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MAITFuse:Multi-Dimension Adaptive Interaction Transform Network For Infrared-visible image Fusion

MAITFuse:Multi-Dimension Adaptive Interaction Transform Netw...

引用

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

作者： Yabin Sun Wentai Lei Ziyi Zhang Jiongchang Liu Chenxu Li Tao Zhang Artificial Intelligence Jiangnan University Wu Xi China Communications Engineering Central South University Chang Sha China School of Electronic Information Central South University Chang Sha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, Transformers have achieved significant success in image fusion. These methods utilize self-attention mechanism across different spatial or channel dimensions and have demonstrated impressive performance. However, existing methods only optimize along a single dimension and struggle to simultaneously capture the complex dependencies between spatial and channel dimensions. To address this problem, we propose a novel multi-dimensional adaptive interaction transformer network, named as MAITFuse, to enhance the multilevel information expression and detail retention capabilities of images. We design a Multi-Dimensional Feature Extraction (MDFE) module to extract features across spatial and channel dimensions in parallel, and introduce a novel weighted cross-attention fusion method to integrate multi-dimensional information effectively. Experimental results show that, compared to existing fusion methods, our proposed method achieves superior fusion performance across various datasets.

关键词： Adaptive systems Transforms Signal processing Feature extraction Transformers Acoustics Data mining Speech processing image fusion visual perception

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Real Rainy Scene Analysis: A Dual-Module Benchmark for image Deraining and Segmentation

Real Rainy Scene Analysis: A Dual-Module Benchmark for Image...

引用

2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Zhao, Shilei Huang, Wenxin Yang, Mingxuan Liu, Wenxuan Wuhan Dashuiyun Technology Co. Ltd China School of Computer Science and Information Engineering Hubei University China School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798350313154

Scene understanding in adverse weather conditions (e.g., rainy and foggy days) has drawn increasing attention, raising some specific benchmarks and algorithms. However, rain streaks in images and videos can significantly degrade visual quality and reduce the effectiveness of computer vision algorithms. Concerning on lack of rainy-clean pairs training samples in real rainy scenes, we propose an unsupervised deraining method that does not require any explicit rain-clean image pairs for training. Instead, our approach leverages the statistical properties of rain streaks and clean regions to learn a rain removal model. Specifically, we choose contrastive learning that encourages the generator to produce derained images that are indistinguishable from clean images while the discriminator distinguishes between real clean images and rain streak patterns. Furthermore, we incorporate a semi-supervised mechanism during segmentation module that exploits low-confidence Pseudo-label to be treated as negative samples for certain classes to facilitate contrastive learning. Our experiments demonstrate that our integrated framework achieve better performance on the given challenging scenarios such as heavy rain and dynamic scenes. © 2023 IEEE.

关键词： Rain

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Vision Transformer Hashing for image Retrieval

Vision Transformer Hashing for Image Retrieval

引用

2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Dubey, Shiv Ram Singh, Satish Kumar Chu, Wei-Ta Indian Institute of Information Technology Computer Vision and Biometrics Lab Allahabad India National Cheng Kung University Dept. of Computer Science and Information Engineering Taiwan

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Recently, Transformer has emerged as a new architecture in deep learning by utilizing self-attention without convolution. Transformer is also extended to Vision Transformer (ViT) for the visual recognition with a promising performance on imageNet. In this paper, we propose a Vision Transformer Hashing (VTS) for image retrieval. We utilize the pre-trained ViT on imageNet as the backbone network and add the hashing head. The proposed VTS model is fine tuned for hashing under six different image retrieval frameworks with their objective functions. We perform the extensive experiments on CIFAR10, imageNet, NUS-Wide, and COCO datasets. The proposed VTS based image retrieval outperforms the recent state-of-the-art hashing techniques with a significant margin. We also find the proposed VTS model as the backbone network is better than the existing networks, such as AlexNet and ResNet. The code is released at https://***/shivram1987/VisionTransformerHashing. © 2022 IEEE.

关键词： image retrieval

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Fuzzy Clustering to Encode Contextual Information in Artistic image Classification 19th

Fuzzy Clustering to Encode Contextual Information in Artis...

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19th International conference on Information processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022

作者： Fumanal-Idocin, Javier Takáč, Zdenko Horanská, L’ubomíra Bustince, Humberto Cordon, Oscar Public University of Navarra Pamplona Spain Institute of Information Engineering Automation and Mathematics Slovak University of Technology in Bratislava Bratislava Slovakia DaSCI Research Institute and DECSAI University of Granada Granada Spain

ISBN: (纸本)9783031089732

Automatic art analysis comprises of utilizing diverse processing methods to classify and categorize works of art. When working with this kind of pictures, we have to take under consideration different considerations compared to classical picture handling, since works of art alter definitely depending on the creator, the scene delineated or their aesthetic fashion. This extra data improves the visual signals gotten from the images and can lead to better performance. However, this information needs to be modeled and embed alongside the visual features of the image. This is often performed utilizing deep learning models, but they are expensive to train. In this paper we utilize the Fuzzy C-Means algorithm to create a embedding strategy based on fuzzy memberships to extract relevant information from the clusters present in the contextual information. We extend an existing state-of-the-art art classification system utilizing this strategy to get a new version that presents similar results without training additional deep learning models. © 2022, Springer Nature Switzerland AG.

关键词： image classification

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RGB-Based Secret Sharing Illumination-Light communications with VN-CodeSK 11

RGB-Based Secret Sharing Illumination-Light Communications w...

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11th IEEE Global conference on Consumer Electronics, GCCE 2022

作者： Hazu, Ryohei Kozawa, Yusuke Habuchi, Hiromasa Ibaraki University Graduate School of Science and Engineering Hitachi Japan Faculty of Engineering Ibaraki University Hitachi Japan

ISBN: (纸本)9781665492324

In illumination-light communications, interference from adjacent illumination-light degrades error rate performance. To deal with the interference light, methods, Which utilize interference introducing visual Secret Sharing, have proposed. Extensions of those schemes to RGB parallel transmission have also been proposed. However, the conventional methods do not provide interference suppression and dimming control functions. In this paper, a system, which introduces Variable N-Code Shift Keying to the RGB-Based conventional system, is proposed. Consequently, it is found that the proposed system can improve error rate performance of the conventional system. © 2022 IEEE.

关键词： Visible light communication

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NeRV++: An Enhanced Implicit Neural Video Representation

NeRV++: An Enhanced Implicit Neural Video Representation

引用

IEEE visual communications and image processing (VCIP)

作者： Ahmed Ghorbel Wassim Hamidouche Luce Morin Univ Rennes INSA Rennes CNRS IETR Rennes France

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Neural fields, also known as implicit neural representations (INRs), have shown a remarkable capability of representing, generating, and manipulating various data types, allowing for continuous data reconstruction at a low memory footprint. Though promising, INRs applied to video compression still need to improve their rate-distortion performance by a large margin, and require a huge number of parameters and long training iterations to capture high-frequency details, limiting their wider applicability. Resolving this problem remains a quite challenging task, which would make INRs more accessible in compression tasks. We take a step towards resolving these shortcomings by introducing neural representations for videos (NeRV)++, an enhanced implicit neural video representation (INVR), as more straightforward yet effective enhancement over the original NeRV decoder architecture, featuring separable conv2d residual blocks (SCRBs) that sandwiches the upsampling block (UB), and a bilinear interpolation skip layer for improved feature representation. NeRV++ allows videos to be directly represented as a function approximated by a neural network, and significantly enhance the representation capacity beyond current INR-based video codecs. We evaluate our method on UVG, MCL JVC, and Bunny datasets, achieving competitive results for video compression with INRs. This achievement narrows the gap to autoencoder-based video coding, marking a significant stride in INR-based video compression research. The code of NeRV++ is available on GitHub.

关键词： Video coding Training image coding Limiting visual communication Neural networks Rate-distortion Video compression Video codecs Software development management

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Dynamic Volumetric Video Coding with Tensor Decomposition

Dynamic Volumetric Video Coding with Tensor Decomposition

引用

IEEE visual communications and image processing (VCIP)

作者： Ju-Yeon Shin Yeoneui Kim Je-Won Kang Gun Bang Dept. of Electronic and Electrical Engineering and Graduate Program in Smart Factory Ewha Womans University Seoul South Korea Electronics and Telecommunications Research Institute Daejeon South Korea

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recently, volumetric video coding based on neural radiance fields has gained significant attention for storing and transmitting three-dimensional (3D) scenes captured from multi-view video. Because the neural networks are trained to produce novel view synthesis of surrounding 3D scenes, compressing the model and then rendering the colors and geometry through the decompressed model can be utilized as a 3D video coding system. However, although this approach provides superior performance compared to conventional 3D video coding standards using depth video, challenges remain in reducing overall model sizes to improve coding efficiency. In this paper, we propose a novel dynamic volumetric video coding technique that employs a Group of Volume (GoV) to divide multi-view video sequences into smaller chunks, addressing complex temporal dynamics. Our method uses volumetric video features represented with 3D spatial and temporal tensor matrices and vectors and encodes them with the GoVs. The tensors are compressed by existing 2D video codec, allowing for fast rendering and easing deployment. Experimental results validate that our method not only reduces memory footprint but also maintains high-quality rendering as compared to state-of-the-art studies.

关键词： Video coding Solid modeling Three-dimensional displays image coding Tensors visual communication Video sequences Rendering (computer graphics) Video codecs Standards

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Compressing 3D Gaussian Splatting via a Generalizable Neural Coder

Compressing 3D Gaussian Splatting via a Generalizable Neural...

引用

IEEE visual communications and image processing (VCIP)

作者： Junteng Zhang Tong Chen Hao Zhu Dong Wang Dandan Ding Zhan Ma Nanjing University Nanjing China Interdisciplinary Research Center for Future Intelligent Chips (Chip-X) Nanjing University Suzhou China OPPO Inc. Nanjing China Hangzhou Normal University Hangzhou China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

As a promising technique for 3D representation, 3D Gaussian Splatting (3DGS) offers fast rendering speed and high fidelity while generating large data volumes. This challenges storage and transmission, so an efficient compression solution is required. Existing implicit methods require pre-scene optimization (online), leading to a long optimization time. By contrast, this paper regards the 3DGS as a point cloud and pre-trains a generalizable (offline) neural coder for compression. After obtaining the 3DGS representation, we focus on the data compression process, which is friendly to applications already equipped with a PCC codec. The neural coder employed is extended from a typical AIbased point cloud compression method, which uses a multiscale and multistage framework to exploit spatial correlations across scales and stages for conditional coding. Experimental results show that our method significantly outperforms existing 3DGS representations without compromising fidelity, achieving more than 39× and 6.8× compression ratio compared to the original 3DGS and SOTA Scaffold-GS, respectively. More importantly, our approach does not require additional time to optimize the compression model.

关键词： Point cloud compression image coding Three-dimensional displays Correlation Codecs visual communication Rendering (computer graphics) Optimization

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Enhancing image Denoising Performance through a Family of Algorithms

Enhancing Image Denoising Performance through a Family of Al...

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International conference on the Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal processing (PCEMS)

作者： Omkar Gupta Irika Nikhita Kalapala Kishor M. Bhurchandi Department of Electronics and Communication Engineering Visvesvaraya National Institute of Technology Nagpur India

images are an integral and indispensable aspect of various disciplines, such as medicine, surveillance, and the entertainment industry. However, the quality of images can be severely compromised by the presence of sensor noise, quantization errors, or transmission errors. This research proposes a novel approach that combines wavelet thresholding and BM3D (Block-Matching and 3D Filtering) techniques for effective image *** efficacy of the methodologies is evaluated and compared to cutting-edge denoising techniques, demonstrating superior performance in both quantitative metrics and visual quality. Furthermore, the study delves into the intricate mechanisms underlying the denoising process and the impact of various parameters on the denoising performance, contributing significantly to the field of image denoising.

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Remote Temperature Monitoring for Infant Incubator using Thermal Camera 14

Remote Temperature Monitoring for Infant Incubator using The...

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14th Biomedical Engineering International conference (BMEiCON)

作者： Chuenpirom, Thanaporn Srirussamee, Kasama Pintavirooj, Chuchart Kings Mongkut Inst Technol Ladkrabang KMITL Sch Engn Bangkok Thailand

ISBN: (纸本)9781665489034

Health concerns over the premature babies are their immature organs and low birth weight. As a result, these babies may have difficulty in breathing, maintaining their healthy body temperature or being light sensitive. Infant incubator has been designed to overcome these issues. It is a medical device that could provide healthy environment, such as light, temperature and humidity. Moreover, it is also able to control and detect any changes which affect the baby's health. Health monitoring systems are critical in modern incubators, which could involve a set of sensors to read and send physiological information, such as temperature. It has also been proposed that establishing remote monitoring system via global system for mobile communications (GSM) would be beneficial for the incubators as it could send an alarm to the doctors and parents in the event that unhealthy vital parameters have been detected. Although a variety of vital parameters are included in the monitoring features of some modern incubators, the capability to detect body temperature of the babies are limited. Hence, this project proposes the development of an effective low-cost smart infant incubator that will benefit the body temperature monitoring of newborn babies using remote IoT-based monitoring system. The monitoring system is controlled through the use of a Raspberry Pi attached to a visual camera and a thermal IR camera with temperature sensor MLX90640, which was able to measure the body temperature remotely. The software of this smart infant incubator was used to improve monitoring system, which involves image processing techniques. The results of measured ambient temperature were found to be accurate and reliable. The prototypical incubator has been designed and manufactured with affordable cost, and the control system of the incubator was able to work comparably to other incubators with potential capability to measure body temperature of the infant.

关键词： Infant incubator IoT image processing

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