This research presents a novel method for monitoring attendance by creating an online system that combines facial recognition technology with machine learning algorithms to detect masks. The main objective of this pro...
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In the agricultural area today, identifying Leaf Disease is very challenging. A significant portion of the economy is dependent on agricultural output, thus when a disease is incorrectly identified, there will be a si...
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images captured in poor lighting conditions (such haze, fog, mist, or smog) have a lower level of visibility because air particles deflect light. Single picture dehazing techniques can restore clarity to a single hazy...
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Deep Learning algorithms have recently become accessible to modern Telecommunication systems due to the advancement in both hardware and software technology. The massively parallel computational tasks of Artificial In...
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Medical image analysis plays a major role in aiding physicians in decision-making. Specifically in detecting COvID-19, Deep Learning (DL) and radiomic approaches have achieved promising results separately. However, DL...
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ISBN:
(纸本)9798350312249
Medical image analysis plays a major role in aiding physicians in decision-making. Specifically in detecting COvID-19, Deep Learning (DL) and radiomic approaches have achieved promising results separately. However, DL results are hard to interpret/visualize, and the radiomic approach encompasses successive steps, such as image acquisition, imageprocessing, segmentation, feature extraction, and analysis. In this paper, we integrate DL with radiomic approaches, aiding in detecting COvID-19. We use DL models to extract 128 relevant deep radiomic features to assess COvID-19 from several image sources of 392 representative chest X-ray (CXR) exams. We avoid successive radiomic steps by employing DL (transfer learning) from imagenet's vGG-16, ResNet50v2, and DenseNet201 networks. We considered a set of Machine Learning (ML) algorithms to further validate our results, providing an ensemble model to detect COvID-19. Our experimental results show that our approach achieved 95% AUC using 128 relevant features from DenseNet201. Conversely, our ensemble model presented 91% AUC, indicating that deep learning-based radiomics could increase binary classification performance in a real scenario. In addition, we highlight that our approach can be adapted to create other DL-based radiomics tools. For reproducibility, we made our code available at https://***/usmarcv/CBMS-DL-based-radiomics.
image inpaintnig in textile manufacturing is a new emerging research topic in preprocessing for jacquard CAD systems. One of the most important aspects of a jacquard CAD system is the simulation of the appearance of a...
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An expert system tailored for foot pressure analysis and imprint detection, with a focus on enhancing podiatric treatments and ergonomics. The system comprises three key components: a foot pressure analyser, an image ...
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This paper explores the enhancement of convolutional imageprocessing on a RISC-v based architecture through the implementation of branch prediction (BP) techniques. Con-volution, a critical operation in image process...
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ISBN:
(数字)9798350350821
ISBN:
(纸本)9798350350838
This paper explores the enhancement of convolutional imageprocessing on a RISC-v based architecture through the implementation of branch prediction (BP) techniques. Con-volution, a critical operation in imageprocessing, is essential for tasks such as edge detection, blurring, and feature extraction. Efficient execution of convolution operations, particularly in a RISC-v architecture, requires effective BP to minimize delays caused by conditional operations. This study presents the development of a 5-stage pipelined 32-bit RISC-v processor, integrated with a 2-bit saturating counter branch predictor using a Branch History Table (BHT). The efficiency of the RISC-v processor was evaluated by synthesizing and implementing it using Xilinx vivado on the Kintex- 7 KC705 Evaluation Platform and testing with a convolution algorithm written in assembly language. Experimental results demonstrate that optimizing BP significantly improves the execution efficiency of convolution operations, making it a promising approach for advanced imageprocessing applications.
Spatial-wise dynamic convolution has become a promising approach to improving the inference efficiency of deep networks. By allocating more computation to the most informative pixels, such an adaptive inference paradi...
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ISBN:
(纸本)9781713871088
Spatial-wise dynamic convolution has become a promising approach to improving the inference efficiency of deep networks. By allocating more computation to the most informative pixels, such an adaptive inference paradigm reduces the spatial redundancy in image features and saves a considerable amount of unnecessary computation. However, the theoretical efficiency achieved by previous methods can hardly translate into a realistic speedup, especially on the multi-core processors (e.g. GPUs). The key challenge is that the existing literature has only focused on designing algorithms with minimal computation, ignoring the fact that the practical latency can also be influenced by scheduling strategies and hardware properties. To bridge the gap between theoretical computation and practical efficiency, we propose a latency-aware spatial-wise dynamic network (LASNet), which performs coarse-grained spatially adaptive inference under the guidance of a novel latency prediction model. The latency prediction model can efficiently estimate the inference latency of dynamic networks by simultaneously considering algorithms, scheduling strategies, and hardware properties. We use the latency predictor to guide both the algorithm design and the scheduling optimization on various hardware platforms. Experiments on image classification, object detection and instance segmentation demonstrate that the proposed framework significantly improves the practical inference efficiency of deep networks. For example, the average latency of a ResNet-101 on the imageNet validation set could be reduced by 36% and 46% on a server GPU (Nvidia Tesla-v100) and an edge device (Nvidia Jetson TX2 GPU) respectively without sacrificing the accuracy. Code is available at https://***/LeapLabTHU/LASNet.
The predominant function of most facial analysis systems revolves around facial alignment and eye tracking, crucial for locating key facial landmarks in images or videos. While developers have access to various models...
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