This research investigates the implementation of real-time aerial image edge detection using the Canny edge detection algorithm with the MicroWatt Power Instruction Set Architecture (ISA)-Open Core Processor on Field ...
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real-time violence detection is essential for protecting the safety and security of people, especially in college campuses that are dynamic and have crowds. Manual surveillance systems are popular but inefficient as t...
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realtime generation has been achieved for 4K full color rainbow hologram with normal PC. A line source approximation can accelerate computation speed 1.3 to 2.6 times faster than the previous result, depending on obj...
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ISBN:
(纸本)9781510670815;9781510670808
realtime generation has been achieved for 4K full color rainbow hologram with normal PC. A line source approximation can accelerate computation speed 1.3 to 2.6 times faster than the previous result, depending on object points. In the approximation, each point in the object is converted to truncated line source. It makes 2D hologram calculation to 1D and calculation speed is increased. Although the rainbow hologram sacrifices vertical parallax, it can be generated faster than image hologram and full color image can be reconstructed with single SLM. Experimental results show that holograms are generated and displayed about 50 frames per second with 140,000 points and full color reconstructed images are observed.
In many video restoration/translation tasks, imageprocessing operations are naively extended to the video domain by processing each frame independently, disregarding the temporal connection of the video frames. This ...
ISBN:
(纸本)9798350307184
In many video restoration/translation tasks, imageprocessing operations are naively extended to the video domain by processing each frame independently, disregarding the temporal connection of the video frames. This disregard for the temporal connection often leads to severe temporal inconsistencies. State-Of-The-Art (SOTA) techniques that address these inconsistencies rely on the availability of unprocessed videos to implicitly siphon and utilize consistent video dynamics to restore the temporal consistency of frame-wise processed videos which often jeopardizes the translation effect. We propose a general framework for this task that learns to infer and utilize consistent motion dynamics from inconsistent videos to mitigate the temporal flicker while preserving the perceptual quality for both the temporally neighboring and relatively distant frames without requiring the raw videos at test time. The proposed framework produces SOTA results on two benchmark datasets, DAVIS and ***, processed by numerous imageprocessing applications. The code and the trained models are available at https://***/MKashifAli/TARONVD.
With the continuous development of digital imageprocessing technology, edge detection technology is playing an increasingly important role in the field of imageprocessing. The Canny algorithm is a classical gradient...
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ISBN:
(纸本)9798400707032
With the continuous development of digital imageprocessing technology, edge detection technology is playing an increasingly important role in the field of imageprocessing. The Canny algorithm is a classical gradient-based edge detection algorithm with excellent performance and robustness. However, due to its large computational amount and poor real-time performance, the traditional software implementation method has been unable to meet the demands of modern high-speed imageprocessing. Therefore, the Canny algorithm hardware is turned into a popular research direction. As a programmable logic device, FPGA has the advantages of high flexibility, short development period and strong parallel computing power, which is widely used in the field of digital signal processing. At present, there has been a lot of research work on FPGA in implementing the Canny algorithm, but most of the schemes have some problems, such as slow speed and high resource occupancy rate. Therefore, this paper presents an improved scheme for the hardware design of Canny algorithm based on FPGA, aiming to improve the speed and efficiency of imageprocessing while reducing the utilization of hardware resources.
Learner engagement is a significant factor determining the success of implementing an intelligent educational network. Currently the use of Massive Open Online Courses has increased because of the flexibility offered ...
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Learner engagement is a significant factor determining the success of implementing an intelligent educational network. Currently the use of Massive Open Online Courses has increased because of the flexibility offered by such online learning systems. The COVID period has encouraged practitioners to continue to engage in new ways of online and hybrid teaching. However, monitoring student engagement and keeping the right level of interaction in an online classroom is challenging for teachers. In this paper we propose an engagement recognition model by combining the image traits obtained from a camera, such as facial emotions, gaze tracking with head pose estimation and eye blinking rate. In the first step, a face recognition model was implemented. The next stage involved training the facial emotion recognition model using deep learning convolutional neural network with the datasets FER 2013. The classified emotions were assigned weights corresponding to the academic affective states. Subsequently, by using the Dlib's face detector and shape predicting algorithm, the gaze direction with head pose estimation, eyes blinking rate and status of the eye (closed or open) were identified. Combining all these modalities obtained from the image traits, we propose an engagement recognition system. The experimental results of the proposed system were validated by the quiz score obtained at the end of each session. This model can be used for realtimevideoprocessing of the student's affective state. The teacher can obtain a detailed analytics of engagement statics on a spreadsheet at the end of the session thus facilitating the necessary follow-up actions.
In the field of videoprocessing, edge detection plays a crucial role in various applications such as object recognition, scene understanding, and video surveillance. real-timeprocessing of video data demands efficie...
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Currently, video data dominates mobile internet traffic. Aided by AI, promoting active research in Blind video Quality Assessment (BVQA) for evaluating received video quality without the original video. However, AI me...
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Holography creates three-dimensional (3D) images with depth and relief, and can be applied to medicine, engineering, and entertainment, enabling holographic communication, live streaming, and virtual gatherings. Digit...
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ISBN:
(纸本)9798350354744;9798350354737
Holography creates three-dimensional (3D) images with depth and relief, and can be applied to medicine, engineering, and entertainment, enabling holographic communication, live streaming, and virtual gatherings. Digital holography involves using digital displays, cameras, and beams. This paper discusses various holographic systems, including computational holography, which uses computers to produce 3D images, Deep Holography (DH), which utilizes deep neural networks (DNN), and tensor holography, which combines DNN and machine learning (ML) to produce 3D images in mid-air. This paper also addresses network challenges, such as the high data transmission, bandwidth limitations that can hinder the quality of the holographic image, and the low latency crucial for maintaining real-time interaction in holographic communication. While some scholars introduce the capacities of 6G networks, other authors propose a compression and differentiated prioritization technique. Holography can create striking images using convolutional neural networks (CNN) and an anti-aliasing double-phase (AA-DPM) method. Based on the communication performance test that has been carried out, it can be concluded that ensuring access to powerful and optimized hardware and software is crucial for both text and holographic image generation. As the complexity and length of the text increase, processingtime augments as well. There are significant differences between using powerful servers and weaker hardware. Finally, it can be concluded that it is imperative to prioritize hardware and software for image generation to facilitate a smooth conversation.
This is a follow-up study on Zernike moments applicable in detection tasks owing to a construction of complex-valued integral images that we have proposed in [3]. The main goal of the proposition was to calculate the ...
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ISBN:
(纸本)9783031637537;9783031637513
This is a follow-up study on Zernike moments applicable in detection tasks owing to a construction of complex-valued integral images that we have proposed in [3]. The main goal of the proposition was to calculate the mentioned features fast (in constant-time). The proposed solution can be applied with success when dealing with single images, however it is still too slow to be used in real-time applications, for example in videoprocessing. In this work we attempted to solve mentioned problem. In this paper we propose a technique in order to reduce the detection time in real-time applications. The degree of reduction is controlled by two parameters: fs (related to the gap between frames that undergo a full scan) and nb (related to the size of neighborhood to be searched on non-fully scanned frames). We present a series of experiments to show how our solution performs in terms of both detection time and accuracy.
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