Night is an inevitable scene for surveillance video. Due to the high image resolution, complex background, uneven illumination, and similarity between the target and the background of hawk-eye surveillance video, it i...
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Thyroid nodules are found in 19% to 67% of individuals who are screened for thyroid cancer using ultrasonography. The large number of individuals examined is placing significant stress on radiologists and the healthca...
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ISBN:
(纸本)9781510660335;9781510660342
Thyroid nodules are found in 19% to 67% of individuals who are screened for thyroid cancer using ultrasonography. The large number of individuals examined is placing significant stress on radiologists and the healthcare system. Previous works on thyroid nodule detection were based on 2D images ignoring the contextual information of adjacent frames in the video, which limits the accuracy of detection. Furthermore, the inability to track nodules at the video scale also limits subsequent analyses such as benign/malignant classification that relies on the whole nodule rather than image frames. In this study, beyond detection on a single image frame, thyroid ultrasound videos were used for the detection of nodules. video-scale tracking requires more training data than frame-scale detection;however, the annotation of medical images is time-consuming and dependent on experienced radiologists. To alleviate the need for large-scale labelled datasets, a patch-scale self-supervised pre-training model was trained on unlabeled data to obtain the ability to extract patch-distinguishing features, which are crucial for object detection. The pre-trained model was transferred to the tracking model to improve the performance. Experimental results on 22 ultrasound videos containing 47 nodules showed that the performance of our proposed method was 0.523 for mAP@50 (object detection task) and 0.430 for HOTA (tracking task), which are better than those of the baseline model (0.451 and 0.398). In addition, the proposed method can process 23 frames per second, which can meet the requirements of real-time tracking in screening scenarios.
The present study showcases a novel deep learning-based vision application tasked with reducing the communication gap between sign language and non-sign language users. Speech and hearing impairments are a type of dis...
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ISBN:
(纸本)9783031686382;9783031686399
The present study showcases a novel deep learning-based vision application tasked with reducing the communication gap between sign language and non-sign language users. Speech and hearing impairments are a type of disability that restricts an individual's ability to communicate with others properly. Modern-day automation tools can be used to address this communication gap and allow people to communicate ubiquitously and in a variety of situations. The method defined in the paper involves loading a video file, extracting each frame, and detecting the hand landmarks in each frame using the Media-Pipe library. Then the frame is cropped, and the region of interest is pre-processed and stored in a new data directory for training purposes. The pre-processing involves the use of Gaussian blur, edge detection, morphological transformations, and signal processing functions. Data augmentation is then performed, and images are saved in a new directory. The images are then used to train a custom CNN model, which contains four convolutional layers along with two fully connected layers. The model is compiled using the categorical cross-entropy loss function, optimised using the RMSprop optimiser, and then evaluated using the evaluation metric, accuracy. The predicted sign language alphabet is displayed on the screen and is converted to speech using the Google Text-to-Speech library. The model achieves an overall accuracy of 93.96%. The findings indicate that the proposed approach can serve as a road map to develop a real-time system capable of sign language recognition and Direct future investigations in this domain.
The increasing demand of emerging 8K video content, has made its transmission one of the main challenges for broadcasting companies. Thus, upgrading the existing infrastructures seem to be the only option to support t...
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Conventional vehicle counting using various techniques such as manual counts are no longer efficient in the era of industrial revolution 4.0. The algorithm within the intelligence system using a realtimevideo and im...
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ISBN:
(纸本)9781510691704
Conventional vehicle counting using various techniques such as manual counts are no longer efficient in the era of industrial revolution 4.0. The algorithm within the intelligence system using a realtimevideo and imageprocessing technique is proposed due to its reliability, efficiency, cost effectiveness and safety for gathering data. Surveillance cameras commonly installed in large cities could be used to obtain traffic data recording, allowing for an automated system to be easily adopted at minimal cost. This study provides an alternative and economical means to estimate traffic density via video-imageprocessing which adopts OpenCV in the Python code. This method only requires a fixed video camera be positioned at an elevated position such as on a pedestrian bridge or a light pole. The images are processed automatically through OpenCV code bindings in Python. The system requires frames from the video to be captured so background subtraction can be performed to detect and count the vehicles using Gaussian Mixture Model. The classification of vehicles by size is done by comparing the contour areas to the assumed values. The proposed algorithm can be adapted to meet the requirements of the user and the camera’s position. The algorithm allows traffic data to be obtained, which may assist local authorities make decisions regarding urban planning and the design of transportation systems. Sample videos of traffic scenes were used to compare the detection and classification of vehicles. Results from the proposed algorithm were compared with manual count results from the field. Analysis of the classification and volume count of vehicles using the proposed algorithm is shown to have an error rate of 1.3% compared to an error rate of 6.4% using the manual tally counter method. The results confirmed that the proposed automatic counting system performed better when compared to the manual tally counter method with the additional benefits of increase cost efficiency and impr
In order to solve the problem of fire detection in the heat exchange station, an intelligent fire early warning system based on image analysis is proposed, which realizes the network, digital and intelligent design of...
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In recent years, with the development of digital technology, digital imageprocessing has been widely and deeply applied in the field of computer graphics. Digital imageprocessing system is a complex real-time system...
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ISBN:
(纸本)9781665487894
In recent years, with the development of digital technology, digital imageprocessing has been widely and deeply applied in the field of computer graphics. Digital imageprocessing system is a complex real-time system, it from the camera, fax machine and other scanning equipment to obtain image information, after digital transformation, digital image information coding, filtering, enhancement, recovery, compression, storage and other processing, finally generate visual image. This design uses TMS320C6748 as the core processor of the system, SAA7113 as the video decoding chip of the system, CPLD as the sampling controller, DDR2 chip as the external expansion memory. The ROM expansion uses NAND flash memory chip. On the basis of hardware design, combined with software algorithm to complete imageprocessing. The system can be used in information communication, image recognition, news scene and other fields of imageprocessing and transmission. This paper analyzes the hardware structure and data processing algorithm of the system in detail. The experimental results show that the system can not only obtain higher compression ratio, but also reduce the distortion of the reconstructed image. The imageprocessing system has certain practicability.
For the detection of objects floating in the river, most of the traditional intelligent video monitoring methods are used to monitor through manual viewing. During the traditional video data transmission and processin...
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real-timevideo monitoring plays an important role in the safety management of the factory. The traditional cloud-based methods have high transmission delay and bandwidth cost, and are not suitable for large-scale vid...
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The proceedings contain 49 papers. The topics discussed include: disaster recovery solution for on-premises infrastructure using Proxmox backup server;a method of encoding video information to increase its reliability...
ISBN:
(纸本)9798350372571
The proceedings contain 49 papers. The topics discussed include: disaster recovery solution for on-premises infrastructure using Proxmox backup server;a method of encoding video information to increase its reliability in an information and telecommunications network;prediction of states of information and communication systems using machine learning;Wi-Fi repeater influence on wireless access;method of selective video segment processing for intelligent videoimage quality enhancement technologies;investigating the efficiency of tournament selection operator in genetic algorithm for solving TSP;an adaptive spectrum time assignment in OFDM datacenter optical networks;big data analysis for startup of supporting Ukraine internet tourism;and AR intelligent real-time method for cultural heritage object recognition.
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