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Oxygen Saturation Estimation Based on Optimal Band Selection from Multi-band Video

Oxygen Saturation Estimation Based on Optimal Band Selection...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Takahashi, Ryo Ashida, Koichi Kobayashi, Yasuo Tokunaga, Rumi Kodama, Shuhei Tsumura, Norimichi Chiba Univ Grad Sch Adv Integrat Sci Inage Ku 1-33 Yayoi Chiba Japan ASTRODESIGN Inc Ota Ku 1-5-2 Minami Yukigaya Tokyo Japan Chiba Univ Grad Sch Global & Transdisciplinary Studies Inage Ku 1-33 Yayoi Chiba Japan Chiba Univ Grad Sch Engn Inage Ku 1-33 Yayoi Chiba Japan

ISBN: (纸本)9781665448994

In this study, we propose a method to estimate oxygen saturation by selecting the best bands from video images captured by a multiband camera. Oxygen saturation is one of the most important bioindicators for measuring human health. For example, when a person contracts COVID-19, which is currently prevalent, oxygen uptake does not work properly and oxygen saturation drops without the person being aware of it, which may lead to severe symptoms. Monitoring oxygen saturation is very important so that the person receives treatment before such a situation occurs. The commonly used contact sensor is uncomfortable because of its pressure and it is difficult to wear on a daily basis, so non-contact estimation of oxygen saturation is desirable. To estimate oxygen saturation using a contact sensor, the difference in the absorption coefficients of oxidized hemoglobin and deoxidized hemoglobin is used. Using the same principle, it is possible to estimate oxygen saturation without contact using the signals from two channels obtained by an RGB camera. Currently, many smartphones are equipped with infrared cameras for face recognition, and increasingly more models are equipped with multi-camera systems consisting of RGB and infrared cameras. In such cases, it is difficult to take advantage of the multiple bands because the optimal combination of bands for oxygen saturation estimation varies depending on the imaging environment and the subject. In this study, to select the optimal combination of bands from multi-band video images, we used a Monte Carlo simulation of light scattering on the skin to simulate pulse waves during oxygen saturation changes while measuring the signals with a multi-band camera. We further propose a method to select the most accurate combination for estimating the oxygen saturation based on the features obtained from the pulse wave.

关键词： Oxygen Monte Carlo methods Pulse measurements Face recognition Estimation Light scattering Cameras

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DiffLight: Integrating Content and Detail for Low-light Image Enhancement

DiffLight: Integrating Content and Detail for Low-light Imag...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Yixu Feng Shuo Hou Haotian Lin Yu Zhu Peng Wu Wei Dong Jinqiu Sun Qingsen Yan Yanning Zhang Northwestern Polytechnical University Xi’an University of Architecture and Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Low Light Image Enhancement (LLIE) task has been a hotspot in low-level computer vision research. The camera sensor can only capture a small amount of ambient light signal in low-light condition, resulting in significant noise black pseudo artifacts in images, which not only degrade visual quality but also affect the performance of down-stream visual tasks. However, current methods often produce overly smoothed and distorted results, or introduce strong noise artifacts. Moreover, for recent UHD high-definition low-light images, due to GPU memory limitations, LLIE must be conducted in patches, leading to block artifacts. Faced with these challenges, we propose a dual-branch pipeline called DiffLight. Specifically, it consists of the Denoising Enhancement (DE) branch and the Detail Preservation (DP) branch. The DE-branch adopts a combination of DiffIR and LEDNet to reduce noise and enhance brightness, while the DP-branch utilizes a novel Light Full-Former (LFF) method, which comprises 20 Full-Attention (LFA) modules to preserve full-scale image details. To tackle block artifacts, we further introduce Progressive Patch Fusion (PPF) for patch fusion. Experimental results demonstrate that our approach is high-ranked in the cvpr2024 NTIRE Low Light Enhancement challenge and produced state-of-the (SOTA) results on other datasets.

关键词： Visualization computer vision Image color analysis conferences Pipelines Noise reduction Graphics processing units

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Snapshot Spectral Imaging for Face Anti-Spoofing: Addressing Data Challenges with Advanced Processing and Training

Snapshot Spectral Imaging for Face Anti-Spoofing: Addressing...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Hui Li Yaowen Xu Zhaofan Zou Zhixiang He China Telecom Artificial Intelligence Technology Co. Ltd Beijing China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Although considerable research progress has been made in the field of face anti-spoofing(FAS), it still faces continuous threats from ultra-realistic face mask attacks. Facing the challenge of highly realistic flexible masks, spectral sensors show great potential in enhancing the safety of FAS systems. However, the application of snapshot spectral imaging (SSI) in FAS is still in its infancy and faces two major challenges: data scarcity and data content differences. To this end, we introduce a data processing and model training method for SSI images. In terms of data processing, we use RandomBorderMask technology and RandomDropChannels strategy to avoid misjudgment of material information, reduce interference from redundant information, and learn from RGB image preprocessing methods to enhance data diversity. Regarding model training, we propose a model integration strategy and semi-supervised learning technology, which combines the prediction results of multiple models and uses pseudo labels to expand the training data to solve the over-fitting problem caused by data scarcity. These innovative methods achieved first-class results in the 5th Face Anti-Spoofing Challenge @cvpr2024, verifying their effectiveness in improving the accuracy and robustness of the FAS system.

关键词： Training Accuracy Face recognition Imaging Interference Predictive models Semisupervised learning

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Dynamic Feature Queue for Surveillance Face Anti-spoofing via Progressive Training

Dynamic Feature Queue for Surveillance Face Anti-spoofing vi...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Keyao Wang Mouxiao Huang Guosheng Zhang Haixiao Yue Gang Zhang Yu Qiao Department of Computer Vision Technology (VIS) Baidu Inc. ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

In recent years, face recognition systems have faced increasingly security threats, making it essential to employ Face Anti-spoofing (FAS) to protect against various types of attacks in traditional scenarios like phone unlocking, face payment and self-service security inspection. However, further exploration is required to fully secure FAS in long-distance settings. In this paper, we propose two contributions to enhance the security of face recognition systems: Dynamic Feature Queue (DFQ) and Progressive Training Strategy (PTS). DFQ converts the conventional binary classification task into a multi-classification task. It treats live samples as a closed set and attack samples as an open set by using a dynamic queue that stores the features of spoofing samples and updates them. On the other hand, PTS targets difficult samples and iteratively adds them in batches for training. The proposed PTS divides the entire training set into blocks, trains only a small portion of the data, and gradually increases the training data with each stage while also incorporating low-scoring positive samples and high-scoring spoof samples from the test set. These two contributions complement each other by enhancing the model’s ability to generalize and defend against various types of attacks, making the face recognition system more secure and reliable. Our proposed methods have achieved top performance on ACER metric with 4.73% on the SuHiFiMask dataset [11] and won the first prize in Surveillance Face Anti-spoofing track of the Challenge@cvpr 2023.

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Thermal Image Super-Resolution Challenge - PBVS 2021

Thermal Image Super-Resolution Challenge - PBVS 2021

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Rivadeneira, Rafael E. Sappa, Angel D. Vintimilla, Boris X. Nathan, Sabari Kansal, Priya Mehri, Armin Ardakani, Parichehr Behjati Dalal, Anurag Akula, Aparna Sharma, Darshika Pandey, Shashwat Kumar, Basant Yao, Jiaxin Wu, Rongyuan Feng, Kai Li, Ning Zhao, Yongqiang Patel, Heena Chudasama, Vishal Prajapati, Kalpesh Sarvaiya, Anjali Upla, Kishor P. Raja, Kiran Ramachandra, Raghavendra Busch, Christoph Almasri, Feras Vandamme, Thomas Debeir, Olivier Gutierrez, Nolan B. Nguyen, Quan H. Beksi, William J. Escuela Super Politecn Litoral ESPOL Fac Ingn Elect & Comp CIDIS Campus Gustavo Galindo Km 30-5 Via Perimetral Guayaquil Ecuador Comp Vis Ctr Campus UAB Barcelona 08193 Spain Couger Inc Tokyo Japan Cent Sci Instruments Org Chandigarh India Indian Inst Engn Sci & Technol Sibpur India MNNIT Allahabad Prayagraj India Northwestern Polytech Univ Xian Peoples R China SVNIT Surat India NTNU Gjovik Norway Univ Libre Bruxelles Brussels Belgium Univ Texas Arlington Arlington TX 76019 USA

ISBN: (纸本)9781665448994

This paper presents results from the second Thermal Image Super-Resolution (TISR) challenge organized in the framework of the Perception Beyond the Visible Spectrum (PBVS) 2021 workshop. For this second edition, the same thermal image dataset considered during the first challenge has been used;only mid-resolution (MR) and high-resolution (HR) sets have been considered. The dataset consists of 951 training images and 50 testing images for each resolution. A set of 20 images for each resolution is kept aside for evaluation. The two evaluation methodologies proposed for the first challenge are also considered in this opportunity. The first evaluation task consists of measuring the PSNR and SSIM between the obtained SR image and the corresponding ground truth (i.e., the HR thermal image downsampled by four). The second evaluation also consists of measuring the PSNR and SSIM, but in this case, considers the x2 SR obtained from the given MR thermal image;this evaluation is performed between the SR image with respect to the semi-registered HR image, which has been acquired with another camera. The results out-performed those from the first challenge, thus showing an improvement in both evaluation metrics.

关键词： Training Measurement computer vision conferences Superresolution Cameras pattern recognition

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Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report

Fast and Accurate Single-Image Depth Estimation on Mobile De...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ignatov, Andrey Malivenko, Grigory Plowman, David Shukla, Samarth Timofte, Radu Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Wang, Yiran Li, Xingyi Shi, Min Xian, Ke Cao, Zhiguo Du, Jin-Hua Wu, Pei-Lin Ge, Chao Yao, Jiaoyang Tu, Fangwen Li, Bo Yoo, Jung Eun Seo, Kwanggyoon Xu, Jialei Li, Zhenyu Liu, Xianming Jiang, Junjun Chen, Wei-Chi Joya, Shayan Fan, Huanhuan Kang, Zhaobing Li, Ang Feng, Tianpeng Liu, Yang Sheng, Chuannan Yin, Jian Benavides, Fausto T. Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Raspberry Pi Trading Ltd Cambridge England AI Witchlabs Zurich Switzerland Tencent GY Lab Shenzhen Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Sch Artificial Intelligence & Automat Wuhan Peoples R China Chinese Acad Sci Inst Automat Nanjing Artificial Intelligence Chip Res Beijing Peoples R China Black Sesame Technol Inc Singapore Singapore Korea Adv Inst Sci & Technol Visual Media Lab Daejeon South Korea Harbin Inst Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China Natl Cheng Kung Univ Multimedia & Comp Vis Lab Tainan Taiwan Samsung Res UK Cambridge England OPPO Res Inst Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper.

关键词： Meters computer vision Image resolution conferences Estimation Real-time systems pattern recognition

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IndoFashion : Apparel Classification for Indian Ethnic Clothes

IndoFashion : Apparel Classification for Indian Ethnic Cloth...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Rajput, Pranjal Singh Aneja, Shivangi Delft Univ Technol Delft Netherlands Tech Univ Munich Munich Germany

ISBN: (纸本)9781665448994

Cloth categorization is an important research problem that is used by e-commerce websites for displaying correct products to the end-users. Indian clothes have a large number of clothing categories both for men and women. The traditional Indian clothes like "Saree" and "Dhoti" are worn very differently from western clothes like t-shirts and jeans. Moreover, the style and patterns of ethnic clothes have a very different distribution from western outfits. Thus the models trained on standard cloth datasets fail on ethnic outfits. We introduce the first large-scale ethnic dataset of over 106K images with 15 different categories for fine-grained classification of Indian ethnic clothes. We gathered a diverse dataset from a large number of Indian e-commerce websites. We then evaluate several baselines for the cloth classification task on our dataset. We obtain 88.43% classification accuracy. We hope that our dataset would foster research in the development of several algorithms such as cloth classification, landmark detection, especially for ethnic clothes. The dataset is publicly available here (1).

关键词： computer vision conferences Clothing pattern recognition Classification algorithms Task analysis Standards

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NTIRE 2021 Learning the Super-Resolution Space Challenge

NTIRE 2021 Learning the Super-Resolution Space Challenge

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Busch, Christoph Chen, Yang Cheng, Jian Chudasama, Vishal Gang, Ruipeng Gao, Shangqi Gao, Kun Gong, Laiyun Han, Qingrui Huang, Chao Jin, Zhi Jo, Younghyun Kim, Seon Joo Kim, Younggeun Lee, Seungjun Lei, Yuchen Li, Chu-Tak Li, Chenghua Li, Ke Liu, Zhi-Song Liu, Youming Nan, Nan Park, Seung-Ho Patel, Heena Peng, Shichong Prajapati, Kalpesh Qi, Haoran Raja, Kiran Ramachandra, Raghavendra Siu, Wan-Chi Son, Donghee Song, Ruixia Upla, Kishor Wang, Li-Wen Wang, Yatian Wang, Junwei Wu, Qianyu Xu, Xinhua Yang, Sejong Yuan, Zhen Zhang, Liting Zhang, Huanrong Zhang, Junkai Zhang, Yifan Zhang, Zhenzhou Zhou, Hangqi Zhu, Aichun Zhuang, Xiahai Zou, Jiaxin Swiss Fed Inst Technol Zurich Switzerland Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Zhejiang Univ Hangzhou Peoples R China NetEaseYunXin Beijing Peoples R China Yonsei Univ Seoul South Korea Seoul Natl Univ Seoul South Korea Univ Ulsan Coll Med Asan Med Ctr Ulsan South Korea Lomin Inc Portland OR USA Fudan Univ Shanghai Peoples R China Simon Fraser Univ Burnaby BC Canada Caritas Inst Higher Educ Hong Kong Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China North China Univ Technol Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China CASIA Beijing Peoples R China NRTA Beijing Peoples R China NCUT Beijing Peoples R China Nanjing Tech Univ Sch Comp Sci & Technol Nanjing Peoples R China Southeast Univ Lab Image Sci & Technol Nanjing Peoples R China SVNIT Surat Gujarat India NTNU Trondheim Norway

ISBN: (纸本)9781665448994

This paper reviews the NTIRE 2021 challenge on learning the super-Resolution space. It focuses on the participating methods and final results. The challenge addresses the problem of learning a model capable of predicting the space of plausible super-resolution (SR) images, from a single low-resolution image. The model must thus be capable of sampling diverse outputs, rather than just generating a single SR image. The goal of the challenge is to spur research into developing learning formulations and models better suited for the highly ill-posed SR problem. And thereby advance the state-of-the-art in the broader SR field. In order to evaluate the quality of the predicted SR space, we propose a new evaluation metric and perform a comprehensive analysis of the participating methods. The challenge contains two tracks: 4x and 8 x scale factor. In total, 11 teams competed in the final testing phase.

关键词： Measurement computer vision conferences Superresolution Predictive models pattern recognition

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Vln↻bert: A Recurrent vision-and-Language BERT for Navigation

Vln↻bert: A Recurrent Vision-and-Language BERT for Navigati...

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2021 ieee/CVF conference on computer vision and pattern recognition, cvpr 2021

作者： Hong, Yicong Wu, Qi Qi, Yuankai Rodriguez-Opazo, Cristian Gould, Stephen The Australian National University Australian Centre for Robotic Vision Australia The University of Adelaide Australian Centre for Robotic Vision Australia

Accuracy of many visiolinguistic tasks has benefited significantly from the application of vision-and-language (V&L) BERT. However, its application for the task of vision- and-language navigation (VLN) remains limited. One reason for this is the difficulty adapting the BERT architecture to the partially observable Markov decision process present in VLN, requiring history-dependent attention and decision making. In this paper we propose a recurrent BERT model that is time-aware for use in VLN. Specifically, we equip the BERT model with a recurrent function that maintains cross-modal state information for the agent. Through extensive experiments on R2R and REVERIE we demonstrate that our model can replace more complex encoder-decoder models to achieve state-of-the-art results. Moreover, our approach can be generalised to other transformer-based architectures, supports pre-training, and is capable of solving navigation and referring expression tasks simultaneously. © 2021 ieee

关键词： Adaptation models Visualization Technological innovation Navigation Bit error rate computer architecture Markov processes

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Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results

Wild Face Anti-Spoofing Challenge 2023: Benchmark and Result...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Dong Wang Jia Guo Qiqi Shao Haochi He Zhian Chen Chuanbao Xiao Ajian Liu Sergio Escalera Hugo Jair Escalante Zhen Lei Jun Wan Jiankang Deng MoreDian InsightFace CASIA Computer Vision Center (UAB) University of Barcelona INAOE

Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of existing approaches to real-world applications remains challenging. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets, which often leads to overfitting during training or saturation during testing. In terms of quantity, the number of spoof subjects is a critical determinant. Most datasets comprise fewer than 2,000 subjects. With regard to diversity, the majority of datasets consist of spoof samples collected in controlled environments using repetitive, mechanical processes. This data collection methodology results in homogenized samples and a dearth of scenario diversity. To address these shortcomings, we introduce the Wild Face Anti-Spoofing (WFAS) dataset, a large-scale, diverse FAS dataset collected in unconstrained settings. Our dataset encompasses 853,729 images of 321,751 spoof subjects and 529,571 images of 148,169 live subjects, representing a substantial increase in quantity. Moreover, our dataset incorporates spoof data obtained from the internet, spanning a wide array of scenarios and various commercial sensors, including 17 presentation attacks (PAs) that encompass both 2D and 3D forms. This novel data collection strategy markedly enhances FAS data diversity. Leveraging the WFAS dataset and Protocol 1 (Known-Type), we host the Wild Face Anti-Spoofing Challenge at the cvpr2023 workshop. Additionally, we meticulously evaluate representative methods using Protocol 1 and Protocol 2 (Unknown-Type). Through an in-depth examination of the challenge outcomes and benchmark baselines, we provide insightful analyses and propose potential avenues for future research. The dataset is released under Insightface 1 .

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