In this paper, we aim to help in identifying the people that are violating social distancing norms set by the government (necessary during the COVID-19 pandemic in public places), by providing an efficient real-time d...
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To achieve target detection and defect recognition in power inspection images, an imageprocessing and recognition algorithm based on deep learning is proposed. This algorithm mainly adopts an improved Faster-RCNN mod...
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Fluidized bed granulation is a unit operation widely used in the pharmaceutical, chemical and food processing industries. It is a manufacturing technology that by suspending lose powders using hot air and transforms t...
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The integration of time-of-Flight (TOF) information in the reconstruction process of Positron Emission Tomography (PET) improves image qualities. However, implementing the cutting-edge model-based deep learning method...
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ISBN:
(纸本)9781728198354
The integration of time-of-Flight (TOF) information in the reconstruction process of Positron Emission Tomography (PET) improves image qualities. However, implementing the cutting-edge model-based deep learning methods for TOF-PET reconstruction is challenging due to the substantial memory requirements. In this study, we presented a novel model-based deep learning approach, LMPDNet, for TOF-PET reconstruction from list-mode data. We addressed the issue of real-time parallel computation of the projection matrix for list-mode data, and proposed an iterative model-based module that utilized a dedicated network model for list-mode data. Our experimental results indicated that the proposed LMPDNet outperformed traditional iteration-based TOF-PET list-mode reconstruction algorithms. Additionally, we compared the spatial and temporal consumption of list-mode data and sinogram data in model-based deep learning methods, demonstrating the superiority of list-mode data in model-based TOF-PET reconstruction.
An accurate and consistent survey of road surface distresses is critical for pavement rehabilitation design and management, allowing public managers to maximize the value of constantly limited budgets for road improve...
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ISBN:
(数字)9781510661714
ISBN:
(纸本)9781510661707;9781510661714
An accurate and consistent survey of road surface distresses is critical for pavement rehabilitation design and management, allowing public managers to maximize the value of constantly limited budgets for road improvements and maintenance. Manual pavement distress surveys are time-consuming, costly, and dangerous on heavily traveled highways. Automated surveys using video recording hardware devices have been developed and improved over the years, to solve the problems associated with manual surveys. However, reliable distress detection software and data analysis remain difficult. With the advances in smartphone technology, it is now possible to use mounted devices in the field effectively for such applications. A smartphone application was previously developed to utilize on-board accelerometer, gyroscope, and GPS sensors, along with software derived signals from the same sensors, to sample vibrational and geolocation datasets to capture pavement distresses such as potholes when mounted in a standardized configuration in a vehicle. This study examines the possibility of using real-time video processing for pavement surface quality detection. Video captured from the mounted camera is analyzed to estimate road conditions and correlated with sensor data ground truth to corroborate the efficacy of the technique. The findings of this study could indeed aid in developing more effective uses of specialized software for pavement condition classification, to assist decision makers in selecting solutions based on budget and desired survey accuracy, and to evaluate how existing devices will perform when used with the developed algorithm.
Due to the flooding problem which has a significant challenge towards locals who are responsible for monitoring the water level in various canals, an innovative solution for monitoring water levels is needed. Therefor...
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ISBN:
(纸本)9798350381771;9798350381764
Due to the flooding problem which has a significant challenge towards locals who are responsible for monitoring the water level in various canals, an innovative solution for monitoring water levels is needed. Therefore, a method utilizing images from CCTV cameras for real-time water level assessment is presented. By leveraging an imageprocessing approach including image restoration and binary imageprocessing techniques, we utilized the HED edge detection followed by erosion to detect water lines and then estimate the water level through the comparison and matching the coordinates between detected edges and predefined water levels. Experimental results, compared to traditional edge detection like Canny and deep learning-based HED edge detection, proved the effectiveness of our proposed method in various challenging canal scenarios, exceeding the accuracy of the other two methods.
The article proposes an algorithm for processing parallel analysis of visual data obtained by a machine vision system, recorded information in the human visible spectrum, and information received by a range camera. An...
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ISBN:
(数字)9781510661714
ISBN:
(纸本)9781510661707;9781510661714
The article proposes an algorithm for processing parallel analysis of visual data obtained by a machine vision system, recorded information in the human visible spectrum, and information received by a range camera. An algorithm for the formation of stable features as elements of the human body, head and pupils of a person and parallel tracking of their increment is proposed. To highlight trend lines in element displacement and eliminate the high frequency component based on a combined criterion. The image is preliminarily processed to reduce the effect of the noise component based on a multi-criteria objective function. As test data used to evaluate the effectiveness, a video stream with a resolution of 1024x768 (8-bit, color image, visible range), 3D data, and expert evaluation data are used.
Through the use of a Streamlit web application, the script is a highly intelligent tool that combines cutting-edge quantum processing principles with traditional computer vision approaches to handle a variety of face ...
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ISBN:
(纸本)9798331515911
Through the use of a Streamlit web application, the script is a highly intelligent tool that combines cutting-edge quantum processing principles with traditional computer vision approaches to handle a variety of face detection and identification tasks. While other libraries handle computer vision jobs and numerical calculations, such as NumPy and OpenCV, Streamlit is essential to developing the web application interface. While Streamlit WebRTC allows real-time video streaming, PIL is utilized for imageprocessing. The application is set up to have a personalized look and a centered layout. Users may upload photographs, choose detection modes, and modify face detection settings on its header and sidebar. The program detects faces in uploaded photographs by encircling them with bounding boxes using the Haar Cascade Classifier. This may be used to build a face dataset or train a face recognition algorithm. Another important feature is real-time face identification, in which the software uses the Haar Cascade Classifier to identify faces in a video stream. Bounding boxes are used to emphasize faces, and photos containing faces are retained for later study or training. The program may utilize taken photographs to train a model for face recognition. These photos are processed, turned into grayscale, and then the LBPH (Local Binary Patterns Histograms) technique is used to train the model. The trained model is then kept for use in upcoming facial recognition assignments. Moreover, the program may be set up to track attendance in realtime by identifying faces in a webcam stream. time stamps are recorded, attendance is logged using recognized faces, and the information is saved to a CSV file. All things considered, the script provides a complete solution that combines traditional and quantum methods for real-time face identification, picture processing, and attendance monitoring. This makes it an adaptable tool for a range of computer vision and machine learning application
This demo paper gives a real-time learned image codec on FPGA. By using Xilinx VCU128, the proposed system reaches 720P@30fps codec, which is 7.76x faster than prior work.
ISBN:
(纸本)9781665475921
This demo paper gives a real-time learned image codec on FPGA. By using Xilinx VCU128, the proposed system reaches 720P@30fps codec, which is 7.76x faster than prior work.
In modern industrial environments, automation is critical in enhancing efficiency and safety. This paper proposes an innovative approach to controlling industrial conveyors by integrating a Raspberry Pi with a digital...
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In modern industrial environments, automation is critical in enhancing efficiency and safety. This paper proposes an innovative approach to controlling industrial conveyors by integrating a Raspberry Pi with a digital imageprocessing system capable of real-time human detection. Consequences aside, the principal objective is to cease conveyor operation immediately upon detecting an approaching individual to prioritize individuals' safety and prevent catastrophes. In 2021, a thorough examination of conveyor accidents across seven discrete heavy industries reaffirmed the imperative nature of implementing such precautions. The system employs the OpenCV module, which comprises object detection algorithms and potentially deep learning models such as YOLO or Faster R-CNN, to discern individuals via digital imageprocessing methods and the Raspberry Pi's computational capabilities. Audible and visual feedback devices provide information on the conveyor's status, and failsafes are incorporated to halt its motion promptly in the event of a malfunction. This endeavor aims to enhance industrial safety by integrating software and physical components. The importance of automation in fostering safer work environments and mitigating potential hazards is underscored.
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