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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4812 条记录，以下是441-450 订阅

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Comparative Study of Implementation of Very Deep Super Resolution Neural Network and Bicubic Interpolation for Single image Super Resolution Quality Enhancement 3

Comparative Study of Implementation of Very Deep Super Resol...

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3rd ieee international conference on Applied Electromagnetics, Signal processing, and Communication, AESPC 2023

作者： Ganesan, P. Ravichandran, M. Sathish, B.S. Joseph, L.M.I.Leo Sajiv, G. Murugesan, R. Vidya Jyothi Institute of Technology Dept. of Electronics and Communication Engineering Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Sri Venkateshwara College of Engineering Dept. of Computer Science Engineering Ai Bengaluru India Sr University Dept. of Electronics and Communication Engineering Telangana Warangal India Saveetha School of Engineering Simats Chennai India Narshima Reddy Engineering College Dept. of Electronics and Communication Engineering Secunderabad India

ISBN: (纸本)9798350358742

To improve the resolution and quality of a low level image, single image super resolution (SISR) is a challenging endeavor in the turf of computer vision. It is essential to many applications, including image editing, security systems, medical imaging, and others. In order to increase the spatial quality of a low level image, SISR attempts to approximate the high frequency details that are missing from the image. The field was formerly based on manually created features and interpolation techniques, but current developments in deep learning have completely changed it. The suggested work compares the effectiveness of bicubic interpolation and neural network based very deep super resolution for single image super resolution image quality improvement. Utilizing blind and complete reference picture quality measures, the effectiveness of the two approaches is evaluated. The effectiveness of the very deep super resolution for single image is clearly demonstrated by the experimental results. © 2023 ieee.

关键词： Medical imaging

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US-Net: A Breast Ultrasound image Segmentation using Deep Learning 15

US-Net: A Breast Ultrasound Image Segmentation using Deep Le...

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15th ieee/ACM Annual international conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： Erragzi, Nouhaila Zrira, Nabila Jimi, Anwar Benmiloud, Ibtissam Sebihi, Rajaa Ngote, Nabil Mohammed V Univ Fac Sci Dept Phys Rabat Morocco Natl Super Sch Mines LISTD Lab ADOS Team Rabat Morocco Natl Super Sch Mines Lab CPS2E MECAtron Team Rabat Morocco Natl Super Sch Mines Abulcasis Int Univ Hlth Sci Rabat Morocco

ISBN: (纸本)9798400704093

Segmentation of medical images is a crucial step in many clinical applications, including the precise diagnosis and treatment of diseases like breast cancer. Therefore, automated segmentation of breast tumors from breast ultrasound images remains a challenging task. In this paper, we developed a new model, called Ultrasound Network (US-Net), which uses the U-Net architecture with attention gates embedded in the skip connections to assign weights to feature maps based on their importance for the segmentation task. Our method underwent evaluation on three public datasets: BUSI, UDIAT, and STUHospital, using the Dice coefficient as the primary metric for segmentation performance. Notably, US-Net achieved impressive Dice coefficients of 86.99%, 94.38%, and 94% on BUSI, UDIAT, and STUHospital, respectively. Experimental results showed that our network outperformed the latest image segmentation methods for lesion segmentation in breast ultrasound.

关键词： Breast Ultrasound image Pre-processing Segmentation Deep learning U-Net Attention mechanism

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Plain Source Code Obfuscation as an Effective Attack Method on IoT Malware image Classification 47

Plain Source Code Obfuscation as an Effective Attack Method ...

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47th ieee-computer-Society Annual international conference on computers, Software, and applications (COMPSAC)

作者： Sato, Hayato Immure, Hiroshi Ishida, Shigemi Nakamura, Yoshitaka Future Univ Hakodate Sch Syst Informat Sci Hakodate Japan Kyoto Tachibana Univ Fac Engn Kyoto Japan

ISBN: (纸本)9798350326970

IoT malware is rapidly increasing due to variants easily generated from publicly available source codes. Malware image classification capable of fast and accurate malware identification attracts attention. Since the classification by imaging is affected by malware binary changes, a binary modification without behavioral changes can be a potential attacking method to the classification by imaging. There are concerns that by combining the publicly available malware source code with readily available source code obfuscation tools, it is possible to construct an effective attack that bypasses image classifiers relatively simply. In this study, we show the effectiveness of the attack by source code obfuscation and the possibility of defense against the attack. The contribution of this research is twofold. 1) We showed that Obfuscator-LLVM (oLLVM) code obfuscation could be used as an attack method on malware image classification. The obfuscated malware binaries made by oLLVM were misclassified by VGG16-based image classifier for all the attacked malware families including Mirai, Lightaidra, and Bashlite. 2) We showed that classifier training with obfuscated samples could address this attack method. We confirmed that the malware image classifier trained with obfuscated malware binaries made by oLLVM could classify with an accuracy of 100% the malware family with obfuscation as the obfuscated original malware family.

关键词： Malware image Classification Code Obfuscation IoT Malware

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Interactive image Segmentation with Cross-Modality Vision Transformers

Interactive Image Segmentation with Cross-Modality Vision Tr...

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ieee/CVF international conference on computer Vision (ICCV)

作者： Li, Kun Vosselman, George Yang, Michael Ying Univ Twente Enschede Netherlands

ISBN: (纸本)9798350307443

Interactive image segmentation aims to segment the target from the background with the manual guidance, which takes as input multimodal data such as images, clicks, scribbles, polygons, and bounding boxes. Recently, vision transformers have achieved a great success in several downstream visual tasks, and a few efforts have been made to bring this powerful architecture to interactive segmentation task. However, the previous works neglect the relations between two modalities and directly mock the way of processing purely visual information with self-attentions. In this paper, we propose a simple yet effective network for click-based interactive segmentation with cross-modality vision transformers. Cross-modality transformers exploit mutual information to better guide the learning process. The experiments on several benchmarks show that the proposed method achieves superior performance in comparison to the previous state-of-the-art models. In addition, the stability of our method in term of avoiding failure cases shows its potential to be a practical annotation tool. The code and pretrained models will be released under https: //***/lik1996/iCMFormer.

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HIERARCHICAL CONDITIONAL SEMI-PAIRED image-TO-image TRANSLATION FOR MULTI-TASK image DEFECT CORRECTION ON SHOPPING WEBSITES 30

HIERARCHICAL CONDITIONAL SEMI-PAIRED IMAGE-TO-IMAGE TRANSLAT...

引用

30th ieee international conference on image processing (ICIP)

作者： Li, Moyan Fu, Jinmiao Xu, Shaoyuan Liu, Huidong Liu, Jia Wang, Bryan Univ Michigan Ann Arbor MI 48109 USA Amazon Seattle WA USA

ISBN: (纸本)9781728198354

On shopping websites, product images of low quality negatively affect customer experience. Although there are plenty of work in detecting images with different defects, few efforts have been dedicated to correct those defects at scale. A major challenge is that there are thousands of product types and each has specific defects, therefore building defect specific models is unscalable. In this paper, we propose a unified image-to-image (I2I) translation model to correct multiple defects across different product types. Our model leverages an attention mechanism which hierarchically incorporates high-level defect groups and specific defect types to guide the network to focus on defect-related image regions. Evaluated on eight public datasets, our model reduces the Frechet Inception Distance (FID) by 24.6% in average compared with MoNCE, the state-of-the-art I2I method. Another practical challenge on shopping websites is the lack of high quality paired images. We extend our model to be semi-paired by leveraging both paired and unpaired data. Tested on a shopping website dataset to correct three image defects, our model reduces (FID) by 63.2% in average compared with WS-I2I, the state-of-the art semi-paired I2I method.

关键词： image to image translation computer Vision image defect auto-correction

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Intelligent English Recognition System Based on computer image Algorithm

Intelligent English Recognition System Based on Computer Ima...

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2023 ieee international conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Chen, Ou Chengdu Neusoft University Chengdu China

ISBN: (纸本)9798350398465

With the acceleration of the global informatization process, improving the efficiency of information processing has become a top priority. The development of science and technology has made the information processing methods advance by leaps and bounds, and the trend of 'electronic information' attracts the participation of all walks of life. This paper aims to study the intelligent English recognition system based on computer image algorithm. This paper firstly gives a brief overview of the current situation and development of intelligent English recognition technology. In view of the current demand for accurate and fast English recognition on the market, the system can automatically distinguish and recognize input images and extract image information. This paper first describes the proposed system from three parts: image preprocessing, document image retrieval, preliminary analysis, and information extraction. In this case, angle detection is faster and can recognize English words more accurately. Experiments show that the combined feature extraction algorithm in this paper has an effective extraction accuracy rate of 80% or a lie, and the optimal learning initial rate is 0.01. © 2023 ieee.

关键词： Informatization

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An image encryption algorithm based on multi-chaotic systems and DNA 2

An image encryption algorithm based on multi-chaotic systems...

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2nd international conference on image processing, computer Vision and Machine Learning, ICICML 2023

作者： Shi, Jiawei Xiong, Feng Shanghai University School of Mechatronic Engineering and Automation Shanghai China

ISBN: (纸本)9798350331417

In this research, we provide a method for encrypting digital images that is based on DNA block encoding and multi-chaotic systems. First, create a scrambling matrix using the one-dimensional logistic chaotic map, then split both the plain image matrix and the scrambling matrix into sub-blocks of the same size. Next, for DNA encoding, decoding, and operation between the respective sub-blocks, four chaotic sequences produced by Lorenz hyperchaotic system are employed. We may finally obtain the cipher picture using the two-dimensional logistic map of the scrambled image. The technique in this study performs well in simulation studies for statistical analysis, sensitivity analysis, and robustness analysis. As a result, the encryption method can withstand many typical assaults and provide the level of security required. © 2023 ieee.

关键词： component DNA encoding image encryption Logistic chaotic map

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Dual-Contrastive Dual-Consistency Dual-Transformer: A Semi-Supervised Approach to Medical image Segmentation

Dual-Contrastive Dual-Consistency Dual-Transformer: A Semi-S...

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ieee/CVF international conference on computer Vision (ICCV)

作者： Wang, Ziyang Ma, Congying Univ Oxford Dept Comp Sci Oxford England Univ Bath Dept Comp Sci Bath Avon England

ISBN: (纸本)9798350307443

Medical image segmentation serves as a crucial underpinning for a myriad of clinical applications. The advent of deep learning techniques has significantly propelled advancements in this field. However, challenges persist due to the limited availability of labelled medical imaging data and the substantial cost of data annotation. This paper introduces a novel semi-supervised learning strategy, amalgamating pseudo-labelling and contrastive learning with a consistency regularization framework. This innovative approach incorporates a modified contrastive learning strategy and a confidence-aware pseudo-labeling strategy, both of which are integrated into a dual-segmentation network ensemble learning structure. Inspired by the recent success of self-attention mechanisms, we harness the power of the Vision Transofmer(ViT) within our proposed semi-supervised framework, and conduct a comprehensive comparison among various combinations of ViT and Convolutional Neural Network(CNN) with the proposed strategy. The efficacy of our proposed method is validated using a publicly available medical image segmentation dataset, where it demonstrates state-of-the-art performance against established methods. The proposed method, all baseline methods, and dataset are available at https://***/ziyangwang007/CV-SSL-MIS.

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Implementation of Underwater image Enhancement for Corrosion Pipeline Inspection (UIECPI) 19

Implementation of Underwater Image Enhancement for Corrosion...

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19th ieee international Colloquium on Signal processing and Its applications, CSPA 2023

作者： Syamsul Amri, S.Q. Abdul Ghani, A.S. Kamarul Baharin, M.A.S. Universiti Malaysia Pahang Pekan Faculty of Manufacturing and Mechatronic Engineering Technology Pahang Malaysia

ISBN: (纸本)9781665476928

The corrosion penetration rate (CPR) during crude oil transportation procedures or gas transportation by carbon steel pipelines is one of the most important critical issue problems for any oil and gas sector today. Several studies have been conducted on these topics using various methods. The major purpose of this research is to use computer vision concepts which is underwater image enhancement for corrosion pipeline inspection to develop a robust and capable model that can accurately detect corrosion using certain algorithms and operating parameters. A reliable algorithm is developed to enhance the input images. The results from this detection model showed that, with small set of examples image, the underwater image enhancement for corrosion pipeline inspection (UIECPI) was able readily distinguished. © 2023 ieee.

关键词： image enhancement

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Natural language processing for the automation of operations in an online bookstore 19

Natural language processing for the automation of operations...

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19th ieee international conference on Intelligent computer Communication and processing conference, ICCP 2023

作者： Iorga, Elena Dumitriu, Tiberius Gheorghe Asachi' Technical University of Iaşi Faculty of Automatic Control and Computer Engineering Iaşi Romania

ISBN: (纸本)9798350370355

The use of Natural Language processing Algorithms (NLP) for automation purposes in various applications is frequently encountered recently. Some research managed to identify the dominant emotion from a text using neural networks (ANN), Random Forest (RF) and Support Vectors Machines (SVM) while other studies classified documents with the aim of mechanizing the extraction process of *** paper presents a study on different Natural Language processing Algorithms (NLP) used for automation of a virtual bookstore. The objective was the application of artificial intelligence algorithms to streamline the necessary processes of an online bookstore or library, taking into consideration the short descriptions and summaries of the books. The aim is to improve the results obtained by supervised algorithms using various techniques such as aggregating unsupervised classifications. In order to boost their understanding of natural language, GPT is used to enhance the dataset by adding additional context. Finally, some of the methods utilized in improving accuracy can be used to create a personalized recommendation system that suits each reader's needs. © 2023 ieee.

关键词： Automation

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