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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 4813 条记录，以下是491-500 订阅

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Word to Sentence Visual Semantic Similarity for Caption Generation: Lessons Learned 18

Word to Sentence Visual Semantic Similarity for Caption Gene...

引用

18th international conference on Machine Vision and applications (MVA)

作者： Sabir, Ahmed Univ Politecn Cataluna TALP Res Ctr Barcelona Spain

ISBN: (纸本)9784885523434

This paper focuses on enhancing the captions generated by image captioning systems. We propose an approach for improving caption generation systems by choosing the most closely related output to the image rather than the most likely output produced by the model. Our model revises the language generation output beam search from a visual context perspective. We employ a visual semantic measure in a word and sentence level manner to match the proper caption to the related information in the image. This approach can be applied to any caption system as a post-processing method.

关键词： image enhancement

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A Dynamic Dual-processing Object Detection Framework Inspired by the Brain's Recognition Mechanism

A Dynamic Dual-Processing Object Detection Framework Inspire...

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ieee/CVF international conference on computer Vision (ICCV)

作者： Zhang, Minying Bu, Tianpeng Hu, Lulu Alibaba Grp Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798350307184

There are two main approaches to object detection: CNN-based and Transformer-based. The former views object detection as a dense local matching problem, while the latter sees it as a sparse global retrieval problem. Research in neuroscience has shown that the recognition decision in the brain is based on two processes, namely familiarity and recollection. Based on this biological support, we propose an efficient and effective dual-processing object detection framework. It integrates CNN- and Transformer-based detectors into a comprehensive object detection system consisting of a shared backbone, an efficient dual-stream encoder, and a dynamic dual-decoder. To better integrate local and global features, we design a search space for the CNN-Transformer dual-stream encoder to find the optimal fusion solution. To enable better coordination between the CNN- and Transformer-based decoders, we provide the dual-decoder with a selective mask. This mask dynamically chooses the more advantageous decoder for each position in the image based on high-level representation. As demonstrated by extensive experiments, our approach shows flexibility and effectiveness in prompting the mAP of the various source detectors by 3.0 similar to 3.7 without increasing FLOPs.

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Downscaled Representation Matters: Improving image Rescaling with Collaborative Downscaled images

Downscaled Representation Matters: Improving Image Rescaling...

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ieee/CVF international conference on computer Vision (ICCV)

作者： Xu, Bingna Guo, Yong Jiang, Luoqian Yu, Mianjie Chen, Jian South China Univ Technol Guangzhou Peoples R China

ISBN: (纸本)9798350307184

Deep networks have achieved great success in image rescaling (IR) task that seeks to learn the optimal downscaled representations, i.e., low-resolution (LR) images, to reconstruct the original high-resolution (HR) images. Compared with super-resolution methods that consider a fixed downscaling scheme, e.g., bicubic, IR often achieves significantly better reconstruction performance thanks to the learned downscaled representations. This highlights the importance of a good downscaled representation. Existing IR methods mainly learn the downscaled representation by jointly optimizing the downscaling and upscaling models. Unlike them, we seek to improve the downscaled representation through a different and more direct way - directly optimizing the downscaled image itself instead of the down-/upscaling models. Consequently, we propose a Hierarchical Collaborative Downscaling (HCD) method that performs gradient descent w.r.t. the reconstruction loss in both HR and LR domains to improve the downscaled representations, so as to boost IR performance. Extensive experiments show that our HCD significantly improves the reconstruction performance both quantitatively and qualitatively. Particularly, we improve over popular IR methods by >0.57 dB PSNR on Set5. Moreover, we also highlight the flexibility of our HCD since it can generalize well across diverse image rescaling models. The code is available at https://***/xubingna/HCD.

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Distributed Deep Learning for Medical image processing in Cloud Environments 1

Distributed Deep Learning for Medical Image Processing in Cl...

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1st international conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Varshney, Neeraj Madan, Parul Shrivastava, Anurag Kumar, C Praveen Rao, A Kakoli Srivastava, Amit Gla University Department of Computer Engineering and Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

The incorporation of distributed deep learning for medical image processing in cloud settings is the subject of this study. The findings demonstrate the high viability and significant performance advantages realized by cloud-based distributed systems, notably significant processing time savings, outstanding diagnostic accuracy, as well as improved scalability. The consequences for security and privacy have been discussed, with a focus on effective safeguards for private medical information. There is a void in the literature about resource and cost-effectiveness optimization tactics used in cloud-based systems. Future research must concentrate on resource optimization tactics for economic sustainability, study developing security risks and privacy techniques, and incorporate real-world implementations in order to improve this topic. This study informs the use of distributed deep learning in cloud-based medical image processing as well as adds to the body of knowledge in healthcare technology. © 2023 ieee.

关键词： cloud computing Distributed deep learning medical image processing privacy resource optimization security

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2023 12th international conference on image processing Theory, Tools and applications, IPTA 2023

2023 12th International Conference on Image Processing Theor...

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12th international conference on image processing Theory, Tools and applications, IPTA 2023

ISBN: (纸本)9798350325416

The proceedings contain 45 papers. The topics discussed include: skin cancer classification using levy stable based ensemble and it’s real-time implementation on OpenVINO toolkit;effects of region of interest location on osteoarthritis detection using deep feature learning;invariant convolutional networks;an order and difference local binary pattern for hyperspectral texture classification;leveraging transfer learning for analyzing cattle front teat placement;animated lightning bolt generation using machine learning;and CNN ensemble robust to rotation using radon transform.

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computer Three-Dimensional Graphics and image Analysis Technology in Visual Communication Design 2

Computer Three-Dimensional Graphics and Image Analysis Techn...

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2nd ieee international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Li, Chang Dalian Neusoft University of Information Liaoning Dalian116000 China

ISBN: (纸本)9798350347456

In computer 3D graphics analysis technology, we can use image conversion and processing to intuitively convert text into 3D images. This paper introduces computer 3D graphics technology, focuses on the basic principle of image analysis, and proposes a visual communication design based on pixel and texture feature extraction. This paper mainly applies the methods of questionnaire and component analysis to investigate the visual communication effect of people in 3D movies. According to the survey data, more than 80% of people affirmed the effect of 3D image analysis technology in visual communication design. © 2023 ieee.

关键词： Visual communication

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Deep Quantigraphic image Enhancement via Comparametric Equations 48

Deep Quantigraphic Image Enhancement via Comparametric Equat...

引用

48th ieee international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Wu, Xiaomeng Sun, Yongqing Kimura, Akisato Ntt Corporation Communication Science Laboratories Japan Ntt Corporation Computer and Data Science Laboratories Japan

ISBN: (纸本)9781728163277

Most recent methods of deep image enhancement can be generally classified into two types: decompose-and-enhance and illumination estimation-centric. The former is usually less efficient, and the latter is constrained by a strong assumption regarding image reflectance as the desired enhancement result. To alleviate this constraint while retaining high efficiency, we propose a novel trainable module that diversifies the conversion from the low-light image and illumination map to the enhanced image. It formulates image enhancement as a comparametric equation parameterized by a camera response function and an exposure compensation ratio. By incorporating this module in an illumination estimation-centric DNN, our method improves the flexibility of deep image enhancement, limits the computational burden to illumination estimation, and allows for fully unsupervised learning adaptable to the diverse demands of different tasks. © 2023 ieee.

关键词： image enhancement

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Advanced Recursive Spherical Quantization for image Retrieval 20

Advanced Recursive Spherical Quantization for Image Retrieva...

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20th international computer conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2023

作者： Fuchun, Wang Lei, Zhao Chaofeng, Yang Yu, Zhao Sichuan Artificial Intelligence Research Institute Yibin644000 China School of Computer Science and Technology Xinjiang University Urumqi830017 China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

In recent years, quantization technology has proven to be very effective in the field of supervised image retrieval, owing to its capacity to provide both high accuracy and swift retrieval speeds. However, the challenge of balancing retrieval accuracy and speeds, as denoted by variable code lengths, persists as a fundamental consideration across different applications. Recently, there are a few methods that employ recursive strategies for quantization encoding, and there remains substantial room for improvement in their effectiveness. In this paper, we introduce the concept of a Recursive Spherical Quantization (RSQ) architecture, capable of generating sequential binary codes. It can reuse unit vector codebook to recursively quantize real vectors. Empirical findings from benchmark datasets demonstrate that our model is competitive or even superior performance in image retrieval, while simultaneously demanding a significantly reduced number of parameters. © 2023 ieee.

关键词： Vector quantization

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Integrating Haar Wavelet Transform and image processing for Accurate Fire Detection 6

Integrating Haar Wavelet Transform and Image Processing for ...

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6th Iraqi international conference on Engineering Technology and its applications, IICETA 2023

作者： Alattar, Ali Ihsan Altaher, Ammar Wisam. Saad, Aymen Al-Jawahry, Hassan M. Kufa Technical Institute Al-Furat Al-Awsat Technical University Najaf Iraq Technical College of Management Al-Furat Al-Awsat Technical University Department of Information Technology Najaf Iraq The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9798350303339

We discussed the proposed methodology for fire detection, which includes six stages: data acquisition, IoT transmitter, compression, recognition, enhancement, and analysis and evaluation. The methodology was implemented using MATLAB 2021 with specific computer specifications. The study used a webcam to capture a fire image and extracted color pixels from it. The Haar wavelet transform was used to compress the image and detect the red color information. Several image enhancement techniques, such as histogram equalization, Wiener filtering, and median filtering, were used to improve the image's quality. The inverse Haar wavelet transform was used for image recognition and analysis. This methodology can be useful for detecting fires in real-time and can be further improved with more advanced techniques. © 2023 ieee.

关键词： image enhancement

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Sugarcane Classification of image processing and Convolutional Neural Networks 2

Sugarcane Classification of Image Processing and Convolution...

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2nd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Kavitha, K. Umasankari, E. IFET College of Engineering Dept of Computer Science Villupuram India

ISBN: (纸本)9798350348002

Sugarcane is a significant crop with several uses in the food, bio-energy, and bio-based product sectors. Many elements, including climate, soil fertility, and plant diseases, can have an impact on the quality of sugarcane. In this study, it suggest a sugarcane classification system based on convolutional neural networks (CNNs) and image processing to automatically evaluate sugarcane crops' quality automatically. Using image processing techniques, the system uses digital photographs of sugarcane to extract attributes including color, texture, and form. These attributes are then sent into a CNN to be categorized into various grades or quality levels. By testing the proposed system using a sizable collection of photographs of sugarcane, comparing the outcomes to those attained using conventional visual inspection techniques. The findings indicate that the suggested system can classify sugarcane crops with a high degree of accuracy and dependability. The sugarcane industry may employ the suggested approach to increase the efficacy and precision of sugarcane quality evaluation. This research highlights the possibility of fusing deep learning models with image processing methods for agricultural applications. © 2023 ieee.

关键词： Support vector machines

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