Face recognition stands as a pivotal domain within computervision, boasting myriad applications in security, identification, and biometrics. this research study proposes a face recognition system using image processi...
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Withthe rapid development of sensing technology, a variety of hand rehabilitation assessment systems have emerged as choices for patients engaged in home-based rehabilitation. However, challenges such as fixed assess...
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the need to automate solar cell quality monitoring techniques is growing as solar power utilization gets significant momentum. However, the detection of internal defects in photovoltaic (PV) cells is still quite a cha...
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ISBN:
(纸本)9783031821523;9783031821530
the need to automate solar cell quality monitoring techniques is growing as solar power utilization gets significant momentum. However, the detection of internal defects in photovoltaic (PV) cells is still quite a challenge, highlighting the demand for a reliable method to reduce the requirement for manual inspections. this paper presents a deep learning model to automatically detect and classify defective PV cells in Electroluminescence images using attention-based U-Net image segmentation. the proposed deep learning architecture uses ResNet50 and attention U-Net networks to formulate a set of discriminating image features, providing an effective procedure of deep transfer learning. the extracted image descriptors are encoded and learned from image masks and annotations that were reprocessed to improve the generalization capability of the photovoltaic cell classifier. the semantic deep learning model withimage augmentation and mask processing provides a baseline to detect and classify any possible faults in photovoltaic cells. the experimental results demonstrate the effectiveness of semantic attention U-Net-Resnet50 deep architecture in classifying PV cells, achieving a precision of 95% and an F1-score of 94%. It also achieved an IoU of 90% for PV image segmentation.
In response to the issue that existing human action recognition models can not make full use of complementary information from different modalities, this thesis proposes a multi-path attention module MA to form the MA...
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Self-Supervised Learning (SSL) is a deep learning paradigm, having the capacity to learn from unlabeled datasets. SSL has demonstrated effectiveness in various applications of computervision and imageprocessing. the...
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the fragility of long bones in osteoporosis is linked to a decrease in cortical layer thickness (Cth) and the development of internal porosity (P) within the cortex. this study is dedicated to investigate whether it i...
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this study presents a novel approach for detecting the angles of the rotated rectangles precisely using the hybrid architecture of Convolutional Neural Networks (CNN) with Multi-Layer Perceptron (MLP) and Support Vect...
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Although diffusion models have achieved impressive success for image generation, its application for image restoration is still underexplored. Following tremendous success in natural language processing, transformers ...
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ISBN:
(纸本)9789819985517;9789819985524
Although diffusion models have achieved impressive success for image generation, its application for image restoration is still underexplored. Following tremendous success in natural language processing, transformers have also shown great success for computervision. Although several researches indicate that increasing transformer depth/width improves the applicability of diffusion models, application of Transformers in diffusion models is still underexplored due to quadratic complexity withthe spatial resolution. In this work, we proposed a Transformer-based Denoising Diffusion Probabilistic Model (TransDDPM) for image restoration. With multi-head cross-covariance attention (MXCA), TransDDPM can operates global self-attention with cross-covariance matrix in channel dimension rather than spatial dimension. Another gated feed-forward network (GFFN) is included to enhance the ability to exploit spatial local context. Powered by these designs, TransDDPM is capable for both long-range dependencies and short-range dependencies and flexible for images of various resolutions. Comprehensive experiments demonstrate our TransDDPM achieves state-of-the art performance on several restoration tasks, e.g., image deraining, image dehazing and motion deblurring.
this paper focuses on the classification and recognition of Zhuang ethnic cultural landscape images using Convolutional Neural Network (CNN) technology. As an integral part of China's diverse ethnic culture, Zhuan...
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Withthe continuous advancement of deep learning technology, text-generated images have emerged as a prominent research area. this paper proposes a deep learning-based approach for text image generation, utilizing a g...
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