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CodePhys: Robust Video-Based Remote Physiological Measurement Through Latent Codebook Querying

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Chu, Shuyang Xia, Menghan Yuan, Mengyao Liu, Xin Seppanen, Tapio Zhao, Guoying Shi, Jingang Xi'an Jiaotong University School of Software Engineering Xi'an China Tencent Ai Lab Shenzhen China Lappeenranta-Lahti University of Technology Lut Computer Vision and Pattern Recognition Laboratory Lappeenranta53850 Finland University of Oulu Center for Machine Vision and Signal Analysis Finland

Remote photoplethysmography (rPPG) aims to measure non-contact physiological signals from facial videos, which has shown great potential in many applications. Most existing methods directly extract video-based rPPG features by designing neural networks for heart rate estimation. Although they can achieve acceptable results, the recovery of rPPG signal faces intractable challenges when interference from real-world scenarios takes place on facial video. Specifically, facial videos are inevitably affected by non-physiological factors (e.g., camera device noise, defocus, and motion blur), leading to the distortion of extracted rPPG signals. Recent rPPG extraction methods are easily affected by interference and degradation, resulting in noisy rPPG signals. In this paper, we propose a novel method named CodePhys, which innovatively treats rPPG measurement as a code query task in a noise-free proxy space (i.e., codebook) constructed by ground-truth PPG signals. We consider noisy rPPG features as queries and generate high-fidelity rPPG features by matching them with noise-free PPG features from the codebook. Our approach also incorporates a spatial-aware encoder network with a spatial attention mechanism to highlight physiologically active areas and uses a distillation loss to reduce the influence of non-periodic visual interference. Experimental results on four benchmark datasets demonstrate that CodePhys outperforms state-of-the-art methods in both intra-dataset and cross-dataset settings. © 2025 IEEE.

关键词： Heart

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CodePhys: Robust Video-based Remote Physiological Measurement through Latent Codebook Querying

arXiv

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arXiv 2025年

作者： Chu, Shuyang Xia, Menghan Yuan, Mengyao Liu, Xin Seppanen, Tapio Zhao, Guoying Shi, Jingang The School of Software Engineering Xi’an Jiaotong University Xi’an China The Tencent AI Lab Shenzhen China The Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT Lappeenranta53850 Finland The Center for Machine Vision and Signal Analysis University of Oulu Finland

关键词： Heart

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Kernel-based classification for brain connectivity graphs on the Riemannian manifold of positive definite matrices 12

Kernel-based classification for brain connectivity graphs on...

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12th IEEE International Symposium on Biomedical Imaging, ISBI 2015

作者： Dodero, Luca Minh, Ha Quang Biagio, Marco San Murino, Vittorio Sona, Diego Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy NeuroInformatics Laboratory Fondazione Bruno Kessler Trento Italy

ISBN: (纸本)9781479923748

An important task in connectomics studies is the classification of connectivity graphs coming from healthy and pathological subjects. In this paper, we propose a mathematical framework based on Riemannian geometry and kernel methods that can be applied to connectivity matrices for the classification task. We tested our approach using different real datasets of functional and structural connectivity, evaluating different metrics to describe the similarity between graphs. The empirical results obtained clearly show the superior performance of our approach compared with baseline methods, demonstrating the advantages of our manifold framework and its potential for other applications. © 2015 IEEE.

关键词： Geometry

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Multi-spectral scale and rotation invariant associative memory method for pattern recognition

Multi-spectral scale and rotation invariant associative memo...

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Optics, Illumination, and Image Sensing for Machine vision III 1988

作者： Kimball, Steven F. Messner, Richard A. University of New Hampshire Synthetic Vision and Pattern Analysis Laboratory Department of Electrical and Computer Engineering DurhamNH03824 United States

The method of using log-polar mapping with spectral band separation to form feature vectors for an associative memory is investigated. Log-polar mapping is used to provide the scale and rotation invariant properties. This reduces the teaching time and total capacity requirement for the associative memory because all orientations and sizes of the target object need not be learned. The images are also broken down into spatial frequency bands. The use of this technique is motivated by evidence that similar processes occur in the human visual system. Suspected benefits include enhanced recognition in the presence of noise and improved network robustness. Simulation results are presented for the case of character recognition. © 1989 SPIE. All rights reserved.

关键词： Associative processing

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Effective brain connectivity through a constrained autoregressive model 1

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1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2016

作者： Crimi, Alessandro Dodero, Luca Murino, Vittorio Sona, Diego Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genoa Italy Department of Computer Science University of Verona Verona Italy Neuroinformatics Laboratory Fondazione Bruno Kessler Trento Italy

ISBN: (数字)9783319467207

ISBN: (纸本)9783319467191

Integration of functional and structural brain connectivity is a topic receiving growing attention in the research community. Their fusion can,in fact,shed new light on brain functions. Targeting this issue,the manuscript proposes a constrained autoregressive model allowing to generate an "effective" connectivity matrix that model the structural connectivity integrating the functional activity. In practice,an initial structural connectivity representation is altered according to functional data,by minimizing the reconstruction error of an autoregressive model constrained by the structural prior. The proposed model has been tested in a community detection framework,where the brain is partitioned using the effective network across multiple subjects. Results showed that using the effective connectivity the resulting clusters better describe the functional interactions of different regions while maintaining the structural organization. © Springer International Publishing AG 2016.

关键词： Brain mapping

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Adaptive and safe mobile manipulator for human robot interaction

Adaptive and safe mobile manipulator for human robot interac...

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IEEE Workshop on Robotic Intelligence in Informationally Structured Space, RIISS

作者： Amal Meddahi Ryad Chellali Pattern Analysis and Computer Vision Laboratory Istituto Italiano di Tecnologia Università degli Studi di Genova Genova Italy Pattern Analysis and Computer Vision Laboratory Istituto Italiano di Tecnologia Genova Italy

Mobile robotic manipulators will be more and more present in our daily life. Many applications use already these platforms such as for homecare, industrial logistics, etc. and most of the time with in vicinity of humans, within dynamic and unknown environments. This poses the problem of adaptability and safety: robots should adapt to support a wide range of human users behaviors, with a strong guaranty of users' safety. In addition, such functions should take place with environments that are dynamic and not known a priori. This contribution reports on the mobile manipulator we are developing. We focus on solving the safe path planning problem for both the robot base and the robot manipulator in the presence of humans and dynamic obstacles: a safety distance should be strictly maintained by the robot with regard to the person involved in the interactive task as well as with any other person or moving object potentially colliding with the robot while performing the assigned task. These two requirements are achieved in real time by a multi-objective optimization that controls the base and the manipulator based on the task definition and the sensing data provided by the embedded sensors. Simulated scenarios have been tested and the obtained results are presented and discussed.

关键词： Robot kinematics Mobile communication Robot sensing systems Manipulator dynamics Mobile robots

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Visualization of High Dimensional Data using an Automated 3D Star Co-ordinate System

Visualization of High Dimensional Data using an Automated 3D...

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International Joint Conference on Neural Networks (IJCNN)

作者： J.S. Shaik M. Yeasin Computer Vision Pattern and Image Analysis Laboratory Electrical and Computer Engineering University of Memphis Memphis TN USA

This paper presents a 3D star coordinate-based visualization technique for exploratory data analysis. To improve the data visualization and reveal the hidden patterns in complex high dimensional data sets, first the 2D star coordinate system is extended to the 3D star coordinate system. An autonomous procedure is defined to find the best configuration for the 3D star coordinate system based on cluster validation measures. To illustrate the efficacy of the proposed techniques, empirical analysis were conducted on a number of synthetic (Five dimensional Gaussian distribution with three classes) and real (Fisher's IRIS, Leukemia, Gastric cancer and Petroleum datasets) databases. Empirical analyses shows that automated 3D star coordinate system helps in better visualization of the complex high dimensional data when compared to 2D star coordinate system and also other projection-based visualization techniques. Also the automated configuration for 3D star coordinate system reveals the hidden patterns in the complex datasets without human intervention.

关键词： Data visualization Data analysis Data mining Visual databases Humans computer vision Image analysis Laboratories Coordinate measuring machines Gaussian distribution

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A distributed multi robot SLAM system for environment learning

A distributed multi robot SLAM system for environment learni...

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IEEE Workshop on Robotic Intelligence in Informationally Structured Space, RIISS

作者： Syed Riaz un Nabi Jafri Ryad Chellali Pattern Analysis and Computer Vision (PAVIS) Laboratory Istituto Italiano di Tecnologia Universita degli Studi di Genova Genova Italy Pattern Analysis and Computer Vision (PAVIS) Laboratory Istituto Italiano di Tecnologia Genova Italy

This paper presents a multi mobile robot simultaneous localization and mapping (SLAM) system for feature based environment learning by using team of exploring robots. The environmental information is measured through the dynamic sensor network in the shape of moving robots with unknown initial poses. Each robot is equipped with 2D laser scanner and a webcam and it serves as a moving sensor node to perceive horizontal and vertical line features respectively. All the moving nodes are responsible to build the informational structured space. The proposed system is using a unified Extended Kalman Filter (EKF) based SLAM framework for each robot which eventually builds a line feature based partial 3D model of the environment. Each moving robotic sensor node then shares its feature based map model to other moving nodes which are in communication range. A global map model is then transformed after getting mutual pose estimation of the robots by matching mutual common map features in addition by taking visual confirmation of other robot. The proposed system has been tested in an indoor environment and results are shown in the paper.

关键词： Simultaneous localization and mapping Cameras Feature extraction Lasers Robot vision systems

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A Progressive Framework for Two-Way Clustering Using Adaptive Subspace Iteration for Functionally Classifying Genes

A Progressive Framework for Two-Way Clustering Using Adaptiv...

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International Joint Conference on Neural Networks (IJCNN)

作者： J.S. Shaik M. Yeasin Computer Vision Pattern and Image Analysis Laboratory Department of Electrical and Computer Engineering University of Memphis Memphis TN USA

This paper presents an adaptive subspace based two-way clustering of microarray data. To analyze the data at various scales a "Progressive" framework is introduced. The goals are to functionally classify genes and also to find differentially expressed genes in microarray expression profiles. Empirical analysis on Colon Cancer dataset shows that ASI performs favorably in grouping genes with similar functions and finding genes that may have been involved in the formation of colon cancer. It was also observed that the proposed algorithm is robust against ordering of samples and yield results consistent with ground truth information.

关键词： Clustering algorithms Cancer Colon Data analysis Robustness Biological tissues Proteins computer vision Image analysis Performance analysis

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Functionally Classifying Genes from Microarray Data Using Linear and Non-linear Data Projection

Functionally Classifying Genes from Microarray Data Using Li...

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ACS/IEEE International Conference on computer Systems and Applications

作者： J. Shaik M. Yeasin Computer Vision Pattern and Image Analysis (CVPIA) laboratory Department of Electrical and Computer Engineering University Of Memphis Memphis TN USA

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