The proceedings contain 19 papers. The topics discussed include: bacterial colony counter using different imageprocessing algorithms;detection of facial expressions based on three feature points using image processin...
ISBN:
(纸本)9798350386363
The proceedings contain 19 papers. The topics discussed include: bacterial colony counter using different imageprocessing algorithms;detection of facial expressions based on three feature points using imageprocessing with artificialneuralnetworks;YOLO-based helmet detection system for safety compliance in oil and gas industry;virtual sample generation using conditional adversarial network with latent spaces as noise inputs;IoT integrated conveyor centralized system;weighted subgraph knowledge distillation for graph model compression;bacterial colony counter using different imageprocessing algorithms;detection of facial expressions based on three feature points using imageprocessing with artificialneuralnetworks;and verifying the effectiveness of using virtual characters for the promotion of a university department.
The growing amount of data collected by Earth Observation (EO) satellites requires new processing procedures able to manage huge quantity of information. artificial intelligence (AI) and deep learning (DL) can provide...
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The growing amount of data collected by Earth Observation (EO) satellites requires new processing procedures able to manage huge quantity of information. artificial intelligence (AI) and deep learning (DL) can provide advanced information also because of their ability to extract valuable information from complex data. Thanks to specific hardware platforms, these algorithms can be used also in space, opening the possibility for new procedures for intelligent data processing. The European Space Agency phi-Sat-2 mission was designed with the purpose of demonstrating the benefits of using AI in space by running AI-based applications on-board a CubeSat. We present here the convolutional autoencoder-based algorithm developed for on-board lossy image compression of the phi-Sat-2 mission and provide a first benchmark addressing a real space mission and a new image compression end-to-end architecture based on AI. image compression is a crucial application that allows to save transmission bandwidth and storage. In fact, images acquired by the sensor can be compressed on-board and sent to the ground where they are reconstructed. DL algorithms have already been successfully applied for image compression however performance degradation may occur in the context of a representative on-board environment. Therefore, besides analyzing the results for the local hardware environment, this article investigates the performance variation for the on-board setting. An additional piece of innovation is the introduction of an applicative metric for the evaluation of the compression to assess the applicability of the reconstructed images for other tasks. Such metric completes those more traditional based on the original-reconstructed image similarity.
In recent years, artificial intelligence technology has become increasingly closely connected with various fields. However, due to the high requirements of traditional convolutional neuralnetworks for memory and comp...
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Eye health has become a global health concern and attracted broad *** the years,researchers have proposed many state-of-the-art convolutional neuralnetworks(CNNs)to assist ophthalmologists in diagnosing ocular diseas...
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Eye health has become a global health concern and attracted broad *** the years,researchers have proposed many state-of-the-art convolutional neuralnetworks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and ***,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model *** alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular *** MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer *** conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 *** results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and ***,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.
Photon-limited deblurring is a complex and demanding problem encountered in various applications where low-light conditions prevail. The scarcity of photons in such situations leads to the introduction of shot noise, ...
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ISBN:
(纸本)9798350350463;9798350350456
Photon-limited deblurring is a complex and demanding problem encountered in various applications where low-light conditions prevail. The scarcity of photons in such situations leads to the introduction of shot noise, resulting in a degradation of image quality. Solving this problem with neuralnetworks often involves constructing models empirically, making the behavior of the underlying architecture challenging to comprehend. A recent technique known as algorithm unrolling has enabled the connection of iterative algorithms with neuralnetworks, where the Convolutional neural Network (CNN) acts as a denoiser. This paper introduces a reduced parameter denoiser to enhance image quality and preserve finer details or avoid over-smoothing of the image during reconstruction. As a result, the unrolled model surpasses existing deblurring methods for improving image quality in low-light conditions. The proposed denoiser reduces the number of parameters by a factor of 3.84 and preserves the finer details while reconstructing. Our model improves computational efficiency and storage requirements compared to the state-of-the-art.
In this paper, we analyze modern approaches and methods of neuralnetworks employment in the tasks of capturing and demonstrating a wide dynamic sound stage (HDR). We highlight the essence of the problem and its relev...
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Transferable adversarial examples highlight the vulnerability of deep neuralnetworks (DNNs) to imperceptible perturbations across various real-world applications. While there have been notable advancements in untarge...
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Hardware such as multipliers and dividers is necessary for all electronic systems. This paper explores Vedic mathematics techniques for high-speed and low-area multiplication. In the study of multiplication algorithms...
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Hardware such as multipliers and dividers is necessary for all electronic systems. This paper explores Vedic mathematics techniques for high-speed and low-area multiplication. In the study of multiplication algorithms, various bits-width ranges of the Anurupyena sutra are used. Parallelism is employed to address challenging problems in recent studies. Various designs have been developed for the Field Programmable Gate Array (FPGA) implementation employing Very Large-Scale integration (VLSI) design approaches and parallel computing technology. Signal processing, machine learning, and reconfigurable computing research should be closely monitored as artificial intelligence develops. Multipliers and adders are key components of deep learning algorithms. The multiplier is an energy-intensive component of signal processing in Arithmetic Logic Unit (ALU), Convolutional neuralnetworks (CNN), and Deep neuralnetworks (DNN). For the DNN, this method introduces the Booth multiplier blocks and the carry-save multiplier in the Anurupyena architecture. Traditional multiplication methods like the array multiplier, Wallace multiplier, and Booth multiplier are contrasted with the Vedic mathematics algorithms. On a specific hardware platform, Vedic algorithms perform faster, use less power, and take up less space. Implementations were carried out using Verilog HDL and Xilinx Vivado 2019.1 on Kintex-7. The area and propagation delay were reduced compared to other multiplier architectures.
Gallbladder cancer is a relatively rare but highly malignant tumor. This study mainly explores the CT findings of gallbladder cancer based on neuralnetworks. This study designed a gallbladder cancer LDCT image denois...
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Gallbladder cancer is a relatively rare but highly malignant tumor. This study mainly explores the CT findings of gallbladder cancer based on neuralnetworks. This study designed a gallbladder cancer LDCT image denoising network. Ability to process different doses of gallbladder cancer LDCT images with significant differences in noise and artifact distribution, this study designed the noise level estimation sub-network as a codec structure;the decoding part is used to generate the noise level of the gallbladder cancer LDCT image Artifact image. artificialneural network is a kind of artificialneural network that simulates the behavior characteristics of animal neural network and achieves the purpose of processing information by adjusting the interconnection between a large number of internal nodes. In order to meet the requirements of medical diagnosis for gallbladder cancer LDCT image quality, this study designed the backbone noise reduction network as a GAN framework that can be internally optimized. The discriminator network structure of this study is a multi-scale inception structure. As a sub-network of GAN, the discriminator network is used to distinguish true and false images and constrain the generator to make the generated images close to real images. In addition, it can be used as a noise evaluation sub-network to evaluate the noise gallbladder cancer LDCT. The treatment methods of gallbladder cancer include surgery, chemotherapy, radiation therapy, arterial interventional perfusion therapy, targeted therapy, etc. Surgery is currently the first choice for the treatment of gallbladder cancer, and the choice of surgery depends on the stage and growth site of gallbladder cancer. The image denoising network was used to evaluate the quality of the noise-reduced image. The average precision of GAN network for gallbladder cancer area is 91.0%, and the highest value is 95.2%. This study will provide a reliable reference value for the auxiliary diagnosis of gallbl
Extracting semantic information from remote sensing (RS) images has gained attention for its wide applications in defense, disaster management, and urban planning. Captioning RS images is challenging due to intricate ...
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ISBN:
(纸本)9798350359329;9798350359312
Extracting semantic information from remote sensing (RS) images has gained attention for its wide applications in defense, disaster management, and urban planning. Captioning RS images is challenging due to intricate properties like resolutions, color bands, and object types. Generating precise captions requires domain expertise, and manual annotation is timeconsuming. The common approach involves using an encoderdecoder-based framework for RS image captioning, where an input image is encoded into a feature vector and decoded into a caption. Selecting the right image encoder is vital for optimizing caption prediction systems in specific domains. While Convolutional neural Network (CNN) based encoders are acknowledged for extracting crucial image features, it's important to assess variations in their mechanisms and architectures carefully. This paper thoroughly examines various CNNs to evaluate their effectiveness in RS image captioning. We also explore the performance of two caption generation techniques, viz., greedy search and beam search. The encoders are clustered as good, medium, and bad, with ResNet (CNN) emerging as the preferred choice in the good cluster across all considered datasets. The impact of choosing between beam search and greedy search is minimal. Additionally, we conduct a subjective evaluation of leading models to address limitations associated with purely numerical assessments. The paper is a novel contribution, providing the first-of-its-kind subjective evaluation of CNN-based encoders for the RS image captioning task.
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