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检索条件"任意字段=1997 Conference on Visual Communications and Image Processing"

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Registration method of 3-D information on a single image by rotating coupled mirrors

Registration method of 3-D information on a single image by ...

引用

1997 IEEE Pacific Rim conference on communications, Computers and Signal processing

作者： Kawasue, K Iwamoto, I Ishimatsu, T Sasebo Natl Coll of Technology Sasebo Japan

ISBN: (纸本)0780339061

A new approach to the three-dimensional measurement of the object surface is introduced. A single TV camera with an apparatus to add the circular bias to the image enables us to record the three-dimensional information of measuring points as circular streaks on a single image. Every shape of the circular streak on the image plane is related to the position of the measuring point. The information is extracted from the image using an image processing technique.

关键词： Stereo vision

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

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

引用

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...

引用

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

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

引用

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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Stereoscopic image reflection removal based on Wasserstein Generative Adversarial Network

Stereoscopic image reflection removal based on Wasserstein G...

引用

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

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

引用

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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LMM-driven Semantic image-Text Coding for Ultra Low-bitrate Learned image Compression

LMM-driven Semantic Image-Text Coding for Ultra Low-bitrate ...

引用

2024 conference on visual communications and image processing

作者： Murai, Shimon Sun, Heming Katto, Jiro Waseda Univ Sch Fundamental Sci & Engn Tokyo Japan Yokohama Natl Univ Fac Engn Yokohama Kanagawa Japan

ISBN: (纸本)9798331529543;9798331529550

Supported by powerful generative models, low-bitrate learned image compression (LIC) models utilizing perceptual metrics have become feasible. Some of the most advanced models achieve high compression rates and superior perceptual quality by using image captions as sub-information. This paper demonstrates that using a large multi-modal model (LMM), it is possible to generate captions and compress them within a single model. We also propose a novel semantic-perceptual-oriented fine-tuning method applicable to any LIC network, resulting in a 41.58% improvement in LPIPS BD-rate compared to existing methods. Our implementation and pre-trained weights are available at https://***/tokkiwa/imageTextCoding.

关键词： Learned image Compression (LIC) Large Multi-modal Model (LMM) Latent Diffusion Model (LDM)

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Enhanced Saliency Prediction via Orientation Selectivity

Enhanced Saliency Prediction via Orientation Selectivity

引用

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

作者： Ye, Peng Wang, Yongfang Xia, Yumeng Shanghai Univ Shanghai Inst Adv Commun & Data Sci Shanghai 200444 Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China

ISBN: (纸本)9781728180687

Saliency prediction can be treated as the activity of the human visual system (HVS). The most effective method should highly approximate the response of HVS to the perceived information. Motivated by that orientation selectivity (OS) mechanism occuring in primary visual cortex (PVC) tells us how the HVS extracts visual information for scene understanding, we propose a novel saliency model by combining an orientation selectivity based local feature called "excitement" map and a visual acuity based global feature called "acuity" map. Further, a saliency augmented operator based on visual error sensitivity is designed to enhance the saliency map. Experimental results on three benchmark databases demonstrate the superior performance of the proposed method compared to ten classical/state-of-the-art algorithms.

关键词： Saliency Orientation Selectivity visual Acuity visual Error Sensitivity

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Inter-Observer visual Congruency in Video-Viewing

Inter-Observer Visual Congruency in Video-Viewing

引用

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

作者： Yue, Jiaomin Lu, Qiang Zhu, Dandan Min, Xiongkuo Zhang, Xiao-Ping Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada

ISBN: (纸本)9781728185514

There are individual differences in human visual attention between observers when viewing the same scene. Inter-observer visual congruency (IOVC) describes the dispersion between different people's visual attention areas when they observe the same stimulus. Research on the IOVC of video is interesting but lacking. In this paper, we first introduce the measurement to calculate the IOVC of video. And an eye-tracking experiment is conducted in a realistic movie-watching environment to establish a movie scene dataset. Then we propose a method to predict the IOVC of video, which employs a dual-channel network to extract and integrate content and optical flow features. The effectiveness of the proposed prediction model is validated on our dataset. And the correlation between inter-observer congruency and video emotion is analyzed.

关键词： Inter-observer visual congruency visual attention neural network movie analysis

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Motion estimation using segmentation and consistency constraint

Motion estimation using segmentation and consistency constra...

引用

1997 conference on visual communications and image processing

作者： Wang, DM Haighton, P Wang, LM Vincent, A Communications Research Ctr. (Canada)

ISBN: (纸本)0819424358

This paper presents an approach to realistic motion field estimation. In this approach, an image is first segmented into homogeneous regions using a new multiscale gradient algorithm followed by watershed transformation. The multiscale gradient algorithm efficiently solves the over-segmentation problem of watershed transformation, increases segmentation accuracy and reduces the computational cost. The motion field is then estimated using block-matching with a consistency constraint. The consistency constraint function is defined by the neighboring motion vectors and the segmentation map. Simulation results show that the motion fields generated by the block-matching with consistency constraint are very smooth within each object, approaching realistic motion fields, even when a small block size is used.

关键词： image sequence analysis motion analysis motion estimation video coding

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