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18th IEEE Int Symp on parallel and distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Li, Haocong Cheng, Yunjia Ren, Wei Zhu, Tianqing China Univ Geosci Sch Comp Sci Wuhan Peoples R China Guangxi Key Lab Cryptog & Informat Secur Guilin Peoples R China Chinese Acad Sci CAS Inst Informat Engn Key Lab Network Assessment Technol Beijing 100093 Peoples R China

ISBN: (纸本)9781665414852

Deep learning especially image recognition techniques have been extensively used in various applications such as unmanned driving. The robustness of deep learning models is of critical importance since fault image recognition results may result in serious incidents. In this paper, we propose a fast quantifying method by general image processing to evaluate the robustness of five typical deep learning models under the Keras framework (i.e., VGG16, InceptionV3, ResNet50, DenseNet, and MobileNet). We analyze six metrics in terms of accuracy, precision, recall, F1, recognition time, and impact factor. The evaluation data is publicly accessible image data sets from Kaggle. In our evaluation, the adversary samples are generated by generally image processing methods such as gray-scaling, color-reversing, and image-flipping, which is ordinary operations and easily launched. The different models over various image processing methods are evaluated and compared comprehensively. The evaluation results show that DenseNet performs best over three conditions such as baseline, gray-scaling and horizontal flipping. MobileNet costs the shortest delay in decision over all image processing methods. F1 score varies with different attack intensity. InceptionV3 presents overall robustness in most conditions.

关键词： robustness model evaluation deep learning image recognition

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Review of parallel processing methods for Big image Data Applications

Review of Parallel Processing Methods for Big Image Data App...

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International Conference on Communication, Computing and Electronics Systems, ICCCES 2019

作者： Vigneshwari, K. Kalaiselvi, K. Department of Computer Science VELS Institute of Science Technology and Advanced Studies Chennai600117 India Formerly Vels University Chennai600117 India

ISBN: (纸本)9789811526114

The coexistence of technologies, like big data application, cloud computing, and the numerous images in the Web has paved the need for new image processing algorithms that exploit the processed image for diverse applications. There arises a need for new image processing algorithms to utilize the processed image for diverse applications though many techniques with variations exist. Ultimately, the enduring issue is to enhance the effectiveness of huge image processing and to maintain the combination of the same with recent works. This paper presents a review of the newest progress in researches on parallel processing methods for the processing of big data. Initially, the reviews about the parallel processing methods were carried out by highlighting some promising parallel processing methods in recent studies, such as the representation of MapReduce (MR) framework, distributed, parallel methods, and Hadoop framework. Subsequently, focus on analysis and deliberations about the challenges and promising solutions of parallel computing methods on big data in various applications and on image processing applications were made and concluded with a summary of number of open problems and research areas. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Big data

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Accelerating Deep Learning Tasks with Optimized GPU-assisted image Decoding 26

Accelerating Deep Learning Tasks with Optimized GPU-assisted...

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26th IEEE International Conference on parallel and distributed Systems (IEEE ICPADS)

作者： Wang, Lipeng Luo, Qiong Yan, Shengen HKUST Dept Comp Sci & Engn Hong Kong Peoples R China SenseTime Res Shenzhen Peoples R China

ISBN: (纸本)9781728190747

In computer vision deep learning (DL) tasks, most of the input image datasets are stored in the JPEG format. These JPEG datasets need to be decoded before DL tasks are performed on them. We observe two problems in the current JPEG decoding procedures for DL tasks: (1) the decoding of image entropy data in the decoder is performed sequentially, and this sequential decoding repeats with the DL iterations, which takes significant time;(2) Current parallel decoding methods under-utilize the massive hardware threads on GPUs. To reduce the image decoding time, we introduce a pre-scan mechanism to avoid the repeated image scanning in DL tasks. Our pre-scan generates boundary markers for entropy data so that the decoding can be performed in parallel. To cooperate with the existing dataset storage and caching, systems, we propose two modes of the pre-scan mechanism: a compatible mode and a fist mode. The compatible mode does not change the image file structure so pre-scanned files can be stored back to disk for subsequent DL tasks. In comparison, the fast mode crafts a JPEG image into a binary format suitable for parallel decoding, which can be processed directly on the GPU. Since the GPU has thousands of hardware threads, we propose a fine-grained parallel decoding method on the pre-scanned dataset. The fine-grained parallelism utilizes the GPU effectively, and achieves speedups of around 1.5x over existing GPU-assisted image decoding libraries on real-world DL tasks.

关键词： deep learning image decoding parallel processing heterogeneous processing GPU

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A parallel Unmixing-Based Content Retrieval System for distributed Hyperspectral imagery Repository on Cloud Computing Platforms

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REMOTE SENSING 2021年第2期13卷 176-176页

作者： Zheng, Peng Wu, Zebin Sun, Jin Zhang, Yi Zhu, Yaoqin Shen, Yuan Yang, Jiandong Wei, Zhihui Plaza, Antonio Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China China Satellite Maritime Tracking & Control Dept Jiangyin 214431 Peoples R China Univ Extremadura Dept Technol Comp & Commun Hyperspectral Comp Lab Caceres 10071 Spain

As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.

关键词： content-based image retrieval cloud computing unmixing hyperspectral images

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Towards a SOLAP Based Decision Support System for Forest Fire Management

Towards a SOLAP Based Decision Support System for Forest Fir...

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Earth Observation and Geo-Spatial Information (ICEOGI), International Conference on

作者： Asma Belaroussi Khalissa Derbal Rachid Benabdellah Mostefa Belhadj-Aissa Image Processing and Radiation Laboratory LTIR USTHB Algiers Algeria LSI Laboratory USTHB Algiers Algeria General Directorate of Forestry DGF Algiers Algeria

Risk, suppression, and damage assessment are essential parts of forest fire management. The involved data is distributed and heterogeneous, concerning meteorology, topomorphology, and socio-economy. In addition, it has a multiscale spatiotemporal analysis aspect. These reinforce the need to use the appropriate methods and technologies to enhance decision-making. This research investigates the potential of adopting spatial data warehousing techniques to build a SOLAP-based DSS that supports the whole decisional chain. (i) source collection and integration; (ii) data repository design and population; (iii) SOLAP analytics; and finally, (iv) data visualization in numerous formats, including tabular, graphic, and dynamic maps.

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Focus on Hard Categories and Hard Examples: Remote Sensing image Scene Classification via Expert Model and Hard Example Mining

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Xiong, Yunsheng Zhang, Peng Dou, Yong Xu, Kele Niu, Xin Natl Univ Def Technol Dept Comp Changsha 410005 Peoples R China Key Lab Parallel & Distributed Proc Changsha 410005 Peoples R China

Deep learning has seen dramatic improvements in remote-sensing image scene classification. However, hard categories and hard examples widely exist in the data sets, due to the intraclass diversity and interclass similarity. In this letter, we propose a novel framework to address these issues. Specifically, our method first trains a general model to obtain the confusion matrix and select the hard categories. Then a sampling strategy is proposed to restructure the training set and an expert model is trained to focus on the hard categories. Finally, the knowledge of the expert model is distilled into the student model through a novel loss function, which encourages the student model to predict the hard label provided by manual annotation. Thus, the model can match the soft label provided by the expert model and pay more attention to the hard examples simultaneously. With this method, the student model cannot only deal with hard categories but also hard examples existing in other easy categories. To empirically demonstrate the effectiveness of the proposed method, we comprehensively evaluate the method on three publicly available benchmark data sets, the obtained results show that the proposed method outperforms the existing baseline methods and achieves superior results on all three data sets.

关键词： Training Remote sensing Predictive models Data models Loss measurement Task analysis Feature extraction Expert model hard categories hard examples knowledge distillation (KD) remote sensing scene classification

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Single Node Acceleration of Generative Adversarial Networks using HPC for image Analytics 22

Single Node Acceleration of Generative Adversarial Networks ...

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Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems

作者： Aswathy Ravikumar Harini Sriraman School of Computer Science and Engineering Vellore Institute of Technology India

ISBN: (纸本)9781450397612

Generative Adversarial Networks (GAN) are approaches that are utilized for data augmentation, which facilitates the development of more accurate detection models for unusual or unbalanced datasets. Computer-assisted diagnostic methods may be made more reliable by using synthetic pictures generated by GAN. Generative adversarial networks are challenging to train because too unpredictable training dynamics may occur throughout the learning process, such as model collapse and vanishing gradients. For accurate and faster results the GAN network need to trained in parallel and distributed manner. We enhance the speed and precision of the Deep Convolutional Generative Adversarial Networks (DCGAN) architecture by using its parallelism and executing it on High-Performance Computing platforms. The effective analysis of a DCGAN in Graphic processing Unit and Tensor processing Unit platforms in which each layer execution pattern is analyzed. The bottleneck is identified for the GAN structure for each execution platforms. The Central processing Unit is capable of processing neural network models, but it requires a great deal of time to do it. Graphic processing Unit in contrast, side, are a hundred times quicker than CPUs for Neural Networks, however, they are prohibitively expensive compared to CPUs. Using the systolic array structure, TPU performs well on neural networks with high batch sizes but in GAN the shift between CPU and TPU is huge so it does not perform well.

关键词： Generative Adversarial Network High-Performance Computing Graphic processing Unit Tensor processing Unit

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Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using ivUS Imaging: A Multi-Hospital Collaboration

arXiv

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arXiv 2024年

作者： Hsiao, Chiu-Han Chen, Kai Peng, Tsung-Yu Huang, Wei-Chieh Research Center for Information Technology Innovation Academia Sinica Taipei City Taiwan Department of Information Management National Taiwan University Taipei City Taiwan Division of Cardiology Department of Internal Medicine Taipei Veterans General Hospital School of Medicine National Yang Ming Chiao Tung University Taiwan Department of Biomedical Engineering National Taiwan University Taipei Taiwan

The traditional interpretation of Intravascular Ultrasound (ivUS) images during Percutaneous Coronary Intervention (PCI) is a time-consuming process that often lacks consistency due to its reliance on the expertise and subjective judgment of physicians. Additionally, the collection and integration of ivUS data across multiple hospital systems are significantly constrained by regulatory restrictions and privacy concerns, posing challenges to collaborative analysis and model development. To establish a consistent and efficient method for ivUS image segmentation and enhance plaque detection during PCI procedures, a parallel 2D U-Net model with a multi-stage segmentation architecture has been developed. The model leverages a federated learning algorithm, enabling collaborative data analysis across multiple healthcare institutions while maintaining data privacy. Plaque segmentation is achieved by identifying and subtracting the External Elastic Membrane (EEM) and lumen areas. To optimize processing efficiency, polar coordinates are generated from Cartesian coordinates during the preprocessing stage, ensuring precise segmentation and improving computational performance in distributed medical imaging workflows. The model demonstrates effective plaque identification, achieving a Dice Similarity Coefficient (DSC) of 0.706, and accurately detects circular boundaries in real-time. Through collaboration with domain experts, the ivUS segmentation system provides enhanced plaque burden interpretation via precise quantitative parameter measurements. Future improvements may include integrating advanced federated learning methods to further enhance model performance. This technology is highly adaptable and suitable for environments where sensitive and distributed data are prevalent. Expanding the number of participating institutions and datasets could further optimize outcomes, enabling hospitals to achieve greater success in medical imaging and intervention. © 2024, CC BY-NC-ND.

关键词： image segmentation

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Scalable Deep Learning on distributed Infrastructures: Challenges, Techniques, and Tools

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ACM COMPUTING SURVEYS 2020年第1期53卷 3-3页

作者： Mayer, Ruben Jacobsen, Hans-Arno Tech Univ Munich Boltzmannstr 3 D-85748 Garching Germany

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-art results in various domains, such as image recognition and natural language processing. One of the reasons for this success is the increasing size of DL models and the proliferation of vast amounts of training data being available. To keep on improving the performance of DL, increasing the scalability of DL systems is necessary. In this survey, we perform a broad and thorough investigation on challenges, techniques and tools for scalable DL on distributed infrastructures. This incorporates infrastructures for DL, methods for parallel DL training, multi-tenant resource scheduling, and the management of training and model data. Further, we analyze and compare 11 current open-source DL frameworks and tools and investigate which of the techniques are commonly implemented in practice. Finally, we highlight future research trends in DL systems that deserve further research.

关键词： Deep-learning systems

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Transformer in UAV image-Based Weed Mapping

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REMOTE SENSING 2023年第21期15卷 5165-5165页

作者： Zhao, Jiangsan Berge, Therese With Geipel, Jakob Norwegian Inst Bioecon Res NIBIO Dept Agr Technol POB 115 NO-1431 As Norway Norwegian Inst Bioecon Res NIBIO Dept Invertebrate Pests & Weeds Forestry Agr & Hort POB 115 NO-1431 As Norway

Weeds affect crop yield and quality due to competition for resources. In order to reduce the risk of yield losses due to weeds, herbicides or non-chemical measures are applied. Weeds, especially creeping perennial species, are generally distributed in patches within arable fields. Hence, instead of applying control measures uniformly, precision weeding or site-specific weed management (SSWM) is highly recommended. Unmanned aerial vehicle (UAV) imaging is known for wide area coverage and flexible operation frequency, making it a potential solution to generate weed maps at a reasonable cost. Efficient weed mapping algorithms need to be developed together with UAV imagery to facilitate SSWM. Different machine learning (ML) approaches have been developed for image-based weed mapping, either classical ML models or the more up-to-date deep learning (DL) models taking full advantage of parallel computation on a GPU (graphics processing unit). Attention-based transformer DL models, which have seen a recent boom, are expected to overtake classical convolutional neural network (CNN) DL models. This inspired us to develop a transformer DL model for segmenting weeds, cereal crops, and 'other' in low-resolution RGB UAV imagery (about 33 mm ground sampling distance, g.s.d.) captured after the cereal crop had turned yellow. images were acquired during three years in 15 fields with three cereal species (Triticum aestivum, Hordeum vulgare, and Avena sativa) and various weed flora dominated by creeping perennials (mainly Cirsium arvense and Elymus repens). The performance of our transformer model, 1Dtransformer, was evaluated through comparison with a classical DL model, 1DCNN, and two classical ML methods, i.e., random forest (RF) and k-nearest neighbor (KNN). The transformer model showed the best performance with an overall accuracy of 98.694% on pixels set aside for validation. It also agreed best and relatively well with ground reference data on total weed coverage, R2 = 0.598. I

关键词： 1Dtransformer perennial weed mapping low-resolution UAV image

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