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16th International conference on Wireless communications and Signal processing, WCSP 2024

作者： Hu, Chengfeng Zhu, Jun Yang, Yang School of Electronic Information Engineering Anhui University Hefei230031 China

ISBN: (纸本)9798350390643

Despite the fairly good performance of Convolutional Neural Networks (CNNs) in image classification tasks, existing CNNs do not perform well when handling datasets with Gaussian noise. This results in the instability of model performance under extreme conditions or due to device aging. To address this issue, this paper introduces parameter-free attention and employs a simple wavelet decomposition and reconstruction method for image preprocessing, which minimizes the impact of noise on the model's performance. When the noise ratio is 40%, the accuracy of the improved model is approximately 90.4%, representing an increase of 3.1% compared to the original model's accuracy of 87.3%. This indicates that the proposed model in this paper is less sensitive to the impact of Gaussian noise, demonstrating its high robustness. © 2024 ieee.

关键词： Wavelet decomposition

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A GUIDED UPSAMPLING NETWORK FOR SHORT WAVE INFRARED imageS USING GRAPH REGULARIZATION 49

A GUIDED UPSAMPLING NETWORK FOR SHORT WAVE INFRARED IMAGES U...

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49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Sippel, Frank Seiler, Jurgen Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany

ISBN: (纸本)9798350344868;9798350344851

Exploiting the infrared area of the spectrum for classification problems is getting increasingly popular, because many materials have characteristic absorption bands in this area. However, sensors in the short wave infrared (SWIR) area and even higher wavelengths have a very low spatial resolution in comparison to classical cameras that operate in the visible wavelength area. Thus, in this paper an upsampling method for SWIR images guided by a visible image is presented. For that, the proposed guided upsampling network (GUNet) uses a graph-regularized optimization problem based on learned affinities is presented. The evaluation is based on a novel synthetic near-field visible-SWIR stereo database. Different guided upsampling methods are evaluated, which shows an improvement of nearly 1 dB on this database for the proposed upsampling method in comparison to the second best guided upsampling network. Furthermore, a visual example of an upsampled SWIR image of a real-world scene is depicted for showing real-world applicability.

关键词： image processing Deep Learning Short Wave Infrared Imaging Guided Upsampling

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DYNAMIC RANGE TRANSFORMER (DRT): LEARNING ENHANCED LOG-PERCEPTUAL INFORMATION WITH SWIN-FOURIER CONVOLUTION NETWORK FOR HDR IMAGING 30

DYNAMIC RANGE TRANSFORMER (DRT): LEARNING ENHANCED LOG-PERCE...

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30th ieee International conference on image processing (ICIP)

作者： Lim, Heunseung Shin, Joongchol Choi, Jinsol Paik, Joonki Chung Ang Univ Dept Image Seoul 06974 South Korea Chung Ang Univ Dept Artificial Intelligence Seoul 06974 South Korea

ISBN: (纸本)9781728198354

The image obtained using an image sensor with limited dynamic range cannot perfectly represent the various lighting conditions of the real world. Various HDR methods have been studied for expanding the dynamic range in a single image. However, it is difficult to avoid ghosting artifacts caused by the movement of the subject over time and the corresponding texture loss. To solve these problems, we present a novel HDR image acquisition method via dynamic range transformer (DrT) that learns enhanced log-perceptual information using Swin-Fourier convolutional neural network as a backbone. When training the DrT with Swin-Fourier network, it estimates the attention map to obtain an HDR image by minimizing the enhanced log-perceptual (ELP) loss. The Swin-Fourier network considers both local and global contexts simultaneously, which reduces ghosting and texture loss. By learning ELP, it also minimizes color distortion and restores fine details of the dynamic range. Experimental results demonstrate that the HDR results obtained using DrT show reduced color distortion, significantly decreased ghosting artifacts, and texture loss compared to conventional methods. We provide implementation code of our proposed methods in https://***/HeunSeungLim/DrT

关键词： high dynamic range transformer log-Euclidean metric

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Rough Spatial Ensemble Kernelized Fuzzy C Means Clustering for Robust Brain MR image Tissue Segmentation 8th

Rough Spatial Ensemble Kernelized Fuzzy C Means Clustering f...

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8th International conference on Computer Vision and image processing (CVIP)

作者： Halder, Amiya Choudhuri, Rudrajit Bhowmick, Arinjay St Thomas Coll Engn & Technol 4 DH Rd Kolkata India

ISBN: (纸本)9783031585340;9783031585357

image segmentation is a crucial step in image processing having various applications in biomedical image analysis. Segmentation of the magnetic resonance images of the brain is one such key area in biomedical image analysis that segments various tissues in the brain and detects tumor regions. In this paper, an unsupervised rough spatial ensemble kernelized fuzzy clustering segmentation algorithm is presented for automated segmentation of magnetic resonance images of the brain. The proposed algorithm is an integration of Rough Fuzzy C Means clustering and the kernel method with a novel ensemble kernel being a combination of spherical kernel, Gaussian, and Cauchy kernels, which improves the performance of the segmentation algorithm. The proposed algorithm performs better than the existing clustering algorithms across a wide range of magnetic resonance images of the brain along with visual indications obtained from the results.

关键词： Iterative Optimization Magnetic Resonance Imaging image Segmentation Rough Set Kernel Method

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2024 ieee International conference on image processing, ICIP 2024 - Proceedings

2024 IEEE International Conference on Image Processing, ICIP...

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31st ieee International conference on image processing, ICIP 2024

ISBN: (纸本)9798350349399

The proceedings contain 593 papers. The topics discussed include: MDBFUSION: a visible and infrared image fusion framework capable for motion deblurring;prune channel and distill: discriminative knowledge distillation for semantic segmentation;imbalanced data robust online continual learning based on evolving class aware memory selection and built-in contrastive representation learning;privacy-preserving visual cues communication for hearing-impaired people using deep learning;transformer-based clipped contrastive quantization learning for unsupervised image retrieval;attention enhancement with parallel groups for remote sensing object detection;cross-domain few-shot in-context learning for enhancing traffic sign recognition;and recurrent 3-D multi-level visual transformer for joint classification of heterogeneous 2-D and 3-D radiographic data.

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SEGMENTATION-DRIVEN INFRARED AND VISIBLE image FUSION VIA TRANSFORMER-ENHANCED ARCHITECTURE SEARCHING 49

SEGMENTATION-DRIVEN INFRARED AND VISIBLE IMAGE FUSION VIA TR...

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49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Fu, Hongming Wu, Guanyao Liu, Zhu Yan, Tiantian Liu, Jinyuan Dalian Univ Technol Sch Software Technol Dalian 116024 Peoples R China Dalian Univ Sch Software Engn Natl & Local Joint Engn Lab Comp Aided Design Dalian 116622 Liaoning Peoples R China

ISBN: (纸本)9798350344868;9798350344851

A series of infrared and visible image fusion (IVIF) methods have emerged to improve the performance of segmentation task. However, existing perception-focused IVIF methods take visual effects and semantic information as a unified goal for training, ignoring the task conflicts. Moreover, these methods often involve manually designed modules, which are laborious and suboptimal. To solve the problems, we propose a collaborative feature learning framework based on neural architecture search (NAS). Specifically, we extract shared features of fusion and segmentation tasks into a unified space and separately process task objectives through a dual decoder. In light of the essential role that semantic information plays in the segmentation task, we construct a hybrid search space with transformers incorporated to enhance context dependence handling. Our method undergoes extensive experiments, showcasing exceptional visual effects and significant enhancements in segmentation tasks compared to other state-of-the-art methods.

关键词： image fusion semantic segmentation neural architecture search

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Real-Time Violence Detection and Alert System using MobileNetV2 and Cloud Firestore 2

Real-Time Violence Detection and Alert System using MobileNe...

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2nd ieee International conference on Networking and communications, ICNWC 2024

作者： Thomas, Mannu Balamurugan, P. SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350365269

The aggressive action greatly jeopardizes individual safety and general well-being. Various alternative tactics have been used to reduce violent behavior, including the installation and maintenance of surveillance systems. It is crucial for monitoring systems to automatically detect aggressive behavior and promptly provide warning signals to the authorities. Computer vision and image processing have emerged as crucial study areas for identifying deviant and violent behavior, drawing in new researchers. The paper introduces a real- time violence detection and alert system that identifies violence in video streams by utilizing MobileNetV2. The system provides a scalable, precise, and adaptable public safety solution by using Cloud Firestore for data storage and administration, and a Telegram Bot for instant notifications. © 2024 ieee.

关键词： image processing

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A Review on Automated Detection and Assessment of Fruit Damage Using Machine Learning

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ieee ACCESS 2024年 12卷 21358-21381页

作者： Safari, Yonasi Nakatumba-Nabende, Joyce Nakasi, Rose Nakibuule, Rose Mbarara Univ Sci & Technol Dept Comp Sci Mbarara Uganda Makerere Univ Dept Comp Sci Kampala Uganda Busitema Univ Dept Comp Sci Kampala Uganda

Automation improves the quality of fruits through quick and accurate detection of pest and disease infections, thus contributing to the country's economic growth and productivity. Although humans can identify the fruit damage caused by pests and diseases, the methods used are inconsistent, time-consuming, and variable. The surface features of fruits typically observed by consumers who seek their health benefits affect their market value. The issue of pest and disease infections further deteriorates fruits' quality, becoming a mounting stressor on farmers since they reduce the potential revenue from fruit production, processing, and export. This article reviews various studies on detecting and classifying damages in fruits. Specifically, we review articles where state-of-the-art approaches under segmentation, image processing, machine learning, and deep learning have proved effective in developing automated systems that address hurdles associated with manual methods of assessing damage using visual experiences. This survey reviews thirty-two journal and conference papers from the past thirteen years that were found electronically through Google Scholar, Scopus, ieee, ScienceDirect, and standard online searches. This survey further presents a detailed discussion of previous research done in the past while emphasizing their strengths and limitations as well as outlining potential future research topics. It also reveals that much as the use of automated detection and classification of fruit damage has yielded promising results in the horticulture industry, more research is still needed with systems required to fully automate the detection and classification processes, especially those that are mobile phone-based towards addressing occlusion challenges.

关键词： Fruit damage detection classification deep learning image analysis segmentation

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Unveiling Local Well-posedness Influence for Cross-modal Person Re-Identification

Unveiling Local Well-posedness Influence for Cross-modal Per...

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2025 ieee International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Yang, Yumeng Dong, Guan-Nan Zhu, Aichun Ni, Mingcheng Li, Yifeng Nanjing Tech University China

ISBN: (纸本)9798350368741

The existing cross-modal retrieval methods trend toward the conventional multi-modal alignment while ignoring the localization bias caused by visual hallucination, including color pollution and appearance-like occlusion due to uncontrollable factors such as weather, illumination, and occlusion. This feature blinding misleads the model to lock in the pseudo-real position and further leads to local unmatched. To this end, we discuss cross-modal local alignment well-posedness by making a phased local modal-masking to calibrate the undisturbed actual local alignment from entity, attribute, and appearance. Specifically, we introduce a mask-based local well-posedness modeling (MLWM) strategy, including text-based entity masking (TEM), text-based attribute-specific masking (TAM), and image-based appearance masking (IAM) to phased collaboratively consider image prompting-based text entities, image prompting-based text attributes, and text prompting-based appearance inference contrast, respectively. Finally, we dynamically optimize the weights of positively correlated image-text pairs by comparing the similarity between original and reconstructed features. Experimental results demonstrate that our method is effective on three public datasets. © 2025 ieee.

关键词： local well-posedness Person re-identification text-to-image

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Optimization Method of visual Communication in image processing Technology 4

Optimization Method of Visual Communication in Image Process...

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4th ieee Annual Flagship India Council International Subsections conference, INDISCON 2023

作者： Gong, Yanyan Tongji University Shanghai China

ISBN: (纸本)9798350333558

The optimization method of visual communication in image processing technology is an important aspect of modern digital media. It involves using various technologies to improve the quality and clarity of images, videos, and other visual content. The main goal of this method is to improve the overall user experience by providing high-quality visual effects that are easy to understand and explain. One of the most common optimization methods used in image processing technology is compression. This technology reduces the size of image files without affecting their quality. Another popular method is color correction, which adjusts the colors in the image to make it more dynamic and attractive. Other optimization methods include noise reduction, sharpening, and contrast adjustment. These technologies help eliminate unnecessary artifacts in the image while enhancing the overall appearance of the image. In summary, optimization methods play a crucial role in visual communication in image processing technology. It helps to improve the quality and clarity of visual effects, while ensuring that users are easy to understand and explain. With the continuous progress of technology, we can expect more complex optimization methods to emerge in the future. This article analyzes the visual communication optimization method of this technology. Firstly, common problems that need to be optimized were introduced. Secondly, the optimization methods for the optimization problem were analyzed. Through research, common problems that need to be optimized in visual communication have been successfully solved using image processing technology, achieving the goal of visual communication optimization. © 2023 ieee.

关键词： visual communication

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