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Point Cloud Geometry Scalable Coding with a Quality-Conditioned Latents Probability Estimator

Point Cloud Geometry Scalable Coding with a Quality-Conditio...

引用

ieee International conference on image processing

作者： Daniele Mari André F. R. Guarda Nuno M. M. Rodrigues Simone Milani Fernando Pereira Università di Padova Italy Instituto de Telecomunicações Portugal ESTG - Politécnico de Leiria Portugal Instituto Superior Técnico – Universidade de Lisboa Portugal

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The widespread usage of point clouds (PC) for immersive visual applications has resulted in the use of very heterogeneous receiving conditions and devices, notably in terms of network, hardware, and display capabilities. In this scenario, quality scalability, i.e., the ability to reconstruct a signal at different qualities by progressively decoding a single bitstream, is a major requirement that has yet to be conveniently addressed, notably in most learning-based PC coding solutions. This paper proposes a quality scalability scheme, named Scalable Quality Hyperprior (SQH), adaptable to learning-based static point cloud geometry codecs, which uses a Quality-conditioned Latents Probability Estimator (QuLPE) to decode a high-quality version of a PC learning-based representation, based on an available lower quality base layer. SQH is integrated in the future JPEG PC coding standard, allowing to create a layered bitstream that can be used to progressively decode the PC geometry with increasing quality and fidelity. Experimental results show that SQH offers the quality scalability feature with very limited or no compression performance penalty at all when compared with the corresponding non-scalable solution, thus preserving the significant compression gains over other state-of-the-art PC codecs.

关键词： Point cloud compression Geometry visualization Codecs image coding Scalability Transform coding

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visual Odometry Drift Reduction Based on LiDAR Point Clouds Alignment 17

Visual Odometry Drift Reduction Based on LiDAR Point Clouds ...

引用

17th ieee International conference on Intelligent Computer Communication and processing, ICCP 2021

作者： Golban, Cǎtǎlin-Cosmin Cobârzan, Corvin-Petruţ Nedevschi, Sergiu Technical University of Cluj-Napoca Computer Science Department Romania Technical University of Cluj-Napoca Image Processing and Pattern Recognition Group Romania

ISBN: (纸本)9781665409766

visual and LiDAR based odometry methods are key enablers for autonomous robots sub-problems such as mapping and localization or temporal aggregation and fusion of sensor data. When it comes to trajectory reconstruction the errors of independent local estimations accumulate over time, resulting in overall drifts from the real trajectory. This paper proposes a method for reducing the global drift by correcting the video-based frame-to-frame trajectory estimation at selected points in time based on the alignment of past and current LiDAR 3D measurements. The main strength of the proposed method is the formulation of the problem as a system of linear equations, which is an adequate numerical approximation as the corrective component is small enough. In this way we eliminate the need to deploy expensive non-linear optimization methods for calculating the correction transform. © 2021 ieee.

关键词： Numerical methods

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Retiformer: Retinex-Based Enhancement In Transformer For Low-Light image

Retiformer: Retinex-Based Enhancement In Transformer For Low...

引用

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

作者： Junxiang Ruan Xiangtao Kong Wenqi Huang Wenming Yang Shenzhen International Graduate School Tsinghua University University of Chinese Academy of Sciences China Southern Power Grid Digital Grid Research Institute Guangzhou China

Transformer-based methods have shown impressive potential in many low-level vision tasks but are rarely used for low-light image enhancement (LLIE). Direct use of Transformer in LLIE will bring unnatural visual effects. This phenomenon encourages us to attempt to learn from the theory of Retinex. After trial and analysis, we finally propose Retiformer. Retiformer decomposes images into reflectance and illumination attention maps by Retinex Window Self-Attention (R-WSA). It will replace element-wise multiplication with the attention mechanism. By the R-WSA, we respectively apply a Decom-Retiformer block and an Enhance-Retiformer block at the head and tail of a Transformer-based backbone. They can decompose and align the reflection and illumination components just like RetinexNet. With this pipeline, Retiformer combines the advantages of Transformer and Retinex theory and achieves state-of-the-art performance of Retinex-based methods.

关键词： Reflectivity Head Lighting Tail Speech enhancement Signal processing Transformers

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3D-Speaker-Toolkit: An Open-Source Toolkit for Multimodal Speaker Verification and Diarization

3D-Speaker-Toolkit: An Open-Source Toolkit for Multimodal Sp...

引用

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

作者： Yafeng Chen Siqi Zheng Hui Wang Luyao Cheng Tinglong Zhu Rongjie Huang Chong Deng Qian Chen Shiliang Zhang Wen Wang Xihao Li Speech Lab Alibaba Group Carnegie Mellon University Zhejiang University University of North Carolina Chapel Hill

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We introduce 3D-Speaker-Toolkit, an open-source toolkit for multimodal speaker verification and diarization, designed for meeting the needs of academic researchers and industrial practitioners. The 3D-Speaker-Toolkit adeptly leverages the combined strengths of acoustic, semantic, and visual data, seamlessly fusing these modalities to offer robust speaker recognition capabilities. The acoustic module extracts speaker embeddings from acoustic features, employing both fully-supervised and self-supervised learning approaches. The semantic module leverages advanced language models to comprehend the substance and context of spoken language, thereby augmenting the system’s proficiency in distinguishing speakers through linguistic patterns. The visual module applies image processing technologies to scrutinize facial features, which bolsters the precision of speaker diarization in multi-speaker environments. Collectively, these modules empower the 3D-Speaker-Toolkit to achieve substantially improved accuracy and reliability in speaker-related tasks. With 3D-Speaker-Toolkit, we establish a new benchmark for multimodal speaker analysis. The toolkit also includes a handful of open-source state-of-the-art models and a large-scale dataset containing over 10,000 speakers. The toolkit is publicly available at https://***/modelscope/3D-Speaker.

关键词： visualization Adaptation models Runtime Semantics Self-supervised learning Signal processing Feature extraction Acoustics Speaker recognition Speech processing

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A robust digital watermarking method for textile image 6

A robust digital watermarking method for textile image

引用

6th ieee International conference on Intelligent Computing and Signal processing, ICSP 2021

作者： Yu, Ying Qiao, Yue Wang, Caiyin Li, Chao Shi, Xiaojuan Dalian Polytechnic University School of Light Industry and Chemical Engineering Dalian China

ISBN: (纸本)9780738143705

In this era of rapid development of network, the infringement of the textile industry is becoming more and more important, which seriously restricts the development of textiles. In this paper, a robust digital watermarking method based on chrominance and texture features of textile images is proposed. The cover image was transformed into CIE LAB color space, and the B channel of the cover image was extracted. The B channel of the carrier image was divided into blocks, and each image block was transformed by DCT. In order to better to maintain the visual effect of the image and the robustness of the watermark, the watermark is embedded in the intermediate frequency domain of DCT. Then the digital image is transferred to the textile by using the dark transfer film through the thermal transfer printing technology. The experimental results indicate that the method is robust to the attack of textile image in the process of print-canning. And the method could coordinate the contradiction between invisibility and robustness. © 2021 ieee.

关键词： Textiles

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Matching in the Dark: A Dataset for Matching image Pairs of Low-light Scenes 18

Matching in the Dark: A Dataset for Matching Image Pairs of ...

引用

18th ieee/CVF International conference on Computer Vision (ICCV)

作者： Song, Wenzheng Suganuma, Masanori Liu, Xing Shimobayashi, Noriyuki Maruta, Daisuke Okatani, Takayuki Tohoku Univ GSIS Sendai Miyagi Japan RIKEN Ctr AIP Tokyo Japan Socionext Inc Yokohama Kanagawa Japan

ISBN: (纸本)9781665428125

This paper considers matching images of low-light scenes, aiming to widen the frontier of SfM and visual SLAM applications. Recent image sensors can record the brightness of scenes with more than eight-bit precision, available in their RAW-format image. We are interested in making full use of such high-precision information to match extremely low-light scene images that conventional methods cannot handle. For extreme low-light scenes, even if some of their brightness information exists in the RAW format images' low bits, the standard raw image processing on cameras fails to utilize them properly. As was recently shown by Chen et al.[14], CNNs can learn to produce images with a natural appearance from such RAW-format images. To consider if and how well we can utilize such information stored in RAW-format images for image matching, we have created a new dataset named MID (matching in the dark). Using it, we experimentally evaluated combinations of eight image-enhancing methods and eleven image matching methods consisting of classical/neural local descriptors and classical/neural initial point-matching methods. The results show the advantage of using the RAW-format images and the strengths and weaknesses of the above component methods. They also imply there is room for further research.

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SSRMamba: Efficient visual State Space Model for Spectral Super-Resolution

SSRMamba: Efficient Visual State Space Model for Spectral Su...

引用

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

作者： Baisong Li Xingwang Wang Haixiao Xu College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Krowledge Engineering of Ministry of Education Jilin University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spectral super-resolution, which reconstructs hyperspectral images (HSI) from a single RGB image, has garnered increasing attention. Due to the limitations of CNN structures in spectral modeling and the high computational cost of Transformer structures, existing deep learning (DL)-based methods struggle to balance spectral reconstruction quality and computational efficiency. Recently, Mamba methods base on state-space models (SSM) show great potential in modeling long-range dependencies with linear complexity. Therefore, we introduce the Mamba model into spectral super-resolution (SSR) task. Specifically, we propose a three-stage SSR network base on Mamba, called SSRMamba. We design SpaMamba, SSMamba, and SpeMamba modules for shallow spatial information extraction, mixed information encoding, and spectral information reconstruction, respectively. Extensive experimental results demonstrate that SSRMamba not only surpasses existing methods in terms of quantification and quality, achieving state-of-the-art (SOTA) performance, but also significantly reduces model size and computational cost. The source code of SSRMamba is available at: https://***/Baisonm-Li/SSRMamba.

关键词： visualization Computational modeling Superresolution Information retrieval Transformers Computational efficiency State-space methods Speech processing image reconstruction Hyperspectral imaging

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Frequency-Domain Guided Multiple Parallel Kernels Network for Low-Light Remote Sensing image Enhancement

Frequency-Domain Guided Multiple Parallel Kernels Network fo...

引用

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

作者： Jingxuan Zhou Hao Li Jinlong Wang Xiongxin Tang Fanjiang Xu Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to dark environments, optical aberrations, etc, the remote sensing images are often submerged under low contrast degradation, which greatly hinders their practical applications for agricultural management and other related tasks. The surface features of remote sensing images are often continuously distributed in space, thus, the sizes of the network’s receptive fields and its ability to learn long-range dependencies are crucial for restoring low-light remote sensing images. Existing methods based on CNN provide limited receptive fields, while Transformer-based methods are constrained by their quadratic computational complexity. To cope with these issues, we propose a novel low-light remote sensing image enhancement network that combines multi-scale receptive fields with frequency-domain attention. Specifically, this network employs multiple parallel kernels of varying sizes to learn multi-scale local features in the spatial domain and complements frequency-domain information to learn global long-range correlations, which achieves local-global feature extraction and further facilitates subsequent degraded images enhancement. We have conducted extensive experiments to demonstrate that our network outperforms existing methods quantitatively and achieves exceptional visual performance, which fully highlights the effectiveness and superiority of our method in enhancing low-light remote sensing images.

关键词： visualization Frequency-domain analysis Transforms Speech enhancement Feature extraction Transformers image restoration Kernel Remote sensing image enhancement

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Watch or Listen: Robust Audio-visual Speech Recognition with visual Corruption Modeling and Reliability Scoring

Watch or Listen: Robust Audio-Visual Speech Recognition with...

引用

conference on Computer Vision and Pattern Recognition (CVPR)

作者： Joanna Hong Minsu Kim Jeongsoo Choi Yong Man Ro Image and Video Systems Lab KAIST

This paper deals with Audio-visual Speech Recognition (AVSR) under multimodal input corruption situations where audio inputs and visual inputs are both corrupted, which is not well addressed in previous research directions. Previous studies have focused on how to complement the corrupted audio inputs with the clean visual inputs with the assumption of the availability of clean visual inputs. However, in real life, clean visual inputs are not always accessible and can even be corrupted by occluded lip regions or noises. Thus, we firstly analyze that the previous AVSR models are not indeed robust to the corruption of multimodal input streams, the audio and the visual inputs, compared to uni-modal models. Then, we design multimodal input corruption modeling to develop robust AVSR models. Lastly, we propose a novel AVSR framework, namely Audio-visual Reliability Scoring module (AV-RelScore), that is robust to the corrupted multimodal inputs. The AV-RelScore can determine which input modal stream is reliable or not for the prediction and also can exploit the more reliable streams in prediction. The effectiveness of the proposed method is evaluated with comprehensive experiments on popular benchmark databases, LRS2 and LRS3. We also show that the reliability scores obtained by AV-RelScore well reflect the degree of corruption and make the proposed model focus on the reliable multimodal representations.

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Occlusion-aware Data Augmentation and Reconstruction for Improving Student Engagement in Online Learning Environments

Occlusion-aware Data Augmentation and Reconstruction for Imp...

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International image processing, Applications and Systems conference (IPAS)

作者： Vincent Pham Sonit Singh School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

The demand for adaptive learning in education is an important application for Computer Vision (CV)-based detection models which extract learners’ faces to classify engagement. However, a loss in visual data due to the partial occlusion of learners’ faces imposes challenge for practical use-cases. In this paper, we propose an occlusion-aware framework to improve the robustness of non-occlusion-aware models in engagement detection. Firstly, our framework consists of an occlusion-aware data augmentation pipeline that aims to simulate the types of partially occluded faces in-the-wild. Secondly, we investigate the application of masked auto-encoders (MAE) for occlusion recovery. Extensive experiments have been performed to measure the effectiveness of the proposed occlusion-aware framework. Specifically, on the challenging FER-2013 dataset our occlusion-aware data augmentation achieves a 5.24% improvement in accuracy on baseline models and a 1.54% improvement against models with augmentation methods used in the state-of-the-art. Alongside the application of MAE, our proposed framework improves baseline classification accuracy by 13.3% and 4.59% on the FER-2013, and DAiSEE dataset for engagement detection. Our investigation into occlusion-aware engagement detection using the DAiSEE dataset provides important insight into the limitations of occlusion-awareness in engagement detection.

关键词： Computer vision visualization Accuracy Computational modeling Pipelines Data augmentation Robustness Data models Faces image reconstruction

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