The exponential growth of technological advancements in satellite and airborne remote sensing is giving rise to large volumes of high-dimensional hyperspectral image data. Apache Spark is one of the most popular, exte...
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ISBN:
(数字)9798331531539
ISBN:
(纸本)9798331531546
The exponential growth of technological advancements in satellite and airborne remote sensing is giving rise to large volumes of high-dimensional hyperspectral image data. Apache Spark is one of the most popular, extensively used and open-source distributedprocessing frameworks, which is proven effective in processing large volumes of remotely sensed hyperspectral images in a time-efficient manner. Open-source distributedprocessing frameworks have proven effective in processing large volumes of remotely sensed hyperspectral images quickly and efficiently. While computational power has been increasing, the rate of data accumulation is more than the processing capabilities. Therefore, more efficient algorithms such as dimensionality reduction are needed to process and get accurate performance for the application. This paper proposes an efficient and parallel spectral dimensionality reduction approach based on feature partitioning principal component analysis called scalable SubXPCA. We implemented scalable SubXPCA on a spark cluster distributed environment. We compared scalable SubXPCA against other distributed feature partitioning and various non-feature partitioning dimensionality reduction methods. Our experiments on different real and synthetic datasets of hyperspectral images confirm that SubXPCA classification performance is not only better than its competitors but also that the running time of SubXPCA is better in distributedprocessing than serial processing. As the size of the hyperspectral image dataset increased, SubXPCA showed a speed up factor of 5.7× and more in the spark cluster compared to the serial version.
This publication presents a digital image correlation (DIC) based technique applied to a shear test on a carbon-reinforced concrete member. DIC methods are based on image sequences where the first image is recorded un...
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Adversarial attacks are now becoming quite a dangerous means of disrupting imageprocessing systems that use machine learning methods for decision making. Therefore, developing effective countermeasures against advers...
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Medical image AI Systems can assist doctors in making diagnoses, thereby improving diagnostic accuracy. These systems are now widely used in hospitals. However, current AI diagnostic methods typically rely on various ...
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ISBN:
(数字)9798350365856
ISBN:
(纸本)9798350365863
Medical image AI Systems can assist doctors in making diagnoses, thereby improving diagnostic accuracy. These systems are now widely used in hospitals. However, current AI diagnostic methods typically rely on various deep learning technologies, which require substantial computational resources. When diagnostic demands surge, traditional monolithic architectures may suffer from low computational performance and queue congestion. To address these issues, this paper compares and analyzes two system architectures based on parallel computing and distributed computing. The first one is a coarse-grained multi-service instance architecture, which uses clustering to expand the system's service instances, though it still presents some challenges. The second is a fine-grained workflow-based distributed architecture, which abstracts the diagnostic process into a workflow divided into several subtasks managed and scheduled by the cluster. This architecture demonstrates advantages in several aspects. Finally, this paper implements a Medical image AI System for pulmonary fibrosis diagnosis based on the workflow-based distributed system architecture.
Deep learning based approaches have been extensively used for the image classification task with improved accuracy and lower computational complexity. The deep learning approaches have shown outstanding performances w...
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ISBN:
(数字)9798331521349
ISBN:
(纸本)9798331521356
Deep learning based approaches have been extensively used for the image classification task with improved accuracy and lower computational complexity. The deep learning approaches have shown outstanding performances when a huge amount of datasets are available to train the model. However, in medical image analysis, sufficient data is not available to train deep learning models. To overcome these issues, a new approach called transfer learning has been introduced for the medical image classification task. This approach has shown outstanding performances with less computational complexity. In the case of transfer learning approaches, the knowledge acquired by training the model with a non-medical image dataset can be transferred to the target dataset. This paper presents a comparative study on the application of transfer learning techniques for the medical image analysis task. This paper demonstrated a complete analysis of the architecture and performance of numerous pre-trained models on the imageNet dataset. Further, This paper presents the significance of the transfer learning techniques for medical imageprocessing tasks for example brain tumor detection, pneumonia classification, and COVID-19 detection by analyzing various state-of-the-art methods. The experimental results indicate that the performance of transfer learning-based methods can be improved by fine-tuning these models using medical-specific datasets.
Due to dark environments, optical aberrations, etc, the remote sensing images are often submerged under low contrast degradation, which greatly hinders their practical applications for agricultural management and othe...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Due to dark environments, optical aberrations, etc, the remote sensing images are often submerged under low contrast degradation, which greatly hinders their practical applications for agricultural management and other related tasks. The surface features of remote sensing images are often continuously distributed in space, thus, the sizes of the network’s receptive fields and its ability to learn long-range dependencies are crucial for restoring low-light remote sensing images. Existing methods based on CNN provide limited receptive fields, while Transformer-based methods are constrained by their quadratic computational complexity. To cope with these issues, we propose a novel low-light remote sensing image enhancement network that combines multi-scale receptive fields with frequency-domain attention. Specifically, this network employs multiple parallel kernels of varying sizes to learn multi-scale local features in the spatial domain and complements frequency-domain information to learn global long-range correlations, which achieves local-global feature extraction and further facilitates subsequent degraded images enhancement. We have conducted extensive experiments to demonstrate that our network outperforms existing methods quantitatively and achieves exceptional visual performance, which fully highlights the effectiveness and superiority of our method in enhancing low-light remote sensing images.
Docker image storage systems, like Docker registry, often employ deduplication to reduce storage overhead. Existing deduplication methods for these systems detect redundancy at either layer or file level, each with it...
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In imageprocessing, "image fusion" is the amalgamation of attributes and requirements from many images into a singular, more comprehensive representation. Multi-modal medical image fusion is a significant c...
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ISBN:
(数字)9798331521349
ISBN:
(纸本)9798331521356
In imageprocessing, "image fusion" is the amalgamation of attributes and requirements from many images into a singular, more comprehensive representation. Multi-modal medical image fusion is a significant category of image fusion. It entails the integration of medical images acquired from several modalities. This work utilizes computed tomography (CT) scans, Positron Emission Tomography (PET) and magnetic resonance imaging (MRI) as modalities. This study (M3IF-SBTS) aims to construct a multi modal medical image approach that combines Optimal Sub-band Tree Structuring (SBTS) and Principal Component Analysis (PCA) with MRI and CT images in a manner that maximizes the information content in the fused image. The SBTS is an advanced wavelet transform version of Discrete Wavelet Transform (DWT), where signal is filtered more times. The PCA provides dimensionality reduction and retains the relevant features. The wavelet coefficients are fused using PCA-based fusion method. This combination of SBTS and PCA provides superior results compared to using SBTS, DWT, or PCA as individual methods. This improves overall visual and parametric quality of fusion results than many compared methods.
In this paper, we develop an Attention based Generative Adversarial Networks (AGAN) to augment image data for the purpose of robust training for efficient processing of the hyper spectral imaging. The AGAN model enabl...
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ISBN:
(数字)9798331521349
ISBN:
(纸本)9798331521356
In this paper, we develop an Attention based Generative Adversarial Networks (AGAN) to augment image data for the purpose of robust training for efficient processing of the hyper spectral imaging. The AGAN model enables generation of images from the sample images that helps in training the classifier and in this study a fundamental classifier namely a convolutional neural network is used. A robust training is conducted to test the accuracy of detecting the instances effectively using the dataset. The simulation shows that the proposed AGAN-CNN attains improved accuracy after robust training than the existing methods.
image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignm...
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ISBN:
(数字)9798331515966
ISBN:
(纸本)9798331515973
image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processingimages, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method.
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