Guillaume Tochon

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17ranked-venue papers

3as first author

6since 2021 · last 2024

0000-0003-4617-4922ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 9 · 4 since 2021Applied, interdisciplinary, general and emerging computing · 5 · 2 first-author · 1 since 2021Artificial intelligence and machine learning · 3 · 1 first-author · 2 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 1 paper		Deep learning architectures and training · 100%
Interdisciplinary, comprehensive, and emerging computing 1 paper		Medical and health informatics · 100%
Computer graphics and multimedia 1 paper		Image and video processing · 100%

Topics — the 4 heaviest of 6, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Medical and health informatics › medical imaging medical image analysis	0.7	1	2023	Why is the Winner the Best? · CVPR 2023
Image and video processing › image segmentation › remote sensing image segmentation hyperspectral image segmentation	0.2	1	2014	Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation · IEEE Trans. Image Process. 2014
Image and video processing image segmentation	0.2	1	2014	Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation · IEEE Trans. Image Process. 2014
Image and video processing › image segmentation › hierarchical segmentation binary partition tree	0.1	1	2014	Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation · IEEE Trans. Image Process. 2014

Methods — techniques the papers use, named apart from their topics

statistical analysis · 1.3spectral unmixing · 0.2pruning strategy · 0.2linear unmixing · 0.2

Year	Publication	Venue	Position
2024	Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021 Carole H. Sudre, Kimberlin M. H. van Wijnen, Florian Dubost, Hieab Adams, David Atkinson, Frederik Barkhof, Mahlet A. Birhanu, Esther Bron, Robin Camarasa, Nish Chaturvedi, Qi Dou 0001, Tavia E. Evans, Ivan Ezhov, Haojun Gao, Marta Gironés-Sangüesa, Juan Domingo Gispert, Beatriz Gomez Anson, Alun D. Hughes, Mohammad Arfan Ikram, Silvia Ingala, Hans Rolf Jäger, Florian Kofler, Hugo J. Kuijf, Denis Kutnar, Bo Li 0088, Luigi Lorenzini, Bjoern Menze, José Luis Molinuevo, Yiwei Pan, Élodie Puybareau, Rafael Rehwald, Ruisheng Su, Lorna Smith, Therese Tillin, Guillaume Tochon, Hélène Urien, Bas H. M. van der Velden, Isabelle F. van der Velpen, Benedikt Wiestler, Frank J. Wolters, Pinar Yilmaz, Marius de Groot, Meike W. Vernooij, Marleen de Bruijne	Medical Image Anal.	40
2023	Structural Analysis of the Additive Noise Impact on the α -tree Baptiste Esteban, Guillaume Tochon, Edwin Carlinet, Didier Verna	CAIP (2)	2
2023	Why is the Winner the Best? abstract International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Tizabi, Fabian Isensee, Tim Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David Gage Ellis, Sandy Engelhardt, Melanie Ganz-Benjaminsen, Noha M. Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, A. Emre Kavur, Oldrich Kodym, Michal Kozubek 0001, Jianning Li 0002, Hongwei Li 0004, Jun Ma 0016, Carlos Martín-Isla, Bjoern Menze, J. Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patino, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin M. H. van Wijnen, Martin Wagner 0001, D. Wei, Amine Yamlahi, Moi Hoon Yap, C. Yuan, Maximilian Zenk, A. Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, J. Cho, C. Choi, Qi Dou 0001, Ivan Ezhov, Christoph M. Friedrich, C. Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García-Faura, Maria Grammatikopoulou, S. Hong, Mostafa Jahanifar, I. Jang, Abdolrahim Kadkhodamohammadi, I. Kang, Florian Kofler, S. Kondo, Hugo J. Kuijf, M. Luu, Tomaz Martincic, Pedro Morais, Mohamed A. Naser, Bruno Oliveira 0002, David Owen 0001, S. Pang, Szymon Plotka, Élodie Puybareau, Nasir M. Rajpoot, K. Ryu, Numan Saeed, Adam J. Shephard, Dejan Stepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Hélène Urien, João L. Vilaça, Kareem A. Wahid, Benedikt Wiestler, Marek Wodzinski, F. Xia, J. Xie, Z. Xiong, Sen Yang 0006, Klaus H. Maier-Hein, Paul F. Jaeger, Annette Kopp-Schneider, Lena Maier-Hein	CVPR	105
2023	Leveraging Neural Koopman Operators to Learn Continuous Representations of Dynamical Systems from Scarce Data abstract Over the last few years, several works have proposed deep learning architectures to learn dynamical systems from observation data with no or little knowledge of the underlying physics. A line of work relies on learning representations where the dynamics of the underlying phenomenon can be described by a linear operator, based on the Koopman operator theory. However, despite being able to provide reliable long-term predictions for some dynamical systems in ideal situations, the methods proposed so far have limitations, such as requiring to discretize intrinsically continuous dynamical systems, leading to data loss, especially when handling incomplete or sparsely sampled data. Here, we propose a new deep Koopman framework that represents dynamics in an intrinsically continuous way, leading to better performance on limited training data, as exemplified on several datasets arising from dynamical systems. Anthony Frion, Lucas Drumetz, Mauro Dalla Mura, Guillaume Tochon, Abdeldjalil Aïssa-El-Bey	ICASSP	4
2022	The Cost of Dynamism in Static Languages for Image Processing abstract Generic programming is a powerful paradigm abstracting data structures and algorithms to improve their reusability, as long as they respect a given interface. Coupled with a performance-driven language, it is a paradigm of choice for scientific libraries where the implementation of manipulated objects may change depending on their use case, or for performance purposes. In those performance-driven languages, genericity is often implemented statically to perform some optimization. This does not fit well with the dynamism needed to handle objects which may only be known at runtime. Thus, in this article, we evaluate a model that couples static genericity with a dynamic model based on type erasure in the context of image processing. Its cost is assessed by comparing the performance of the implementation of some common image processing algorithms in C++ and Rust, two performance-driven languages supporting some form of genericity. Finally, we demonstrate that compile-time knowledge of some specific information is critical for performance, and also that the runtime overhead depends on the algorithmic scheme in use. Baptiste Esteban, Edwin Carlinet, Guillaume Tochon, Didier Verna	GPCE	3
2022	Estimation of the noise level function for color images using mathematical morphology and non-parametric statistics abstract Noise level information is crucial for many image processing tasks, such as image denoising. To estimate it, it is necessary to find homegeneous areas within the image which contain only noise. Rank-based methods have proven to be efficient to achieve such a task. In the past, we proposed a method to estimate the noise level function (NLF) of grayscale images using the tree of shapes (ToS). This method, relying on the connected components extracted from the ToS computed on the noisy image, had the advantage of being adapted to the image content, which is not the case when using square blocks, but is still restricted to grayscale images. In this paper, we extend our ToS-based method to color images. Unlike grayscale images, the pixel values in multivariate images do not have a natural order relationship, which is a well-known issue when working with mathematical morphology and rank statistics. We propose to use the multivariate ToS to retrieve homogeneous regions. We derive an order relationship for the multivariate pixel values thanks to a complete lattice learning strategy and use it to compute the rank statistics. The obtained multivariate NLF is composed of one NLF per channel. The performance of the proposed method is compared with the one obtained using square blocks, and validates the soundness of the multivariate ToS structure for this task. Baptiste Esteban, Guillaume Tochon, Edwin Carlinet, Didier Verna	ICPR	2
2020	Learning Endmember Dynamics in Multitemporal Hyperspectral Data Using A State-Space Model Formulation abstract Hyperspectral image unmixing is an inverse problem aiming at recovering the spectral signatures of pure materials of interest (called endmembers) and estimating their proportions (called abundances) in every pixel of the image. However, in spite of a tremendous applicative potential and the avent of new satellite sensors with high temporal resolution, multitemporal hyperspectral unmixing is still a relatively underexplored research avenue in the community, compared to standard image unmixing. In this paper, we propose a new framework for multitemporal unmixing and endmember extraction based on a state-space model, and present a proof of concept on simulated data to show how this representation can be used to inform multitemporal unmixing with external prior knowledge, or on the contrary to learn the dynamics of the quantities involved from data using neural network architectures adapted to the identification of dynamical systems. Lucas Drumetz, Mauro Dalla Mura, Guillaume Tochon, Ronan Fablet	ICASSP	3
2019	Estimating the Noise Level Function with the Tree of Shapes and Non-parametric Statistics Baptiste Esteban, Guillaume Tochon, Thierry Géraud	CAIP (2)	2
2019	Braids of partitions for the hierarchical representation and segmentation of multimodal images Guillaume Tochon, Mauro Dalla Mura, Miguel Angel Veganzones, Thierry Géraud, Jocelyn Chanussot	Pattern Recognit.	1
2018	A First Step Toward a Fair Comparison of Evaluation Protocols for Text Detection Algorithms abstract Text detection is an important topic in pattern recognition, but evaluating the reliability of such detection algorithms is challenging. While many evaluation protocols have been developed for that purpose, they often show dissimilar behaviors when applied in the same context. As a consequence, their usage may lead to misinterpretations, potentially yielding erroneous comparisons between detection algorithms or their incorrect parameters tuning. This paper is a first attempt to derive a methodology to perform the comparison of evaluation protocols. We then apply it on five state-of-the-art protocols, and exhibit that there indeed exist inconsistencies among their evaluation criteria. Our aim here is not to rank the investigated evaluation protocols, but rather raising awareness in the community that we should carefully reconsider them in order to converge to their optimal usage. end abstract Aliona Dangla, Élodie Puybareau, Guillaume Tochon, Jonathan Fabrizio	DAS	3
2017	Improved Local Spectral Unmixing of hyperspectral data using an algorithmic regularization path for collaborative sparse regression abstract Local Spectral Unmixing (LSU) methods perform the unmixing of hyperspectral data locally in regions of the image. The endmembers and their abundances in each pixel are extracted region-wise, instead of globally to mitigate spectral variability effects, which are less severe locally. However, it requires the local estimation of the number of endmembers to use. Algorithms for intrinsic dimensionality (ID) estimation tend to overestimate the local ID, especially in small regions. The ID only provides an upper bound of the application and scale dependent number of endmembers, which leads to extract irrelevant signatures as local endmembers, associated with meaningless local abundances. We propose a method to select in each region the best subset of the locally extracted endmembers. Collaborative sparsity is used to detect spurious endmembers in each region and only keep the most influent ones. We compute an algorithmic regularization path for this problem, giving access to the sequence of successive active sets of endmembers when the regularization parameter is increased. Finally, we select the optimal set in the sense of the Bayesian Information Criterion (BIC), favoring models with a high likelihood, while penalizing those with too many endmembers. Results on real data show the interest of the proposed approach. Lucas Drumetz, Guillaume Tochon, Miguel Angel Veganzones, Jocelyn Chanussot, Christian Jutten	ICASSP	2
2017	Object Tracking by Hierarchical Decomposition of Hyperspectral Video Sequences: Application to Chemical Gas Plume Tracking abstract It is now possible to collect hyperspectral video sequences at a near real-time frame rate. The wealth of spectral, spatial, and temporal information of those sequences is appealing for various applications, but classical video processing techniques must be adapted to handle the high dimensionality and huge size of the data to process. In this paper, we introduce a novel method based on the hierarchical analysis of hyperspectral video sequences to perform object tracking. This latter operation is tackled as a sequential object detection process, conducted on the hierarchical representation of the hyperspectral video frames. We apply the proposed methodology to the chemical gas plume tracking scenario and compare its performances with state-of-the-art methods, for two real hyperspectral video sequences, and show that the proposed approach performs at least equally well. Guillaume Tochon, Jocelyn Chanussot, Mauro Dalla Mura, Andrea L. Bertozzi	IEEE Trans. Geosci. Remote. Sens.	1
2016	Hyperspectral Local Intrinsic Dimensionality abstract The intrinsic dimensionality (ID) of multivariate data is a very important concept in spectral unmixing of hyperspectral images. A good estimation of the ID is crucial for a correct retrieval of the number of endmembers (the spectral signatures of macroscopic materials) in the image, for dimensionality reduction or for subspace learning, among others. Recently, some approaches to perform spectral unmixing and superresolution locally have been proposed, which require a local estimation of the number of endmembers to use. However, the role of ID in local regions of hyperspectral images has not been properly addressed. Some important issues when dealing with small regions of hyperspectral data can seriously affect the performance of conventional hyperspectral ID estimators. We show that three factors mainly affect local ID estimation: the number of pixels in the local regions, which has to be high enough for the estimations to be relevant, the number of hyperspectral bands which complicates the estimations if the ambient space has a high dimensionality, and the noise, which can be misinterpreted as a signal when its power is important. Here, we review the hyperspectral ID estimators on the literature for local ID estimation, we show how they behave in a local setting on synthetic and real data sets, and we provide some guidelines to make proper use of these estimators in local approaches. Lucas Drumetz, Miguel Angel Veganzones, Ruben Marrero, Guillaume Tochon, Mauro Dalla Mura, Giorgio Licciardi, Christian Jutten, Jocelyn Chanussot	IEEE Trans. Geosci. Remote. Sens.	4
2014	Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation abstract The binary partition tree (BPT) is a hierarchical region-based representation of an image in a tree structure. The BPT allows users to explore the image at different segmentation scales. Often, the tree is pruned to get a more compact representation and so the remaining nodes conform an optimal partition for some given task. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. Linear spectral unmixing consists of finding the spectral signatures of the materials present in the image (endmembers) and their fractional abundances within each pixel. The proposed methodology exploits the local unmixing of the regions to find the partition achieving a global minimum reconstruction error. Results are presented on real hyperspectral data sets with different contexts and resolutions. Miguel Angel Veganzones, Guillaume Tochon, Mauro Dalla Mura, Antonio Plaza, Jocelyn Chanussot	IEEE Trans. Image Process.	2
2013	Hyperspectral image segmentation using a new spectral mixture-based binary partition tree representation abstract The Binary Partition Tree (BPT) is a hierarchical region-based representation of an image in a tree structure. BPT allows users to explore the image at different segmentation scales, from fine partitions close to the leaves to coarser partitions close to the root. Often, the tree is pruned so the leaves of the resulting pruned tree conform an optimal partition given some optimality criterion. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. The proposed methodology exploits the local unmixing of the regions to find the partition achieving a global minimum reconstruction error. We successfully tested the proposed approach on the well-known Cuprite hyperspectral image collected by NASA Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This scene is considered as a standard benchmark to validate spectral unmixing algorithms. Miguel Angel Veganzones, Guillaume Tochon, Mauro Dalla Mura, Antonio Plaza, Jocelyn Chanussot	ICIP	2
2013	A comparison study between windowing and binary partition trees for hyperspectral image information mining abstract Remote sensors capture large scenes that are conventionally split in smaller patches before being stored and analyzed. Traditionally, this has been done by dividing the scene in rectangular windows. Such windowing methodology could provoke the separation of spectrally homogeneous areas or objects of interest into two or more patches. This is due to the presence of objects of interest in correspondence to windows' borders, or because the fixed size of the windows does not adapt well to the scale of the objects. To alleviate this issue, the windows can be arranged in an overlapping way, incurring in some data redundancy storage. Recently, tree representations have been used as an alternative to windowing in order to structure and store large amounts of remote sensing data. In this work we explore the benefits of using Binary Partition Trees (BPT) instead of windowing to store hyperspectral large scenes. We are particularly interested in storing the information resulting of local spectral unmixing processes running over a large real hyperspectral scene. We show that under similar conditions BPT allows a better storage of the unmixing information in terms of reconstruction error. Miguel Angel Veganzones, Guillaume Tochon, Mauro Dalla Mura, Antonio Plaza, Jocelyn Chanussot	IGARSS	2
2012	Binary partition tree as a hyperspectral segmentation tool for tropical rainforests abstract Individual tree crown delineation in tropical forests is of great interest for ecological applications. In this paper we propose a method for hyperspectral image segmentation based on binary tree partitioning. The initial partition is obtained from a watershed transformation in order to make the method computationally more efficient. Then we use a non-parametric region model based on histograms to characterize the regions and the diffusion distance to define the region merging order. The pruning strategy is based on the discontinuity of size increment observed when iteratively merging the regions. The segmentation quality is assessed visually and appears to perform well on most cases, but tree delineation could be improved by including structural information derived from LiDAR data. Guillaume Tochon, Jean-Baptiste Féret, Roberta E. Martin, Raul Tupayachi, Jocelyn Chanussot, Gregory Asner	IGARSS	1