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Swin Transformer based Siamese Network for Thermal and Optical image Registration

Swin Transformer based Siamese Network for Thermal and Optic...

引用

IEEE Signal processing and communications Applications (SIU)

作者： Mohamed Elsaeidy İsmail Can Yağmur Hasan Fehmi Ateş Bahadir Kürşat Güntürk Department of Computer Engineering Istanbul Medipol University Istanbul Turkey Department of Computer Science Ozyegin University Istanbul Turkey

The process of multi-modal image registration is fundamental in remote sensing and visual navigation applications. However, existing image registration methods that are designed for single modality images do not provide satisfactory results when applied to multi-modal image registration. In this research, our objective is to achieve highly accurate alignment of both infrared and optical (visible range) images. To accomplish this goal, we explore the effectiveness of the Swin Transformer encoder and cosine loss in enhancing the keypoint-based image registration process. Simulation results show the improvement achieved in multi-modal registration by using a transformer based Siamese network.

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Alignment of image-Text and Video-Text Datasets

Alignment of Image-Text and Video-Text Datasets

引用

IEEE Signal processing and communications Applications (SIU)

作者： Yunus Emre Özköse Zeynep Gökçe Pınar Duygulu Bilgisayar Mühendisliği Hacettepe Üniversitesi Ankara Türkiye

In this study, the alignment of video-text and imagetext datasets is studied. Firstly, similarities are calculated over the texts in the two data sets. A retrieval setup with visual similarities is then applied to the subset which is created via calculated text similarities. A BERT-based embedding vector method is applied to the raw and pure texts. As a visual feature, object-based and CLIP-based methods are used to define video frames. According to the results, alignment with CLIP features achieves the best results in the subset created by filtering using raw text.

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Lightweight Arbitrary-Scale Super-Resolution of Remote Sensing images via Super-Scale Feature

Lightweight Arbitrary-Scale Super-Resolution of Remote Sensi...

引用

IEEE visual communications and image processing (VCIP)

作者： Yifei Long Yuantong Zhang Daiqin Yang Zhenzhong Chen Huairui Wang Shan Liu School of Remote Sensing and Information Engineering Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan University Wuhan China Tencent Shenzhen China Tencent America Palo Alto CA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Remote sensing image (RSI) super-resolution (SR) demands lightweight and efficient methods due to required rapid response in practical applications. Integrating RSIs with different resolutions for diverse applications also requires arbitrary-scale SR, making fix-scaled SR scale inflexible. Therefore, a lightweight SR algorithm capable of arbitrary-scale is necessary for RSIs. To address the above issue, a super-scale feature-based lightweight arbitrary-scale (SFLA) SR network is proposed in this paper. The network consists of two modules: 1) A super-scale feature extraction (SSFE) module that extracts features at both the initial low-resolution (LR) and an integer super-scale resolution, 2) A self-attention implicit function reconstruction (SIFR) module that utilizes multi-layer perceptron (MLP) network and self-attention mechanism for pixel-wise feature mapping to achieve superior SR results. Comparative experiments and ablation results demonstrate that the proposed SFLA algorithm effectively strikes a good balance between performance and complexity.

关键词： visual communication Superresolution Feature extraction Complexity theory Remote sensing image reconstruction

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Improving Reconstruction Fidelity in Generative Face Video Coding using High-Frequency Shuttling

Improving Reconstruction Fidelity in Generative Face Video C...

引用

IEEE visual communications and image processing (VCIP)

作者： Goluck Konuko Giuseppe Valenzise Anthony Trioux CNRS CentraleSupélec Laboratoire des Signaux et Systèmes Univ. Paris-Saclay France School of Telecommunications Engineering Xidian University Xi’an China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Generative face video coding (GFVC) schemes applied to talking head videos have recently demonstrated significant coding gains compared to traditional coding frameworks, particularly at ultra-low bitrates. Despite advancements in the field, these methods still face challenges in handling large pose and facial expression changes, as well as (dis-)occlusions. Recently, a hybrid approach (HDAC+) that combines a low-quality video coded with a conventional codec and animation-based coding has been proposed for standardization and shown to partially address these issues. Although HDAC+ shows promising results, it still struggles with generating accurate images. In this paper, we propose HDAC-HF, an improvement to the reconstruction process in HDAC+. Based on empirical observations that some pose and expression details are lost during animation, we introduce a high-frequency (HF) shuttling mechanism to enhance reconstruction fidelity at the decoder side, inspired by recent advancements in video super-resolution. By enhancing the flow of high-frequency details in the feature domain, we improve the reconstruction of facial expressions and poses. Qualitative and quantitative experiments confirm that the proposed method improves reconstruction without any additional bitstream or signaling cost compared to the baseline HDAC+ codec.

关键词： Video coding Codecs image coding visual communication Bit rate Animation Encoding Decoding image reconstruction Faces

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Sample Adaptive Offset for Geometry-based Point Cloud Attribute Compression

Sample Adaptive Offset for Geometry-based Point Cloud Attrib...

引用

IEEE visual communications and image processing (VCIP)

作者： Lizhi Hou Qian Zhang Yiling Xu Cooperative MediaNet Innovation Center Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Geometry-based Point Cloud Compression (G-PCC) has demonstrated remarkable coding efficiency across a wide range of datasets. However, due to the irreversible quantization loss, the G-PCC reconstructed attribute is inevitably distorted. In this paper, we propose a Sample Adaptive Offset (SAO) method for G-PCC to reduce attribute distortion. Specifically, after the regular attribute coding of G-PCC, the whole point cloud is first classified according to their distorted attribute values to maintain a low-complexity design. The cross-channel correlation is also utilized to enhance the classification process. Then, one optimal offset is derived for each class by a Rate Distortion Optimization (RDO). A second stage RDO is performed to ensure the maximum positive gain. Finally, the selected classes and their corresponding offsets are applied to the reconstructed attribute to reduce the distortion. We integrate the proposed method on top of the G-PCC solid point cloud test model GeSTM_v6.0. The simulation results show that the proposed method can achieve up to {0.4%, 6.3%, 6.1%} attribute Bjøntegaard-Delta (BD)-rate gain on different channels with negligible additional complexity on the encoding and decoding side.

关键词： Point cloud compression Solid modeling image coding visual communication Simulation Rate-distortion Distortion Solids Encoding image reconstruction

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Learned image Compression with Enhanced Dynamic Spatial Aggregation and Asymmetric Entropy model

Learned Image Compression with Enhanced Dynamic Spatial Aggr...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuanton Zhang Nianxiang Fu Guo Yu Xiangdong Lv Huairui Wang Zhenzhong Chen School of Remote Sensing and Information Engineering Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China

Learned image compression (LIC) has shown significant potential and better rate-distortion performance than traditional techniques. However, existing CNN-based approaches or window-based self-attention methods can only capture spatial information within fixed ranges. To tackle this limitation, we propose a novel method with dynamic spatial aggregation for transform coding. Our approach introduces enhanced adaptive aggregation, which generates kernel offsets to capture relevant information within content-dependent ranges, improving the transform process. Furthermore, we define a generalized coarse-to-fine entropy model that considers global context, channel-wise information, and spatial context in a coarse-to-fine manner. Additionally, our method takes into full consideration the model’s efficiency and complexity. By introducing the asymmetric entropy model structure and efficient heterogeneous convolution, our approach maintains lower coding complexity and higher decoding speed while ensuring performance. Experimental results demonstrate a substantial improvement in rate-distortion performance achieved by our method when compared to traditional compression methods like VTM and BPG, as well as some LIC methods.

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TSARN: A Joint Temporal-Spatial-Angular Reconstruction Network for Light Field Lenslet Video Compression

TSARN: A Joint Temporal-Spatial-Angular Reconstruction Netwo...

引用

IEEE visual communications and image processing (VCIP)

作者： Huan Li Xinpeng Huang Yongjie Lu Ping An School of Communication and Information Engineering Shanghai University Shanghai China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Light Field (LF) lenslet videos are capable of capturing the aggregation of light rays in dynamic scenes, providing a more immersive experience. However, the immense data volume of LF lenslet videos poses significant challenges for storage and transmission. Existing methods that utilize traditional video codecs to encode all views fail to meet the demands for efficient compression. This paper proposes a compression framework based on down-sampling, which focuses on designing an efficient reconstruction method at the decoder side. In LF lenslet videos, pixels that are invisible in the current view may be visible in adjacent views angularly and/or in adjacent frames temporally. Therefore, we propose a joint temporal-spatial-angular reconstruction network. In this network, Spatial-Angular Convolutional Module utilizes different forms of convolutions to fully extract spatial-angular features for synthesizing LF views initially; Deformable Convolutional Temporal Fusion Module employs deformable convolutions to align information from different frames and aggregate temporal features; View Refinement Module is finally adopted to further refine the features and reconstruct high-quality LF lenslet videos. Experimental results demonstrate that our proposed LF lenslet video compression framework achieves superior performance, significantly reducing the bitstream.

关键词： Convolutional codes image coding visual communication Video compression Feature extraction Light fields Decoding Video codecs image reconstruction Videos

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Performance Evaluation of Speech Enhancement of Single Acoustic Signal Referring to image Information 5

Performance Evaluation of Speech Enhancement of Single Acous...

引用

5th International conference on Imaging, Signal processing and communications (ICISPC)

作者： Matsumoto, Mitsuharu Univ Electrocommun Dept Informat 1-5-1 Chofugaoka Chofu Tokyo 1828585 Japan

ISBN: (纸本)9781665424257

This paper presents performance evaluation of speech enhancement of an acoustic signal referring to image information. In recent years, we often see smart phones and tablets in everyday life. Smart phones and tablets have a camera and a microphone. Although many speech enhancement techniques use only the audio information from microphones, it is considered that visual information from a camera is also helpful to enhance the speech. Although the frame rate of the normal movie is very low compared to the sampling frequency of the audio signal, it is useful if it is adequately used. We investigated the performance depending on the parameters and confirmed its effectiveness through the experiments.

关键词： Vision-referential speech enhancement Mask information Smart phone Binary mask

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Lightweight Graph Convolutional Network Based on Multi-Head Residual Attention for Hand Point Classification

Lightweight Graph Convolutional Network Based on Multi-Head ...

引用

IEEE visual communications and image processing (VCIP)

作者： Duc-Chinh Nguyen Manh-Hung Ha Manh-Tuan Do Oscal Tzyh-Chiang Chen Faculty of Applied Sciences International School Vietnam National University Hanoi Vietnam Department of Electrical Engineering National ChungCheng University

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In this work, we propose a lightweight Graph Convolution Network (GCN) based on residual attention model successfully applied to the classification task of hand point landmarks. The main highlights of our study involve redefining the standard reference point from the initial outer-coordinate origin to the central position within the palm and improving the node features within the graph. Additionally, we construct a graph network capable of classifying points on the hand. This is achieved through a 2-layer structure, which incorporates attention modules to optimize graph information processing, along with a skip connection to prevent feature uniformity within nodes after numerous hidden layers. The experimental results on the ALS dataset indicate that the proposed model yields remarkably high accuracy rates on both the training and test sets, reaching 97.75% and 97.41%, respectively, while keeping the computational complexity (parameters are only 25,874) at an extremely low level. Moreover, the proposed model exhibited high accuracy rates on the Sign Language MNIST dataset, achieving 99.41% on both accuracy and F1-score. Thus, it has great potential to be deployed on portable devices.

关键词： Hands Training Sign language Accuracy Convolution visual communication Computational modeling Information processing Standards Testing

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image processing using Cloud for Surveillance 23

Image Processing using Cloud for Surveillance

引用

Proceedings of the 5th International conference on Information Management & Machine Intelligence

作者： Shivam Tiwari Aarhee Phukan T R Vedhavathy Department of Networking and Communications SRM Institute of Science and Technology India Department of Networking and Communication SRM Institute of Science and Technology India

ISBN: (纸本)9798400709418

In the era of digitization and big data, the world is inundated with an ever-growing volume of visual content, be it images or videos. As organizations strive to harness the potential of these multimedia data sources, there is an increasing need for advanced image processing techniques that can automate the analysis and extraction of valuable information. Amazon Web Services (AWS) Rekognition emerges as a powerful solution in this landscape, offering a comprehensive system for image and video analysis through the lens of machine learning and computer vision. This paper delves into the realm of image processing using AWS Rekognition, unveiling the transformative capabilities of this cloud-based service and its applications in various domains. As we embark on this journey, we will explore the principles, methodologies, and real-world implications of leveraging AWS Rekognition for image analysis.

关键词： AWS Rekognition

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