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Wavefront estimation through structured detection in laser scanning microscopy

Biomedical Optics Express 2025年第5期16卷 2135-2155页

作者： Fersini, Francesco Zunino, Alessandro Morerio, Pietro Baldini, Francesca Diaspro, Alberto Booth, Martin J. Del Bue, Alessio Vicidomini, Giuseppe Molecular Microscopy and Spectroscopy Istituto Italiano di Tecnologia Genoa Italy Dipartimento di Informatica Bioingegneria Robotica e Ingegneria dei Sistemi University of Genoa Genoa Italy Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genoa Italy Nanoscopy and NIC@IIT Istituto Italiano di Tecnologia Genoa Italy Department of Physics University of Genoa Genoa Italy Department of Engineering Science University of Oxford Oxford United Kingdom

Laser scanning microscopy (LSM) is the base of numerous advanced imaging techniques, including confocal laser scanning microscopy (CLSM), a widely used tool in life sciences research. However, its effective resolution is often compromised by optical aberrations, a common challenge in all optical systems. While adaptive optics (AO) can correct these aberrations, current methods face significant limitations: aberration estimation, which is central to any AO approach, typically requires specialized hardware or prolonged sample exposure, rendering these methods sample-invasive, and less user-friendly. In this study, we propose a simple and efficient AO strategy for CLSM systems equipped with a detector array – image-scanning microscopy – and an AO element for beam shaping. We demonstrate, for the first time, that datasets acquired with a detector array inherently encode aberration information. As a proof-of-concept of this important property, we designed a custom convolutional neural network capable of decoding aberrations up to the 11th Zernike coefficient, directly from a single acquisition. While this data-driven approach represents an initial exploration of the aberration content, it opens the door to more advanced decoding strategies – including model-based methods. This work establishes a new paradigm for aberration sensing in LSM and is designed to work synergistically with conventional AO approaches such as phase diversity, enabling faster, less invasive, and more accessible high-resolution imaging. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Aberrations

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

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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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"Connected to the people": Social Inclusion & Cohesion in Action through a Cultural Heritage Digital Tool

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Proceedings of the ACM on Human-computer Interaction 2023年第CSCW2期7卷 1-37页

作者： Nisi, Valentina Bala, Paulo Cesário, Vanessa James, Stuart Del Bue, Alessio Nunes, Nuno Jardim Instituto Superior Técnico U. Lisbon Lisbon Portugal Visual Geometry and Modelling Lab Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy

Current cultural policies are evolving from social inclusion (removing barriers and promoting equality for participation in culture) to social cohesion (fostering solid bonds between groups despite their differences). Digital interventions can create spaces that promote social inclusion and cohesion. In this paper, we report on the design and evaluation of a cultural heritage and digital storytelling application supporting a participatory approach to culture and hosting society. We evaluate our intervention in three marginalized communities with different social-cultural contexts: migrant women in Barcelona, a community living in a priority neighbourhood in Paris and second and third-generation migrants in Lisbon. Through an analysis of their application use, our findings point at their needs and desires, highlighting how the app can support social inclusion as the first step towards cohesion, but that these are heterogeneous concepts susceptible to nuanced appropriations by the different communities. © 2023 ACM.

关键词： Digital devices

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Visual place recognition for multi-robots maps merging

Visual place recognition for multi-robots maps merging

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2012 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2012

作者： Li, Zhao Un Nabi Jafri, Syed Riaz Chellali, Ryad Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Universita Degli Studi di Genova Italy Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Italy

ISBN: (纸本)9781479901654

This paper presents a new approach allowing to a group of robots team to merge their individual maps without any a priori knowledge about their relative positions. While moving, each robot acquires laser data and a video stream. The first data are used to create individual maps (SLAM), while images sequences are used to derive invariant visual descriptions of the visited places. The later are then exchanged between robots to determine a probability of being close to each other and sharing a common place in order to initiate a map merging process. In such a way, ambiguous situations that usually occur when a single sensor is used are reduced. We present the solution we developed in order to extract a compact visual description that remains constant regardless to the actual pose of the robots. Two or more robots having such description can verify if they are or not present at the same place, before merging their respective individual maps. This scheme, e.g. the fusion of laser-range data and visual information enhances and accelerates the construction of the global map and consequently disambiguate individual maps. The validation of the approach has been performed on various indoor environments mainly, office-like spaces and houses. © 2012 IEEE.

关键词： Robots

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Algorithmic Advances in Riemannian Geometry and Applications 1

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丛书名： Advances in computer vision and pattern Recognition

1000年

作者： Hà Quang Minh Vittorio Murino

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Multi robot SLAM for features based environment modelling

Multi robot SLAM for features based environment modelling

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11th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2014

作者： Riaz Un Nabi Jafri, Syed Ahmed, Waheed Ashraf, Zubair Chellali, Ryad Electronic Engineering Department NED UET Karachi Pakistan Pattern Analysis and Computer Vision LAB-IIT Genova Italy

ISBN: (纸本)9781479939787

This paper is presenting a multi robot simultaneous localization and mapping (SLAM) framework for environment modelling using 2D and 3D features. The proposed solution is using a team of mobile robots which are exploring unknown environment with unknown poses. Each team member is allowed to build its independent features based SLAM solution and to share the local map model among other mates. By matching the overlapping tendency of any two mates, a map merging strategy is introduced which in result is building global map. The overall approach has tested in 2D and 3D features based environment and results have shown. © 2014 IEEE.

关键词： Extended Kalman filters

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Online Evaluation of Textile Yarn Evenness Using Fast Exposure Imaging and Image Processing Techniques 7

Online Evaluation of Textile Yarn Evenness Using Fast Exposu...

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7th International Conference on Automation, Control and Robots, ICACR 2023

作者： Haleem, Noman Bustreo, Matteo Del Bue, Alessio University of Groningen Campus Fryslan Leeuwarden Netherlands Istituto Italiano di Tecnologia Pattern Analysis & Computer Vision Genova Italy

ISBN: (纸本)9798350302882

Evenness is regarded as the most important attribute of textile yarn quality as irregular yarns reduce efficiency of spinning process and deteriorate quality of resultant fabrics. In traditional yarn spinning process, evenness is tested offline using laboratory scale testers through sampling from a whole lot of yarns. The lag associated with this approach limits agility in responding to a variety of process variations whereas limited sampling restricts achieving fine grained yarn quality insights. These factors together result in limited control on yarn spinning process and eventually compromise quality of the yarn *** aim to address this problem by developing a new yarn evenness testing system, which can be deployed on an individual spinning position to evaluate yarn quality in an online manner. The proposed system, which is based on a combination of fast exposure imaging and image processing techniques, was first validated using a control set of yarn specimens and later tested in normal yarn production through comparison with an existing offline yarn evenness tester. The results demonstrate promising potential of the proposed system for wider application in yarn spinning industry. © 2023 IEEE.

关键词： Textile industry

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Geometric image labeling with global convex labeling constraints 11th

Geometric image labeling with global convex labeling constra...

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11th International Conference on Energy Minimization Methods in computer vision and pattern Recognition, EMMVCPR 2017

作者： Zern, Artjom Rohr, Karl Schnörr, Christoph Image and Pattern Analysis Group Heidelberg University Heidelberg Germany Biomedical Computer Vision Group BIOQUANT Heidelberg University Heidelberg Germany

ISBN: (纸本)9783319781983

In [2], a smooth geometric labeling approach was introduced by following the Riemannian gradient flow of a given objective function on the so-called assignment manifold. The approach evaluates a user-defined data term and performs spatial regularization by Riemannian averaging of the assignment vectors. In this paper, we extend this approach in order to impose global convex constraints on the labeling results based on linear filter statistics in the label space. The smoothness of the approach is preserved by using logarithmic barrier functions to handle the new constraints. We discuss how suitable filters can be determined from example data of a given image class, and we demonstrate numerically the effectiveness of the constraints in several academic labeling scenarios. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Geometry

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Semi-supervised multi-feature learning for person re-identification

Semi-supervised multi-feature learning for person re-identif...

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2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013

作者： Figueira, Dario Bazzani, Loris Minh, Ha Quang Cristani, Marco Bernardino, Alexandre Murino, Vittorio Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy ISR Instituto Superior Técnico Lisboa Portugal

ISBN: (纸本)9781479907038

Person re-identification is probably the open challenge for low-level video surveillance in the presence of a camera network with non-overlapped fields of view. A large number of direct approaches has emerged in the last five years, often proposing novel visual features specifically designed to highlight the most discriminant aspects of people, which are invariant to pose, scale and illumination. On the other hand, learning-based methods are usually based on simpler features, and are trained on pairs of cameras to discriminate between individuals. In this paper, we present a method that joins these two ideas: given an arbitrary state-of-the-art set of features, no matter their number, dimensionality or descriptor, the proposed multi-class learning approach learns how to fuse them, ensuring that the features agree on the classification result. The approach consists of a semi-supervised multi-feature learning strategy, that requires at least a single image per person as training data. To validate our approach, we present results on different datasets, using several heterogeneous features, that set a new level of performance in the person re-identification problem. © 2013 IEEE.

关键词： Cameras

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