In this paper, the 3D space imaging model of machine vision is constructed. Starting from the traditional machine vision imageprocessing algorithm flow, the image denoising process and target tracking process are opt...
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Digital image watermarking plays an important role in securing multimedia content against unauthorized use and manipulation. The realm of image watermarking focuses on its vulnerability to various attacks and the inte...
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Due to the limitations of software technology and weak imageprocessing technology, traditional interior design has limited scene reproduction ability and supports a small range of scenes. Therefore, this article prop...
Due to the limitations of software technology and weak imageprocessing technology, traditional interior design has limited scene reproduction ability and supports a small range of scenes. Therefore, this article proposes an interior design system based on imageprocessing and virtual reality technology. The system hardware consists of an imageprocessing module, a virtual simulation module, and a database support module, mainly responsible for processingimage information. The system software mainly consists of program loading module, data storage and reading/writing module, bus transmission module, etc. After testing, the system's denoised image is smooth and clear, with obvious edge segmentation. After image rendering, the reflection and light effects are good, and the imageprocessing time is short, which has a good indoor design effect.
UAV line inspection greatly improves the efficiency of transmission line inspection. If the digital image collected by computer is processed, the efficiency can be further improved, and image enhancement processing is...
UAV line inspection greatly improves the efficiency of transmission line inspection. If the digital image collected by computer is processed, the efficiency can be further improved, and image enhancement processing is an important process of computerimageprocessing Combined with the characteristics of power line digital gray image, two different gray image enhancement algorithms using random mathematical model and fuzzy mathematical model are discussed, and the application effects of these algorithms are compared This paper analyzes the shortcomings of the existing image enhancement algorithms in the application of power line gray image, presents an improved image enhancement algorithm based on fuzzy mathematical model, and uses an example to verify its better applicability.
This paper proposes two memristor array memory structures for binary and grey-scale images. The memristor memory array is simulated, and the memory cross-array based on 2T2M has been designed for reading and writing o...
This paper proposes two memristor array memory structures for binary and grey-scale images. The memristor memory array is simulated, and the memory cross-array based on 2T2M has been designed for reading and writing operations. This effectively reduces the number of memory cells required for storage, allowing for a decrease in space utilized and an increase in storage density per single memory cell. Through this, the efficiency of storage can be improved, providing an enriched theoretical basis for utilizing memristors in imageprocessing.
Frame interpolation is an essential video processing technique that adjusts the temporal resolution of an image sequence. While deep learning has brought great improvements to the area of video frame interpolation, te...
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ISBN:
(纸本)9781665493468
Frame interpolation is an essential video processing technique that adjusts the temporal resolution of an image sequence. While deep learning has brought great improvements to the area of video frame interpolation, techniques that make use of neural networks can typically not easily be deployed in practical applications like a video editor since they are either computationally too demanding or fail at high resolutions. In contrast, we propose a deep learning approach that solely relies on splatting to synthesize interpolated frames. This splatting-based synthesis for video frame interpolation is not only much faster than similar approaches, especially for multi-frame interpolation, but can also yield new state-of-the-art results at high resolutions.
This paper introduces a parallel architecture designed for real-time imageprocessingapplications, utilizing a combination of digital signal processor (DSP) and field-programmable gate array (FPGA) components for opt...
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Neural network approaches are machine learning methods that are widely used in various domains, such as healthcare and cybersecurity. Neural networks are especially renowned for their ability to deal with image datase...
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ISBN:
(纸本)9798350326970
Neural network approaches are machine learning methods that are widely used in various domains, such as healthcare and cybersecurity. Neural networks are especially renowned for their ability to deal with image datasets. During the training process with images, various fundamental mathematical operations are performed in the neural network. These operations include several algebraic and mathematical functions, such as derivatives, convolutions, and matrix inversions and transpositions. Such operations demand higher processing power than what is typically required for regular computer usage. Since CPUs are built with serial processing, they are not appropriate for handling large image datasets. On the other hand, GPUs have parallel processing capabilities and can provide higher speed. This paper utilizes advanced neural network techniques, such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST-VGG16, and our proposed models, to compare CPU and GPU resources. We implemented a system for classifying Autism disease using face images of autistic and non-autistic children to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It was observed that GPU outperformed CPU in all tests conducted. Moreover, the performance of the neural network models in terms of accuracy increased on GPU compared to CPU.
image translation is an important and challenging area of computer vision. It aims to design models that translate source-domain images to target-domain images with applications such as data enhancement, style migrati...
image translation is an important and challenging area of computer vision. It aims to design models that translate source-domain images to target-domain images with applications such as data enhancement, style migration and super-resolution. Until 2016, researchers have successfully implemented generative adversarial networks to image translation using the findings of deep learning techniques for visual generation tasks. In this paper, we first review and analyze the literature in the field of GAN-based image translation and summarize common normalization techniques and model judging metrics. In accordance with the mapping connection between the model inputs and outputs, self-supervised and unsupervised methods to image translation models are then split into categories. The strengths and weaknesses of these models are then analyzed, and a short explanation of how each model performed on various evaluation indicators. Finally, several potential future research issues in this field are discussed.
Remote sensing images are useful for a wide variety of planet monitoring applications, from tracking deforestation to tackling illegal fishing. The Earth is extremely diversethe amount of potential tasks in remote sen...
ISBN:
(纸本)9798350307184
Remote sensing images are useful for a wide variety of planet monitoring applications, from tracking deforestation to tackling illegal fishing. The Earth is extremely diversethe amount of potential tasks in remote sensing images is massive, and the sizes of features range from several kilometers to just tens of centimeters. However, creating generalizable computer vision methods is a challenge in part due to the lack of a large-scale dataset that captures these diverse features for many tasks. In this paper, we present SATLASPRETRAIN, a remote sensing dataset that is large in both breadth and scale, combining Sentinel-2 and NAIP images with 302M labels under 137 categories and seven label types. We evaluate eight baselines and a proposed method on SATLASPRETRAIN, and find that there is substantial room for improvement in addressing research challenges specific to remote sensing, including processingimage time series that consist of images from very different types of sensors, and taking advantage of long-range spatial context. Moreover, we find that pre-training on SATLASPRETRAIN substantially improves performance on downstream tasks, increasing average accuracy by 18% over imageNet and 6% over the next best baseline. The dataset, pre-trained model weights, and code are available at https://***/.
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