Froth flotation is an important process in the mineral processing industry for extracting valuable materials. This work investigates online microscopic imaging and machine learning based image analysis methods for rea...
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Super-resolution (SR) aims to reconstruct high-resolution images from low-resolution inputs, with deep learning advancements driving substantial improvements in SR performance. This paper presents a comprehensive revi...
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Enriching and developing the connotation and value of labor education theories can help students in higher vocational colleges form correct viewpoint and attitude towards labor. Higher vocational colleges should put m...
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Enriching and developing the connotation and value of labor education theories can help students in higher vocational colleges form correct viewpoint and attitude towards labor. Higher vocational colleges should put more efforts to education through practice based on the features of each discipline. Accurately identifying students' behavior in complex practice and training scenarios is very important for teachers to know about their status during practice and training, however, existing research results are not applicable to complex practice and training scenarios since they have neither considered how to improve the accuracy of static image identification while ensuring the model is lightweight structured, nor considered the time series information of students' behavior during practice and training in the collected videoimages. For this reason, this paper took the property management major as the subject to study the identification of student behavior during practice and training based on videoimage. In the paper, the students' practice and training content was divided into three aspects, a task of asking students to cooperate with each other to deal with an equipment failure emergency was adopted for the research, and a research idea of helping teachers figure out students' status during practice and training via identifying their actions and intentions during the said activities was determined. Then, a few pre-processing operations were performed on the captured videoimages of student behavior during practice and training, including removing abnormal image frames, filtering, and aligning, etc. After that, based on the collected videoimage data, the dynamic convolution kernel was improved and optimized, and a lightweight convolution network model was built for identifying student behavior during practice and training. At last, experimental results verified the validity of the proposed identification model.
AI has become one of the key forces driving social progress, particularly in the fields of VR and AR, where the application of AI video generation technology is spearheading a technological revolution. This article de...
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This article proposes an algorithm framework recently developed for video synthetic aperture radar (ViSAR) and presents the processing results of the airborne data collected by a W-band radar. This framework aims to a...
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This article proposes an algorithm framework recently developed for video synthetic aperture radar (ViSAR) and presents the processing results of the airborne data collected by a W-band radar. This framework aims to accelerate the time-domain algorithm and produce synthetic aperture radar (SAR) video that has no change of view angle, which is highly desired in target tracking. The presented framework includes a few modified processing algorithms, where the strategy of subaperture recursion is proposed. The modified acceleration algorithm is used to avoid the interpolation and adapt to high squint angles without rotation of image grids. In addition, the conventional autofocus method has been improved to deal with urban data that usually contain strong scatterers. The algorithm framework can achieve SAR video without rotation of view angle with a lower computational load compared to other existing time-domain methods, which has been verified on airborne real data. Its processing efficiency becomes impressive if the apertures are highly overlapped in some cases that require a very high frame rate.
The concept of education, which has existed for hundreds of years, is being moved to online environments, especially with the increase in internet use. Although the use of internet-based applications in education incr...
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ISBN:
(纸本)9798350349467;9798350349450
The concept of education, which has existed for hundreds of years, is being moved to online environments, especially with the increase in internet use. Although the use of internet-based applications in education increases accessibility to information, it makes it difficult to evaluate students' performances fairly. In recent years, the number of imageprocessing studies on this subject has been increasing in order to take online education platforms to the next level. In this study, fatigue estimation was made to measure the performance of students in distance education systems. Some machine learning and imageprocessing methods were used for fatigue prediction. In the proposed study, two different data sets consisting of 15182 images were used. Python and Flask framework are used in model training. A web-based application was developed with Flask that performs real-time fatigue detection and head position estimation via webcam.
This work presents a novel approach to real-timevideo frame interpolation using Generative Adversarial Networks (GANs) to enhance streaming services. We developed a custom GAN architecture comprising a generator, whi...
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With the increasing importance of video data in real-world applications, there is a rising need for efficient object detection methods that utilize temporal information. While existing video object detection (VOD) tec...
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ISBN:
(纸本)1577358872
With the increasing importance of video data in real-world applications, there is a rising need for efficient object detection methods that utilize temporal information. While existing video object detection (VOD) techniques employ various strategies to address this challenge, they typically depend on locally adjacent frames or randomly sampled images within a clip. Although recent Transformer-based VOD methods have shown promising results, their reliance on multiple inputs and additional network complexity to incorporate temporal information limits their practical applicability. In this paper, we propose a novel approach to single image object detection, called Context Enhanced TRansformer (CETR), by incorporating temporal context into DETR using a newly designed memory module. To efficiently store temporal information, we construct a class-wise memory that collects contextual information across data. Additionally, we present a classification-based sampling technique to selectively utilize the relevant memory for the current image. In the testing, We introduce a test-time memory adaptation method that updates individual memory functions by considering the test distribution. Experiments with CityCam and imageNet VID datasets exhibit the efficiency of the framework on various video systems. The project page and code will be made available at: https://***/CETR.
image enhancement technology plays an important role in the practical application of detecting underwater tunnels. By improving image quality, it enhances the accuracy and reliability of detection results. However, in...
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With the rapid developments of low-end IoT devices and artificial intelligence (AI), the heterogeneous and dynamic data has been increasing drastically, especially in the era of AI-enabled maritime cyber-physical syst...
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