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检索条件"任意字段=Conference on Visual Communications and Image Processing 2002"

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DepthUNet: A Dehazing Model with Adaptive Depth Attention for Natural images

DepthUNet: A Dehazing Model with Adaptive Depth Attention fo...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Wang, Kai-Yu Lin, Yu-Hsiang Hsiung, Pao-Ann National Chung Cheng University Department of Computer Science and Information Engineering Chiayi Taiwan

ISBN: (纸本)9798350359855

Most image dehazing deep learning models target synthetic datasets of hazy images, resulting in not considering features in natural hazy images. Leveraging on depth attention with adaptation, we propose a novel dehazing network called depthUNet, that is focused on natural images. Utilizing the correlation between depth information and haze distribution, our network enhances its generalization performance on natural images. Furthermore, our method improves the PSNR for the non-homogeneous realistic haze dataset NH-HAZE from 20.66 (DeHamer's result) to 20.74, using only 1.6% of the parameters. Similarly, for the outdoor scenes realistic haze dataset O-HAZE, our method enhances the PSNR from 24.36 (MSBDN's result) to 25.77, with just 6% of the parameters. On natural road images with haze from dataset RTTS, our method improved the vehicle detection rate by 10% in terms of the R-squared value. In summary, our method outperforms all state-of-The-Art methods while utilizing the least number of parameters. © 2023 IEEE.

关键词： image enhancement

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Recurrent Multi-connection Fusion Network for Single image Deraining

Recurrent Multi-connection Fusion Network for Single Image D...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Liu, Yuetong Zhang, Rui Zhang, Yunfeng Ning, Yang Yao, Xunxiang Han, Huijian Shandong Univ Finance & Econ Dept Comp Sci & Technol Jinan Peoples R China Shandong Jianzhu Univ Dept Comp Sci & Technol Jinan Peoples R China

ISBN: (纸本)9781665475921

Single image deraining is an important problem in many computer vision tasks because rain streaks can severely degrade the image quality. Recently, deep convolution neural network (CNN) based single image deraining methods have been developed with encouraging performance. However, most of these algorithms are designed by stacking convolutional layers, which encounter obstacles in learning abstract feature representation effectively and can only obtain limited features in the local region. In this paper, we propose a recurrent multi-connection fusion network (RMCFN) to remove rain streaks from single images. Specifically, the RMCFN employs two key components and multiple connections to fully utilize and transfer features. Firstly, we use a multi-scale fusion memory block (MFMB) to exploit multi-scale features and obtain long-range dependencies, which is beneficial to feed useful information to a later stage. Moreover, to efficiently capture the informative features on the transmission, we fuse the features of different levels and employ a multi-connection manner to use the information within and between stages. Finally, we develop a dual attention enhancement block (DAEB) to explore the valuable channel and spatial components and only pass further useful features. Extensive experiments verify the superiority of our method in visual effect and quantitative results compared to the state-of-the-arts.

关键词： Single image deraining multi-connection multi-scale fusion memory block dual attentive enhancement block

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Fundus image Classification for Glaucoma using U-Net Architecture and Logistic Regression 2

Fundus Image Classification for Glaucoma using U-Net Archite...

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2nd International conference on Paradigm Shifts in communications Embedded Systems, Machine Learning and Signal processing, PCEMS 2023

作者： Bajaj, Vanshika Kotambkar Shelke, Deepali M. Shri Ramdeobaba College of Engineering and Management Dept. of Information Technology Nagpur India

ISBN: (纸本)9798350310719

The leading cause of visual impairment after cataract, is glaucoma and the only way to combat it is to detect it early. It is imperative to develop a system that can work effectively without a lot of equipment, qualified medical personnel, and takes less time in order to address this fundamental issue. A Computer-Aided Diagnosis (CAD) system, which employs different algorithms for medical image processing and analysis, can assist in achieving this. One of the ways to diagnose glaucoma is to calculate Optic Cup to Optic Disc ratio (CDR) and this can be done with the help of CAD algorithms. In medical image processing the primary focus is on image segmentationand its classification in order to obtain a result. In this paper, the exploration the best-known CNN model, U-Net for image segmentation of Optic Disc and Optic Cup from a fundus image and Logistic Regression, a classification model to determine a relationship between these two terms rather than previously used CDR formulas. © 2023 IEEE.

关键词： Computer aided diagnosis

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Variable-rate Learned image Compression with Adaptive Quantization Step Size

Variable-rate Learned Image Compression with Adaptive Quanti...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Mei, Feihong Li, Li Liu, Dong USTC Deep Space Exploration Laboratory Hefei China

ISBN: (纸本)9798350359855

Nowadays, learned image compression has outperformed traditional coding methods like VVC. However, learned image compression methods are often optimized for specific rates and lack support for variable rate compression. Though some efforts have been made to address variable rate compression, striking the right balance between performance and complexity remains a challenge. To address this, we propose the Adaptive Quantize Step Size (AQS) technique, allowing continuous variable rate within a single model. Unlike methods that learn to scale the latent representation, AQS enables learning of different quantization step sizes for different rates, preserving the latent representation's distribution while only affecting the quantization step size, which is beneficial for the entropy model. As a result, our method achieves superior performance compared to existing methods while maintaining similar complexity. It delivers comparable results across a wide range of rates using a single model with minimal additional parameters. © 2023 IEEE.

关键词： image compression

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Development of image processing Techniques in Crack Detection and Analysis

Development of Image Processing Techniques in Crack Detectio...

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2023 International conference on Electrical, Computer, communications and Mechatronics Engineering, ICECCME 2023

作者： Al Dhafari, Luqman Sulaiman Afzal, Aftab Al Bahrani, Raid Ibrahim Al Busaidi, Yahyah Sheikh-Akbari, Akbar Hossain, Mohammad Sayeed Military Technological College Dept. Aeronautical Engineering Muscat Oman Leeds Beckett University School of Built Environment Engineering and Computing Leeds United Kingdom

ISBN: (纸本)9798350322972

Inspection of aircraft skin is required as per the Corrosion Prevention and Control Program (CPCP) to ensure aircraft structural integrity. Human visual inspection is the most widely used technique in aircraft surface inspection, according to the CPCP. Scheduled inspections and regular maintenance of an aircraft through conventional methods constitute tedious and lengthy procedures. Often the visual inspections lead to subjective judgement and do not constitute repeatability. Many automated vision-based aircraft skin inspection systems have been designed to provide a safe, quick, and accurate visual assessment over the past years. This paper presents a section of research investigating defect detecting and accurately locating the outer body of an aircraft using an Unmanned Aerial Vehicle (UAV) to capture images and digital image processing techniques to locate possible cracks. The inspection system is used to initially detect locations of cracks (defects) on an aircraft's outer skin and the detected crack is further investigated using thermal and ultrasound imaging methods. The scope of this paper includes a review of the design and development of a series of advanced dedicated image processing algorithms suitable for applying digital image processing on images captured from the outer surface of a typical aircraft fuselage. © 2023 IEEE.

关键词： Crack detection

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Multi-disease Classification and Severity Estimation of Cotton and Soybean Plants Using DenseNet 3rd

Multi-disease Classification and Severity Estimation of Cott...

引用

3rd International conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Bhujade, Vaishali G. Sambhe, V. K. VJTI Mumbai Comp Engn Dept Mumbai India VJTI Mumbai Dept Informat Technol Mumbai India

ISBN: (纸本)9783031640698;9783031640704

As agriculture plays vital role in nation's economy, early diagnosis of plant diseases is a crucial and challenging task. Soybean and Cotton are the cash crops in Maharashtra therefore this study focuses on these crops. Automatic disease recognition and classification poses a variety of challenges in consideration of available datasets, tools, and image capturing conditions and has received considerable attention in the past few decades. Diseases have proven to be the root cause to major losses in the production of crop and low-quality yield. Within this scenario, the automatic disease recognition and classification is very critical and primary challenge for sustainable farming. Traditional methods were manual which are prone to errors, time-consuming and costly. In recent years Deep learning along with image processing has garnered tremendous success in a variety of application domains including automatic disease detection, but, traditional methods were not focusing on multiple diseases available on single leaf image. In this investigation, image processing techniques are investigated for their potential application in identifying cotton and soybean leaf diseases. The study evaluates the accuracy and loss of two approaches, Inception-visual Geometry Group Network (INC-VGGN) and FACED, following various training epochs. FACED shows superior performance than INC-VGGN for both cotton and soybean plants in terms of training and validation accuracy. FACED falls behind INC-VGGN in training accuracy at first, but at the end of 30 epochs, it has caught up and even surpassed it for soybean. FACAD gives high precision and recall over INC-VGGN.

关键词： Deep Learning image processing Disease detection INC-VGGN FACED Accuracy Validation

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Bottom-Up Transformer Reasoning Network for Text-image Retrieval 1

引用

29th International conference on Neural Information processing

作者： Yang, Zonghao Zhou, Yue Chen, Ao Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (数字)9789819916450

ISBN: (纸本)9789819916443;9789819916450

image-text retrieval is a complicated and challenging task in the cross-modality area, and lots of experiments have made great progress. Most existing researches process images and text in one pipeline or are highly entangled, which is not practical and human-friendly in the real world. Moreover, the image regions extracted by Faster-RCNN are highly over-sampled in the image pipeline, which causes ambiguities for the extracted visual embeddings. From this point of view, we introduce the Bottom-up Transformer Reasoning Network (BTRN). Our method is built upon the transformer encoders to process the image and text separately. We also embed the tag information generated by Faster-RCNN to strengthen the connection between the two modalities. Recall at K and normalized discounted cumulative gain metric (NDCG) metrics are used to evaluate our model. Through various experiments, we prove our model can reach state-of-the-art results.

关键词： Text-image retrieval Transformer encoder Tag embedding Double-Stream architecture

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ST-CLIP: Learning SpatioTemporal CLIP with GraphTransformer for Action Recognition 17

ST-CLIP: Learning SpatioTemporal CLIP with GraphTransformer ...

引用

17th IEEE International conference on Signal processing, ICSP 2024

作者： Zheng, Zhenxing Lyu, Jing Gong, Junlong An, Gaoyun Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an China Beijing Jiaotong University Institute of Information Science Beijing China

ISBN: (纸本)9798350387384

Large pretrained models applied to image data have showcased significant progress in visual representation learning. However, the direct application of pretrained image models to video data is insufficient for capturing spatiotemporal context. The challenge lies in effectively transferring image-modal knowledge from web-scale data to the video domain. To enable spatiotemporal modeling, we propose SpatioTemporal CLIP (ST-CLIP) that incorporates a lightweight graph reasoning unit into each encoder of the vision Transformer to efficiently reason about temporal relations between frames. Extensive experiments and ablation studies highlight the competitiveness of ST-CLIP compared to state-of-the-art methods. The proposed model achieves impressive performance on fully-supervised experiments on Kinetics-400. © 2024 IEEE.

关键词： Graph neural networks

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A Near Lossless Learned image Coding Network Quantization Approach for Cross-Platform Inference

A Near Lossless Learned Image Coding Network Quantization Ap...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Hang, Xinyu Jia, Chuanmin Ma, Siwei Gao, Wen Peking University National Engineering Research Center on Visual Technology School of Computer Science China Peking University National Key Laboratory for Multimedia Information Processing School of Computer Science China Peking University Wangxuan Institute of Computer Technology China

ISBN: (纸本)9798350359855

This paper presents an innovative pipeline design for the quantization of neural image codecs, ensuring robust and consistent deployment across various platform inferences. Our approach addresses a significant challenge prevalent in neural image compression networks: the lack of quantization methods for entropy probability hyper-priors, such as Gaussian mixture models (GMMs), in the existing literature. Our method serves as a solution to prevent decoding errors stemming from float-point discrepancies between the encoder and decoder, especially when executed on different platforms. By implementing a quantization scheme based on look-up tables (LUTs), we achieve precise quantization of GMM probability models with minimal performance degradation, approaching near losslessness. Moreover, our contribution offers a comprehensive quantization pipeline that facilitates dependable data transmission and harmonious synchronization of arithmetic coding algorithms between the encoder and decoder components. Empirical evidence highlights the effectiveness of our method, demonstrating seamless codec matching on diverse platforms and substantiating its capacity to enhance the integrity and security of compressed data. This work enhances the practical applicability of neural compression techniques across real-world scenarios. © 2023 IEEE.

关键词： Table lookup

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Infrared-to-Optical image Translation for Keypoint-Based image Registration 30

Infrared-to-Optical Image Translation for Keypoint-Based Ima...

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30th IEEE Signal processing and communications Applications conference (SIU)

作者： Elsaeidy, Mohamed Erkol, Muhammed Emin Gunturk, Bahadir Kiirsat Ates, Hasan Fehmi Istanbul Medipol Univ Dept Comp Engn Istanbul Turkey Istanbul Medipol Univ Dept Elect & Elect Engn Istanbul Turkey

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450928

Multi-modal image registration is a critical step in many remote sensing and visual navigation applications. While image registration techniques developed for single modality images do not perform well for multi-modal registration, techniques developed for multi-modal image registration are not suitable for real-time applications. In this study, we aim to achieve real-time registration of infrared and optical (visible range) images. Specifically, we investigate the use of image-to-image translation deep network to convert infrared images to optical images, which is then applied for keypoint-based image registration.

关键词： Multi-modal image registration GAN image translation network

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