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Asia-Pacific Signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Tetsuya Asakawa Masashi Hashimoto Takeshi Miyaji Kazuki Shimizu Kei Nomura Masaki Aono Toyohashi University of Technology Aichi Japan Toyohashi Heart Center Aichi Japan

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Semantic segmentation is an image recognition technique that classifies each pixel in an image. This technique is crucial in the fields of automated driving and medicine. Despite its significance, limited computational resources is available for its application. The current challenge involves to developing models that are not only accurate but also lightweight and fast. DDRNet (Deep Dual-Resolution Net) is a real-time segmentation method inspired by HRNet (High-Resolution Net), which is a highly accurate method with a multi-resolution substructure. DDRNet aims to achieve both accuracy and real-time performance reducing the number of resolutions used in the subnetworks and performing feature extraction more efficiently than HRNet. However, DDRNet is less accurate than non-lightweight models, especially in small object recognition. Therefore, this research aimed to improve the accuracy of DDRNet while maintaining its lightweight and high computation speed. We conducted experiments on a coronary calcification dataset using DDR-SAPC-U, which is a modified version of the proposed method specialized for medical imaging with the addition of a skip connection. Overall, DDR-SAPC-U demonstrated the highest accuracy compared to conventional medical methods and was relatively lightweight in terms of computational complexity. Compared to conventional methods, DDR-SAPC-U improved the accuracy of left anterior descending artery calcification by 7.59 points.

关键词： Accuracy Convolution Computational modeling Semantic segmentation Real-time systems Object recognition Computational complexity Spatial resolution Arteries Biomedical imaging

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Regularized Deep Convolutional Encoding-Decoding Generative Network for Remote Sensing Cloud image Compression

Regularized Deep Convolutional Encoding-Decoding Generative ...

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2025 Data Compression conference, DCC 2025

作者： Wang, Yu Yang, Jing Wang, Sijia Yang, Xingli The School of Modern Educational Technology Shanxi University Taiyuan030006 China The School of Mathematics and Statistics Shanxi University Taiyuan030006 China The School of Computer and Information Technology Shanxi University Taiyuan030006 China

ISBN: (纸本)9798331534714

Typically, meteorological cloud observations are often presented in the form of high-quality and high-resolution images, such as satellite remote sensing cloud images and ground-based remote sensing cloud images. However, faced with such a large number of high-resolution images, even for the existing GPU servers and parallel computing with high-speed data processing capabilities, the computational cost of image recognition and analysis is very large, or even unacceptable. A widely used method is to resize the original image to a version with a decreased resolution and to obtain cloud information from this decreased image. Clearly, image compression is crucial. Universal interpolation based image compression methods compress the image size by directly clipping unimportant pixels at the pixel level, which may fail to capture important image feature information such as image structure, resulting in low image recognition performance. In particular, the popular deep encoding-decoding generative network can perfectly reconstruct the image almost identical to the original image by self-supervised learning the useful high-resolution image feature representation. Thus, in this paper, a remote sensing cloud image compression method based on regularized deep convolutional encoding-decoding generation network is proposed by constraining encoding-decoding network and the interpolation method. The proposed method extracts useful feature representations from the original image through an encoding process with a convolutional neural network model, and then reconstructs the compressed image at a specified compression ratio through a decoding process with a deconvolutional neural network model. In the proposed method, a Kullback-Leibler (KL) divergence constraint is introduced to align the feature representation distribution with a standard normal distribution, enhancing the accuracy of the feature representation. Additionally, the regularization constraint between the feature maps o

关键词： image compression

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Research and Design of Remote Management of distributed Airport Security Inspection Equipment Based on Machine Vision

Research and Design of Remote Management of Distributed Airp...

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2021 International conference on Aviation Safety and Information Technology, ICASIT 2021

作者： Chen, Ying Shi, Ruiqing Hainan College of Vocational and Technical Hainan570216 China Haikou Meilan International Airport Security Station Haikou570216 China

ISBN: (纸本)9781450390422

With the rapid increase of domestic airport scale, passenger throughput and the change of international situation, the types and quantity of airport security inspection equipment have increased greatly, and centralized management will greatly improve its management efficiency. However, there are no related products or systems in China at present. This paper studies a distributed remote management system of airport security inspection equipment based on machine vision. The hardware system of acquisition terminal is designed and implemented, and the sensor network of security inspection equipment of single terminal building based on ZigBee technology is established. In this paper, aiming at machine vision and image processing technology, through effective image processing and pattern recognition methods, such as geometric transformation, filtering and denoising, threshold processing, etc., the image is converted into machine-readable form, and then the software program is used to process, analyze and read to obtain the corresponding information, so as to realize card and card reading. The embedded Web Server function is adopted to publish the monitoring platform to the work local area network, which realizes the remote monitoring of security inspection equipment by users in the local area network. © 2021 ACM.

关键词： Airport security

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Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity

Improving Chinese Spelling Check by Character Pronunciation ...

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2022 conference on Empirical methods in Natural Language processing, EMNLP 2022

作者： Li, Jiahao Wang, Quan Mao, Zhendong Guo, Junbo Yang, Yanyan Zhang, Yongdong University of Science and Technology of China Hefei China MOE Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China People's Daily Online Co. Beijing China People's Public Security University of China Beijing China

Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts. As most of these spelling errors are caused by phonetic similarity, effectively modeling the pronunciation of Chinese characters is a key factor for CSC. In this paper, we consider introducing an auxiliary task of Chinese pronunciation prediction (CPP) to improve CSC, and, for the first time, systematically discuss the adaptivity and granularity of this auxiliary task. We propose SCOPE which builds on top of a shared encoder two parallel decoders, one for the primary CSC task and the other for a fine-grained auxiliary CPP task, with a novel adaptive weighting scheme to balance the two tasks. In addition, we design a delicate iterative correction strategy for further improvements during inference. Empirical evaluation shows that SCOPE achieves new state-of-the-art on three CSC benchmarks, demonstrating the effectiveness and superiority of the auxiliary CPP task. Comprehensive ablation studies further verify the positive effects of adaptivity and granularity of the task. Code and data used in this paper are publicly available at https://***/jiahaozhenbang/SCOPE. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Instant water body variation detection via analysis on remote sensing imagery

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JOURNAL OF REAL-TIME image processing 2021年第5期18卷 1577-1590页

作者： Wu, Yirui Han, Pengfei Zheng, Zhan Hohai Univ Coll Comp & Informat Nanjing Peoples R China

Water resource is one of the most valuable natural resources for human beings, which requires to be monitored for careful protection. Inspired by a significant power of machine learning methods, researchers have successfully developed many applications to automatically perform identification on the water body via analyzing remote sensing images. Since a similar category of ground objects could show a large difference in spectral representation, researchers try to propose distinctive and effective features to offer redundant information for category classification. Moreover, large amount of high-resolution remote sensing images require analyzing algorithms to be parallel processed for instant feedback. Based on these requirements, we propose a novel water body variation detection via analysis on remote sensing images. Specifically, the proposed method firstly perform pixel-level classification to locate abnormal changes with thoughts of visual word patterns. Afterwards, the proposed method proposes block division method to construct parallel running version with Mapreduce structure. With high representational and parallel running abilities, the proposed method is capable to accurately detect variation areas on remote sensing images with instant feedback. Experiments on several self-collected datasets show the proposed method has achieved better efficiencies than comparative studies.

关键词： Real-time RS applications of detection and estimation theory Remote sensing images Water body variation detection method distributed processing

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An Effective Automatic Recognition Approach of Chinese Herbal Medicine by Micro image processing

An Effective Automatic Recognition Approach of Chinese Herba...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Guangyao Pang Zhenming Yu Honglin Pan Chengwen Xie Ping Gong Xiaoying Zhu Zizhen Peng Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China

The identification of traditional Chinese medicine is the key to control the quality of traditional Chinese medicine and ensure the safety and effectiveness of clinical medication. Compared with the physical and chemical identification methods with expensive equipment and complex operation, microscopic image identification of traditional Chinese medicine is an effective method with low cost. However, this method still has a high learning cost and identification errors due to staff fatigue. Therefore, this paper designs an effective automatic recognition approach of Chinese herbal medicine by micro image processing. The core of this method is the introduction of transfer learning and data enhancement methods, which effectively alleviates the problem of insufficient number of microscopic image data samples in the microscopic recognition of traditional Chinese medicine, and realizes the automatic recognition of traditional Chinese medicine. We construct a library of microscopic recognition features of Chinese herbal medicine, and designe evaluation experiments on this basis. The results show that the recognition performance of our method is better than that of SSD method, especially the F1 value is increased by 7.25 %.

关键词： Deep learning image recognition Costs Microscopy Transfer learning Ubiquitous computing Raw materials

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SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models 33

SVL-Adapter: Self-Supervised Adapter for Vision-Language Pre...

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33rd British Machine Vision conference Proceedings, BMVC 2022

作者： Pantazis, Omiros Brostow, Gabriel Jones, Kate E. Aodha, Oisin Mac University College London United Kingdom University of Edinburgh United Kingdom Alan Turing Institute United Kingdom Niantic United States

Vision-language models such as CLIP are pretrained on large volumes of internet sourced image and text pairs, and have been shown to sometimes exhibit impressive zero- and low-shot image classification performance. However, due to their size, fine-tuning these models on new datasets can be prohibitively expensive, both in terms of the supervision and compute required. To combat this, a series of light-weight adaptation methods have been proposed to efficiently adapt such models when limited supervision is available. In this work, we show that while effective on internet-style datasets, even those remedies under-deliver on classification tasks with images that differ significantly from those commonly found online. To address this issue, we present a new approach called SVL-Adapter that combines the complementary strengths of both vision-language pretraining and self-supervised representation learning. We report an average classification accuracy improvement of 10% in the low-shot setting when compared to existing methods, on a set of challenging visual classification tasks. Further, we present a fully automatic way of selecting an important blending hyperparameter for our model that does not require any held-out labeled validation data. Code for our project is available here: https://***/omipan/svl_adapter. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Text processing

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distributed algorithms for scalable proximity operator computation and application to video denoising

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DIGITAL SIGNAL processing 2022年第0期128卷

作者： Abboud, Feriel Stamm, Marin Chouzenoux, Emilie Pesquet, Jean-Christophe Talbot, Hugues WITBE Collines Arche Immeuble Opera F-92800 Puteaux La Defense France Inst Polytech Paris ENSTA Paris Palaiseau France Univ Paris Saclay Ctr Visual Comp Cent Supelec Inria Gif Sur Yvette France

Optimization problems arising in signal and image processing involve an increasingly large number of variables. In addition to the curse of dimensionality, another difficulty to overcome is that the cost function usually reads as the sum of several loss or regularization terms, which are non-necessarily smooth and possibly composed with large-size linear operators. Proximal splitting approaches are fundamental tools to address such problems, with demonstrated efficiency in many applicative fields. In this paper, we present a new distributed algorithm for computing the proximity operator of a sum of non-necessarily smooth convex functions composed with arbitrary linear operators. Our algorithm relies on a primal-dual splitting strategy, and benefits from established convergence guaranties. Each involved function is associated with a node of a hypergraph, with the ability to communicate with neighboring nodes sharing the same hyperedge. Thanks to this structure, our method can be efficiently implemented on modern parallel computing architectures, distributing the computations on multiple nodes or machines, with controlled requirements for synchronization steps. Good numerical performance and scalability properties are demonstrated on a problem of video sequence denoising. Our code implemented in Julia is made available at https://github .com /MarinENSTA /distributed_julia_denoising. (C) 2022 Elsevier Inc. All rights reserved.

关键词： Convex optimization Proximal methods distributed algorithm parallel programming Video restoration

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A Strategy of parallel SLIC Superpixels for Handling Large-Scale images over Apache Spark

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REMOTE SENSING 2022年第7期14卷 1568-1568页

作者： Wang, Ning Chen, Fang Yu, Bo Wang, Lei Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci 9 Dengzhuang South Rd Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Hainan Key Lab Earth Observat Sanya 572029 Peoples R China

Superpixel segmentation algorithms are widely used in the image processing field. The size of the large-scale images usually exceeds the memory of a single machine given that the size of image data has increased rapidly in recent years. This leads to big challenges for implementing sequential superpixel segmentation methods, although these algorithms have good scalability. Additionally, segmentation of large-scale images over a distributed cluster is a feasible solution. Nevertheless, it is challenging to transplant sequential superpixel algorithms directly to a distributed environment, as usually there are incomplete object problems in the border area of image tiles. To overcome the incomplete object problems, one approach is to build a distributed strategy based on a sequential SLIC superpixel segmentation algorithm over a distributed cluster organized by Apache Spark. In our research, the decomposed image tiles were divided into two categories-even tiles and odd tiles. The even tiles were first segmented by the SLIC algorithm, then the cluster centers and buffer sizes of even tiles were extracted and switched to odd tiles. During the shuffle stage, the odd tiles acquired pixels from adjacent even tiles according to the buffer sizes, and then the buffered odd tiles were segmented by the SLIC algorithm with the help of the shared cluster centers. The superpixels with shared cluster centers were generated in even tiles and remained in order to enlarge the odd tiles rather than redundant computing of specific areas to modify incomplete superpixels well. Specifically, this strategy employs the shared variables to transmit intermediate results and the shuffle operations were carried out among approximately half of the entire image tiles, which reduces the communications further. The distributed strategy was evaluated in terms of the accuracy and execution efficiency, which revealed that the proposed strategy could not only get better F-measure values but is also implem

关键词： superpixel algorithms distributed computation image processing Apache Spark digital disaster reduction

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Prediction of the Cyanobacteria Coverage in Time-series images based on Convolutional Neural Network 21

Prediction of the Cyanobacteria Coverage in Time-series Imag...

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4th International conference on Control and Computer Vision, ICCCV 2021

作者： Ye, Xiangyu Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Processing Computer College National University of Defense Technology China

ISBN: (纸本)9781450390477

In recent years, the problem of lake eutrophication has become increasingly severe. The monitoring and control of cyanobacteria in lakes are of great significance. The information obtained by existing monitoring methods is relatively lagging, and it is impossible to monitor the sudden outbreak of cyanobacteria in time. Getting cyanobacteria information directly through camera images is a breakthrough. In this paper, after analyzing the characteristics of time series cyanobacteria images, we propose a block prediction scheme based on the CNN model. Experiments show that this method can quickly calculate the coverage of cyanobacteria in the monitoring image in a short time. It can also effectively distinguish cyanobacteria-rich water areas, which significantly facilitates water quality monitoring and cyanobacteria management. We can draw a chart of the changes in the coverage of cyanobacteria by analyzing multi-day time-series images. The chart helps us conduct a short-term water quality analysis to better deal with the outbreak of cyanobacteria. © 2021 ACM.

关键词： Lakes

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