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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

共 691 条记录，以下是231-240 订阅

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LANGUAGE-ORIENTED COMMUNICATION WITH SEMANTIC CODING AND KNOWLEDGE DISTILLATION FOR TEXT-TO-image GENERATION 49

LANGUAGE-ORIENTED COMMUNICATION WITH SEMANTIC CODING AND KNO...

引用

49th IEEE international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Nam, Hyelin Park, Jihong Choi, Jinho Bennis, Mehdi Kim, Seong-Lyun Yonsei Univ Seoul South Korea Deakin Univ Geelong Vic Australia Univ Oulu Oulu Finland

ISBN: (纸本)9798350344868;9798350344851

By integrating recent advances in large language models (LLMs) and generative models into the emerging semantic communication (SC) paradigm, in this article we put forward to a novel framework of language-oriented semantic communication (LSC). In LSC, machines communicate using human language messages that can be interpreted and manipulated via natural language processing (NLP) techniques for SC efficiency. To demonstrate LSC's potential, we introduce three innovative algorithms: 1) semantic source coding (SSC) which compresses a text prompt into its key head words capturing the prompt's syntactic essence while maintaining their appearance order to keep the prompt's context;2) semantic channel coding (SCC) that improves robustness against errors by substituting head words with their lenghthier synonyms;and 3) semantic knowledge distillation (SKD) that produces listener-customized prompts via in-context learning the listener's language style. In a communication task for progressive text-to-image generation, the proposed methods achieve higher perceptual similarities with fewer transmissions while enhancing robustness in noisy communication channels.

关键词： Semantic communication (SC) large language model (LLM) generative model

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Multi-modal Medical image Fusion Technique to Improve Glioma Classification Accuracy 6

Multi-modal Medical Image Fusion Technique to Improve Glioma...

引用

6th international conference on signal and image processing, ICSIP 2021

作者： Ullah, Hikmat Zhao, Yaqin Wu, Longwen Noor, Alam Zhao, Liang Harbin Institute of Technology School of Electronics and Information Engineering Harbin150001 China Institute of Industrial and Electrical Testing China Railway Harbin Group Co. Ltd. Harbin150001 China

ISBN: (纸本)9780738133737

Usually, the low grade (LGG) and high-grade glioma (HGG) classification algorithms proposed in the literature directly concatenate the magnetic resonance image (MRI) modalities that directly affect the accuracy and precision results. Therefore, here this problem is highlighted at the initial level by applying a multi-modality fusion scheme. First, multi-level edge-preserving filtering (MLEPF) is applied to decompose the source images into fine-structure (FS), coarse-structure (CS), and base (BS) layers. Then, the novel sum-modified Laplacian (NSML) and parameter Adaptive PCNN based fusion strategy is adopted for the fusion FS and CS layers. Where region energy (RE) and information entropy (IE) based fuzzy pixel rules are implemented for the fusion of BS layers. The final fused image is achieved by integrating all the three fused layers. Visual and quantitative analysis prove that the proposed scheme results are satisfactory compare to the state-of-art. The results were also evaluated by using the Google Inception V3 convolutional neural network (CNN). © 2021 IEEE.

关键词： Fuzzy logic

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High-Performance image Visual Feature Detection and Matching

High-Performance Image Visual Feature Detection and Matching

引用

international conference on Communications, signal processing and VLSI, IC2SV 2019

作者： Sambamurthy, N. Kamaraju, M. Department of ECE Gudlavalleru Engineering College Gudlavalleru India

ISBN: (纸本)9789813340572

The fundamental task in high-speed and high-accuracy CCTV video surveillance system is attention-based image visual point detection and matching. The image viewpoints are different at different instants of time, so that the image recognition and matching systems are having large computational complexity. The designed work is to present an ROI-based SIFT image detection and BRIEF matching algorithms with a processing speed of 44 ns, and it meets the real-time requirement. The performance of the designed system is improved with a hybrid sorting-based median filter. These algorithms are able to establish accurate feature extraction and matching at 30fps for 640 Pixel video. This can be implemented using MATLAB and Xilinx tools. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Median filters

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SEMANTIC-ENHANCED SUPERVISED CONTRASTIVE LEARNING 49

SEMANTIC-ENHANCED SUPERVISED CONTRASTIVE LEARNING

引用

49th IEEE international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhang, Pingyue Wu, Mengyue Yu, Kai Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Contrastive learning has significantly advanced research on enhancing data utilization and improving representation learning. Supervised contrastive learning has demonstrated the benefits of incorporating label information into the learning process. Building upon this foundation, we propose Semantic-Enhanced Supervised Contrastive Learning of Representation (SECLR), which not only leverages label information but also incorporates conceptual semantics to guide the selection of positive and negative samples. Our approach also diverges from traditional supervised contrastive learning by introducing semantic similarity scores as additional weights in the loss function design. This allows us to better distinguish the degrees of positive and negative relationships. We validate the performance of SECLR on benchmark datasets, including imagenet, VGGSound, and imagenet-100. Our results show a significant performance boost. Furthermore, we conduct a detailed analysis of SECLR using different configurations.

关键词： Audio classification image classification contrastive learning

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Application of CLIP on advanced GAN of Zero-Shot Learning

Application of CLIP on Advanced GAN of Zero-Shot Learning

引用

2021 international conference on signal processing and Machine Learning, CONF-SPML 2021

作者： Li, Peize Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao China

ISBN: (纸本)9781665417341

In recent years, deep learning models have achieved world-renowned achievements in the fields of image, speech and text recognition. However, the insufficient amount of labeled data has brought serious problems, and it is also difficult to identify unseen classes well. Therefore, if we want to achieve perfect recognition of unseen classes, we need to perform zero-shot learning. In order to solve the zero-shot learning problem, a better solution can be obtained by using the semantic space method. Zero-shot learning attempts to classify unseen data after learning the seen data. In this case, it is one of the most difficult learning methods to achieve perfect recognition. CLIP uses a data set of 400 million data pairs, resulting in higher efficiency and better robustness. Using the features obtained by traditional RESNET neural network and CLIP, two advanced methods, F-CLSWGAN and TF-VAEGAN, were tested. Through ZSL and GZSL experiments, excellent results have been achieved and the effectiveness of the combined method has been verified. This paper has tested the good effect of the application of CLIP on ZSL and GZSL. The experimental results show that CLIP has excellent performance on the AWA2 data set, whether it is using F-CLSWGAN or TF-VAEGAN. Among them, the effect of TF-VAEGAN is better. © 2021 IEEE.

关键词： image classification

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Recent Progress in Low Light image Enhancement algorithms Based on Deep Learning 16

Recent Progress in Low Light Image Enhancement Algorithms Ba...

引用

16th international conference on Digital image processing, ICDIP 2024

作者： Wang, Wencheng Yin, Boxin Li, Lun Wu, Xiaojin Li, Lei Liu, Hongtao He, Weikai Gao, Yuan Lan, Xiaowei Weifang University College of Machinery and Automation Weifang261061 China Qufu Normal University College of Engineering Rizhao276827 China School of Astronautics Shandong Jiaotong University China

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

In low light environments, the collected images have characteristics such as low contrast, low signal-to-noise ratio, and loss of details, leading to an overall decline in image quality. Low light image enhancement aims to restore images with complete details and has gradually become a research hotspot in computer image processing. With the large-scale increase in data volume in recent years, deep learning based methods have gradually become mainstream. This article provides a detailed classification and analysis of low light image enhancement methods based on deep learning, sorts out various networks used, introduces the basic principles and steps of various algorithms, and introduces the existing low light image enhancement datasets and evaluation methods. Finally, a summary of the content was made, pointing out the difficulties in current research and providing prospects for future research directions. © 2024 SPIE.

关键词： image enhancement Deep learning image processing Convolutional neural networks

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Simple Linear Iterative Clustering with Efficiency 1

引用

17th international conference on Intelligent Information Hiding and Multimedia signal processing (IIH-MSP) / 14th international conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Liao, Nannan Liu, Hui Li, Cheng Ren, Xia Guo, Baolong Xidian Univ Inst Intelligent Control & Image Engn Xian Peoples R China

ISBN: (数字)9789811910579

ISBN: (纸本)9789811910579;9789811910562

As an instructive work to generate satisfactory superpixels, simple linear iterative clustering (SLIC), has become fundamental and popular in various computer vision tasks. In this work, the algorithm is reconsidered, and an integrated framework is proposed to further improve the running speed and segmentation performance. In the first stage, a fast convergence strategy for clustering is presented on SLIC to redistribute the seeds efficiently. This is done to initialize a set of clustering centers that is fairly representative of local information on the image plane. Then, a followup work of SLIC termed simple non-iterative clustering (SNIC) is utilized to process more accurate segmentation without any post-processing to enforce connectivity. Experimental results show that the framework could generate a synergetic effect and performs better than previous superpixel algorithms in a limited computational time.

关键词： Clustering Initialization Redistribution Superpixels

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Progress in image processing, Pattern Recognition and Communication Systems 1

引用

丛书名： Lecture Notes in Networks and Systems

1000年

作者： Michal Choraś Ryszard S. Choraś Marek Kurzyński Paweł Trajdos Jerzy Pejaś Tomasz Hyla

ISBN: (数字)9783030815233

ISBN: (纸本)9783030815226

This book presents a collection of high-quality research papers accepted to multi-conference consisting of international conference on image processing and Communications (IP&C 2021), international conference on Computer Recognition Systems (CORES 2021), international conference on advanced Computer Systems (ACS 2021) held jointly in Bydgoszcz, Poland (virtually), in June 2021.;The accepted papers address current computer science and computer systems-related technological challenges and solutions, as well as many practical applications and results.;The first part of the book deals with advances in pattern recognition and classifiers, the second part is devoted to image processing and computer vision, while the third part addresses practical applications of computer recognition systems. Machine learning solutions for security and networks are tackled in part four of the book, while the last part collects papers on progress in advanced computer systems.;We believe this book will be interesting for researchers and practitioners in many fields of computer science and IT applications.

关键词： Computational Intelligence Artificial Intelligence Pattern Recognition signal, image and Speech processing image processing and Computer Vision

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Radar Active Jamming signal Identification Method Based on Time-frequency image Features 2

Radar Active Jamming Signal Identification Method Based on T...

引用

2nd international conference on image processing and Media Computing, ICIPMC 2023

作者： Hao, Wanbing Feng, Xiaoyi Jiang, Xiaoyue Northwestern Polytechnical University School of Electronics and Information Xi'an China

ISBN: (纸本)9798350326611

In this paper, the Choi-Williams method is used to describe five typical jamming signals, and the grayscale images of the time-frequency two-dimensional distribution of the jamming signals are obtained. The contour information is maximally enhanced by median filtering and image segmentation processing algorithms for the grayscale images. The effective time-frequency domain feature parameter set is extracted and the dimensionality reduction is done. SVM is used to identify typical radar active jamming signals and provide a priori information for radar anti-jamming. This paper verifies the adopted method by MATLAB simulation experiments and analyzes the performance and feasibility of the adopted method. © 2023 IEEE.

关键词： image segmentation

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Time-frequency image-based signal recognition by using improved CenterNet 12

Time-frequency image-based signal recognition by using impro...

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12th international conference on Information Optics and Photonics (CIOP)

作者： Weng, Junhui Yu, Lei Zhu, Hongna Ye, Jin Cheng, Le Sui, Hao Li, Zhilong Zhang, Jinli Southwest Jiaotong Univ Sch Phys Sci & Technol Chengdu 610031 Peoples R China Natl Key Lab Sci & Technol Blind Signal Proc Chengdu 610031 Peoples R China

ISBN: (数字)9781510649903

ISBN: (纸本)9781510649903;9781510649897

An improved CenterNet is proposed for signal recognition with time-frequency image input. The signal is transformed into time-frequency image by short-time Fourier transform, hence, the signal recognition is transformed into investigating the object detection problem in the field of image detection. Then, the advanced achievements of image detection are adopted to enhance the performance of signal recognition. Here, an improved CenterNet-based object detection network, which demonstrates great advantages in detection speed, is proposed. The results show that the proposed method identifies the signal modulation format with high speed. After training and testing on the self-collected data set with 6 types and 7800 samples, the mean average precision achieves 98.38% and the frame per second reaches 21.4. Compared with the original CenterNet, the detection speed increases more than 4 times while only reducing recognition accuracy by 0.3%, where this algorithm gives a promising way for applications of real-time signal recognition.

关键词： Short-time Fourier transform CenterNet multi-scale pyramid convolution deformable convolution pyramid feature fusion structure

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