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31st IEEE conference on Signal processing and communications Applications (SIU)

作者： Ozkose, Yunus Emre Gokce, Zeynep Duygulu, Pinar Hacettepe Univ Bilgisayar Muhendisligi Ankara Turkiye

ISBN: (纸本)9798350343557

In this study, the alignment of video-text and image-text 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.

关键词： dataset alignment deep learning machine learning

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Fast Retrieval of Pharmaceutical Packaging images Using Keypoint Matching with Angle and Scale Voting for Outlier Rejection

Fast Retrieval of Pharmaceutical Packaging Images Using Keyp...

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2024 conference on visual communications and image processing

作者： Zakaria, Yona Ishiyama, Rui Ishidera, Eiki Matsui, Tomokazu Yasumoto, Keiichi Nara Inst Sci & Technol Nara Japan Univ Dodoma Dodoma Tanzania NEC Corp Ltd Tokyo Japan RIKEN Ctr Adv Intelligence Project Tokyo Japan

ISBN: (纸本)9798331529543;9798331529550

Counterfeit medicines present a severe public health threat, especially in low-resource countries where consumers lack reliable means to verify the medicines they purchase. visual inspection of medicine packaging images through keypoint matching techniques offers a promising approach for detecting design inconsistencies that could indicate counterfeit products. However, conventional methods often struggle with high computational costs and reduced accuracy when processing images of varying quality and perspectives. To address these limitations, we propose the Angle and Scale Voting (ASVote) method, which enhances keypoint-based image matching by introducing a 2D voting mechanism that leverages relative angles and scales of the keypoints to eliminate false matches(outliers) while identifying consistent matches (inliers). This approach significantly improves both processing time and accuracy. Experiments on a real-world dataset of medicine packages show that ASVote improves processing time and accuracy, outperforming conventional methods.

关键词： keypoint feature matching RANSAC USAC image retrieval visual inspection counterfeit drugs

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Perceptual Evaluation of Pre-processing for Video Transcoding

Perceptual Evaluation of Pre-processing for Video Transcodin...

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Huang, Shiyu Luo, Ziyuan Xu, Jiahua Zhou, Wei Chen, Zhibo Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei Peoples R China

ISBN: (纸本)9781728185514

Recently, the pre-processed video transcoding has attracted wide attention and has been increasingly used in practical applications for improving the perceptual experience and saving transmission resources. However, very few works have been conducted to evaluate the performance of pre-processing methods. In this paper, we select the source (SRC) videos and various pre-processing approaches to construct the first Pre-processed and Transcoded Video Database (PTVD). Then, we conduct the subjective experiment, showing that compared with the video sent to the codec directly at the same bitrate, the appropriate pre-processing methods indeed improve the perceptual quality. Finally, existing image/video quality metrics are evaluated on our database. The results indicate that the performance of the existing image/video quality assessment (IQA/VQA) approaches remain to be improved. We will make our database publicly available soon.

关键词： Video quality assessment Subjective evaluation Database Pre-processing for video transcoding

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WAVELET image MODELING AND ITS APPLICATION IN MODEL-BASED image-CODING

WAVELET IMAGE MODELING AND ITS APPLICATION IN MODEL-BASED IM...

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conference on visual communications and image processing 94

作者： GUAN, L UNIV SYDNEY DEPT ELECT ENGNSYDNEYNSW 2006AUSTRALIA

ISBN: (纸本)081941638X

This paper describes an image modeling scheme based on wavelet signal decomposition and statistical texture analysis, and its application in model-based image coding. In this approach, the image being considered is first decomposed into octal signal bands which contains different spectral components of the images. Based on the property of the signal decomposition statistical texture analysis techniques are hierarchically employed to separate the image into different categories with distinct modeling parameters. Coding is performed based on the texture image model.

关键词： image compression Wavelets Model-based design Discrete wavelet transforms Wavelet transforms Statistical modeling Motion models image analysis Statistical analysis image transmission

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Lattice vector quantization of image using a product code form and a new labeling method

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Proceedings of SPIE - The International Society for Optical Engineering 1994年第P 1期2308卷 422-433页

作者： Moureaux, Jean-Marie Antonini, Marc Barlaud, Michel Univ. de Nice-Sophia Antipolis Valbonne Fr

The paper develops a new fast and low complexity method for image coding. It is based on Lattice Vector Quantization (for both L1 and L2 norms). A solution is proposed to solve the labelling of quantized vectors, whic... 详细信息

ISBN: (纸本)081941638X;9780819416384

关键词： image coding

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SMRD: A Local Feature Descriptor for Multi-modal image Registration

SMRD: A Local Feature Descriptor for Multi-modal Image Regis...

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Xie, Jiayu Jin, Xin Cao, Hongkun Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Key Lab Broadband Network & Multimedia Shenzhen Peoples R China

ISBN: (纸本)9781728185514

image registration among multimodality has received increasing attention in the scope of computer vision and computational photography nowadays. However, the nonlinear intensity variations prohibit the accurate feature points matching between modal-different image pairs. Thus, a robust image descriptor for multi-modal image registration is proposed, named shearlet-based modality robust descriptor(SMRD). The anisotropic feature of edge and texture information in multi-scale is encoded to describe the region around a point of interest based on discrete shearlet transform. We conducted the experiments to verify the proposed SMRD compared with several state-of-the-art multi-modal/multispectral descriptors on four different multi-modal datasets. The experimental results showed that our SMRD achieves superior performance than other methods in terms of precision, recall and F1-score.

关键词： Multi-modal image registration feature descriptor discrete shearlet transform

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Lossy Coding for Spatially Adaptive Conditioning in Semantic image Communication

Lossy Coding for Spatially Adaptive Conditioning in Semantic...

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2024 conference on visual communications and image processing

作者： Eteke, Cem Griessel, Alexander Kellerer, Wolfgang Steinbach, Eckehard Tech Univ Munich Sch Computat Informat & Technol Chair Media Technol D-80333 Munich Germany Tech Univ Munich Sch Computat Informat & Technol Chair Commun Networks D-80333 Munich Germany Tech Univ Munich Sch Computat Informat & Technol Munich Inst Robot & Machine Intelligence D-80333 Munich Germany

ISBN: (纸本)9798331529543;9798331529550

The increasing demand for high-quality, real-time visual communication and the growing user expectations, coupled with limited network resources, necessitate novel approaches to semantic image communication. This paper presents a method to enhance semantic image communication that combines a novel lossy semantic encoding approach with spatially adaptive semantic image synthesis models. By developing a model-agnostic training augmentation strategy, our approach substantially reduces susceptibility to distortion introduced during encoding, effectively eliminating the need for lossless semantic encoding. Comprehensive evaluation across two spatially adaptive conditioning methods and three popular datasets indicates that this approach enhances semantic image communication at very low bit rate regimes.

关键词： semantic communication lossy semantic coding semantic augmentation image synthesis

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Stereoscopic image reflection removal based on Wasserstein Generative Adversarial Network

Stereoscopic image reflection removal based on Wasserstein G...

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

作者： Wang, Xiuyuan Pan, Yikun Lun, Daniel P. K. Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9781728180687

Reflection removal is a long-standing problem in computer vision. In this paper, we consider the reflection removal problem for stereoscopic images. By exploiting the depth information of stereoscopic images, a new background edge estimation algorithm based on the Wasserstein Generative Adversarial Network (WGAN) is proposed to distinguish the edges of the background image from the reflection. The background edges are then used to reconstruct the background image. We compare the proposed approach with the state-of-the-art reflection removal methods. Results show that the proposed approach can outperform the traditional single-image based methods and is comparable to the multiple-image based approach while having a much simpler imaging hardware requirement.

关键词： Reflection removal GAN stereoscopic images

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Ultra-High Resolution image Segmentation with Efficient Multi-Scale Collective Fusion

Ultra-High Resolution Image Segmentation with Efficient Mult...

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

作者： Sun, Guohao Yan, Haibin Beijing Univ Posts & Telecommun Sch Automat Beijing Peoples R China

ISBN: (纸本)9781665475921

Ultra-high resolution image segmentation has attracted increasing attention recently due to its wide applications in various scenarios such as road extraction and urban planning. The ultra-high resolution image facilitates the capture of more detailed information but also poses great challenges to the image understanding system. For memory efficiency, existing methods preprocess the global image and local patches into the same size, which can only exploit local patches of a fixed resolution. In this paper, we empirically analyze the effect of different patch sizes and input resolutions on the segmentation accuracy and propose a multi-scale collective fusion (MSCF) method to exploit information from multiple resolutions, which can be end-to-end trainable for more efficient training. Our method achieves very competitive performance on the widely-used DeepGlobe dataset while training on one single GPU.

关键词： Semantic segmentation remote sensing ultra-high resolution convolutional neural networks

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Learning From Paired and Unpaired Data: Alternately Trained CycleGAN for Near Infrared image Colorization

Learning From Paired and Unpaired Data: Alternately Trained ...

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

作者： Yang, Zaifeng Chen, Zhenghua ASTAR Inst High Performance Comp Singapore Singapore ASTAR Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781728180687

This paper presents a novel near infrared (NIR) image colorization approach for the Grand Challenge held by 2020 IEEE International conference on visual communications and image processing (VCIP). A Cycle-Consistent Generative Adversarial Network (CycleGAN) with cross-scale dense connections is developed to learn the color translation from the NIR domain to the RGB domain based on both paired and unpaired data. Due to the limited number of paired NIR-RGB images, data augmentation via cropping, scaling, contrast and mirroring operations have been adopted to increase the variations of the NIR domain. An alternating training strategy has been designed, such that CycleGAN can efficiently and alternately learn the explicit pixel-level mappings from the paired NIR-RGB data, as well as the implicit domain mappings from the unpaired ones. Based on the validation data, we have evaluated our method and compared it with conventional CycleGAN method in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and angular error (AE). The experimental results validate the proposed colorization framework.

关键词： VCIP challenge 2020 NIR image colorization CycleGAN UNet paired/unpaired images data augmentation

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