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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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"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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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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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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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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Image analysis and Processing — ICIAP 2015 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Vittorio Murino Enrico Puppo

ISBN: (数字)9783319232348

ISBN: (纸本)9783319232331

The two-volume set LNCS 9279 and 9280 constitutes the refereed proceedings of the 18th International Conference on Image analysis and Processing, ICIAP 2015, held in Genoa, Italy, in September 2015. The 129 papers presented were carefully reviewed and selected from 231 submissions. The papers are organized in the following seven topical sections: video analysis and understanding, multiview geometry and 3D computer vision, pattern recognition and machine learning, image analysis, detection and recognition, shape analysis and modeling, multimedia, and biomedical applications.

关键词： Image Processing and computer vision

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Image analysis and Processing — ICIAP 2015 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Vittorio Murino Enrico Puppo

ISBN: (数字)9783319232317

ISBN: (纸本)9783319232300

关键词： Image Processing and computer vision pattern Recognition Artificial Intelligence Algorithm analysis and Problem Complexity computer Graphics

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Guided Zoom: Questioning network evidence for fine-grained classification 30

Guided Zoom: Questioning network evidence for fine-grained c...

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30th British Machine vision Conference, BMVC 2019

作者： Bargal, Sarah Adel Zunino, Andrea Petsiuk, Vitali Zhang, Jianming Saenko, Kate Murino, Vittorio Sclaroff, Stan Department of Computer Science Boston University United States Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Italy Adobe Research Department of Computer Science University of Verona Italy

We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions. It does so by making sure the model has "the right reasons" for a prediction, defined as reasons that are coherent with those used to make similar correct decisions at training time. The reason/evidence upon which a deep convolutional neural network makes a prediction is defined to be the spatial grounding, in the pixel space, for a specific class conditional probability in the model output. Guided Zoom examines how reasonable such evidence is for each of the top-k predicted classes, rather than solely trusting the top-1 prediction. We show that Guided Zoom improves the classification accuracy of a deep convolutional neural network model and obtains state-of-the-art results on three fine-grained classification benchmark datasets. © 2019. The copyright of this document resides with its authors.

关键词： Forecasting

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