or second permanent molars using three isolation techniques. The time required for sealant placement was recorded for each technique. Following sealant placement, an interview-based questionnaire was administered to the participants to evaluate their preference regarding the isolation techniques. Forty-eight children (23 male and 25 female) with a mean age of 8.58 ± 1.93 years participated in this study. The mean chair times were 248.14, 255.89, and 243.29 s for the Isolite system, rubber dam isolation, and cotton roll isolation, respectively. Approximately 79% of participants considered cotton roll isolation to be the most comfortable, whereas approximately 71% were significantly less likely to use rubber dam isolation again. In conclusion, there were no significant differences in sealant placement time among the three isolation techniques. However, cotton roll isolation was the technique that was most preferred by the participants.

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