Test Whats new message.
Scheduled Maintenance on Monday 1/13/2025
Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.
Showing 1-25 of 110 results
- Sort
Filter Results
Show
Sort By
Results
Efficient local correlation volume for unsupervised optical flow estimation on small moving objects in large satellite images
Sarra Khairi;Etienne Meunier;Renaud Fraisse;Patrick Bouthemy
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2024 | Conference Paper |
With the advent of deep learning methods, performance and efficiency of optical flow estimation has significantly increased, especially for supervised models. However, they do not generalize well to more specific data involving small moving objects in large images, such as high-resolution aerial or satellite sequences. In addition, annotation and realistic simulation are difficult for these conten...Show More
Unsupervised Space-Time Network for Temporally-Consistent Segmentation of Multiple Motions
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Motion segmentation is one of the main tasks in computer vision and is relevant for many applications. The optical flow (OF) is the input generally used to segment every frame of a video sequence into regions of coherent motion. Temporal consistency is a key feature of motion segmentation, but it is often neglected. In this paper, we propose an original unsupervised spatiotemporal framework for mo...Show More
EM-Driven Unsupervised Learning for Efficient Motion Segmentation
In this paper, we present a CNN-based fully unsupervised method for motion segmentation from optical flow. We assume that the input optical flow can be represented as a piecewise set of parametric motion models, typically, affine or quadratic motion models. The core idea of our work is to leverage the Expectation-Maximization (EM) framework in order to design in a well-founded manner a loss functi...Show More
Trajectory Saliency Detection Using Consistency-Oriented Latent Codes From a Recurrent Auto-Encoder
In this paper, we are concerned with the detection of progressive dynamic saliency from video sequences. More precisely, we are interested in saliency related to motion and likely to appear progressively over time. It can be relevant to trigger alarms, to dedicate additional processing or to detect specific events. Trajectories represent the best way to support progressive dynamic saliency detecti...Show More
3D Optical Flow Estimation Combining 3D Census Signature and Total Variation Regularization
Sandeep Manandhar;Patrick Bouthemy;Eric Welf;Philippe Roudot;Charles Kervrann
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
Year: 2020 | Conference Paper |
We present a 3D optical flow method for 3D fluorescence image sequences which preserves discontinuities in the computed flow field. We propose to minimize an energy function composed of a linearized 3D Census signature-based data term and a total variational (TV) regularizer. To demonstrate the efficiency of our method, we have applied it to real sequences depicting collagen network, where the mot...Show More
ROAM: A Rich Object Appearance Model with Application to Rotoscoping
Juan-Manuel Pérez-Rúa;Ondrej Miksik;Tomas Crivelli;Patrick Bouthemy;Philip H. S. Torr;Patrick Pérez
Rotoscoping, the detailed delineation of scene elements through a video shot, is a painstaking task of tremendous importance in professional post-production pipelines. While pixel-wise segmentation techniques can help for this task, professional rotoscoping tools rely on parametric curves that offer the artists a much better interactive control on the definition, editing and manipulation of the se...Show More
Unsupervised Motion Saliency Map Estimation Based On Optical Flow Inpainting
2019 IEEE International Conference on Image Processing (ICIP)
Year: 2019 | Conference Paper |
Cited by: Papers (4)
The paper addresses the problem of motion saliency in videos, that is, identifying regions that undergo motion departing from its context. We propose a new unsupervised paradigm to compute motion saliency maps. The key ingredient is the flow inpainting stage. Candidate regions are determined from the optical flow boundaries. The residual flow in these regions is given by the difference between the...Show More
Intensity-based matching and registration for 3D correlative microscopy with large discrepancies
Bertha Mayela Toledo Acosta;Xavier Heiligenstein;Grégoire Malandain;Patrick Bouthemy
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
Correlative microscopy, especially light and electron microscopy (CLEM), enables the study of cells and subcellular elements in complementary ways, provided a reliable registration between images is efficiently achievable. We propose a general automatic registration method. Due to large discrepancies in appearance, field-of-view, resolution and position, a pre-alignment stage is required before an...Show More
A sparse-to-dense method for 3D optical flow estimation in 3D light-microscopy image sequences
Sandeep Manandhar;Patrick Bouthemy;Erik Welf;Philippe Roudot;Charles Kervrann
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
We present a two-stage 3D optical flow estimation method for light microscopy image volumes. The method takes a pair of light microscopy image volumes as input, segments the 2D slices of the source volume in superpixels and sparsely estimates the 3D displacement vectors in the volume pair. A weighted interpolation is then introduced to get a dense 3D flow field. Edges and motion boundaries are con...Show More
Multi-scale spot segmentation with selection of image scales
Bertha Mayela Toledo Acosta;Antoine Basset;Patrick Bouthemy;Charles Kervrann
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2017 | Conference Paper |
Detecting spot-like objects of different sizes in images is needed in many applications. Multiple image scales must then be handled for reliable spot segmentation. We define an original criterion based on the a contrario approach and the LoG scale-space framework to automatically select the meaningful scales. We then design a coarse-to-fine multi-scale spot segmentation scheme involving a locally ...Show More
Determining Occlusions from Space and Time Image Reconstructions
Juan-Manuel Pérez-Rúa;Tomas Crivelli;Patrick Bouthemy;Patrick Pérez
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2016 | Conference Paper |
Cited by: Papers (7)
The problem of localizing occlusions between consecutive frames of a video is important but rarely tackled on its own. In most works, it is tightly interleaved with the computation of accurate optical flows, which leads to a delicate chicken-and-egg problem. With this in mind, we propose a novel approach to occlusion detection where visibility or not of a point in next frame is formulated in terms...Show More
Robust selection of parametric motion models in image sequences
2016 IEEE International Conference on Image Processing (ICIP)
Year: 2016 | Conference Paper |
Cited by: Papers (1)
Parametric motion models are commonly used in image sequence analysis for different tasks. A robust estimation framework is usually required to reliably compute the motion model. The choice of the right model is also important. However, dealing simultaneously with both issues remains an open question. We propose a robust motion model selection method with two variants, which relies on the Fisher t...Show More
Hierarchical motion decomposition for dynamic scene parsing
Juan-Manuel Pérez-Rúa;Tomas Crivelli;Patrick Pérez;Patrick Bouthemy
2016 IEEE International Conference on Image Processing (ICIP)
Year: 2016 | Conference Paper |
A number of applications in video analysis rely on a per-frame motion segmentation of the scene as key preprocessing step. Moreover, different settings in video production require extracting segmentation masks of multiple moving objects and object parts in a hierarchical fashion. In order to tackle this problem, we propose to analyze and exploit the compositional structure of scene motion to provi...Show More
A common image representation and a patch-based search for correlative light-electron-microscopy (CLEM) registration
Bertha Mayela Toledo Acosta;Patrick Bouthemy;Charles Kervrann
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
Year: 2016 | Conference Paper |
Cited by: Papers (3)
Correlative light-electron microscopy (CLEM) enables to relate dynamics (or functions) with structure for a better understanding of cell mechanisms. However, the LM and EM images are of very different size, spatial resolution, field of view, and appearance. Registration of LM and EM modalities is then a timely, important but difficult open problem, which still requires some manual assistance. We h...Show More
Detection and estimation of membrane diffusion during exocytosis in TIRFM image sequences
A. Basset;P. Bouthemy;J. Boulanger;F. Waharte;C. Kervrann;J. Salamero
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
Year: 2015 | Conference Paper |
Cited by: Papers (2)
Assessing the dynamics of plasma membrane diffusion processes in live cell fluorescence microscopy is of paramount interest to understand cell mechanisms. We propose a new method to detect vesicle fusion events, and estimate the associated diffusion coefficients in image sequences of total internal reflection fluorescence microscopy (TIRFM). In contrast to usual approaches, a diffusion coefficient...Show More
Adaptive Spot Detection With Optimal Scale Selection in Fluorescence Microscopy Images
Antoine Basset;Jérôme Boulanger;Jean Salamero;Patrick Bouthemy;Charles Kervrann
Accurately detecting subcellular particles in fluorescence microscopy is of primary interest for further quantitative analysis such as counting, tracking, or classification. Our primary goal is to segment vesicles likely to share nearly the same size in fluorescence microscopy images. Our method termed adaptive thresholding of Laplacian of Gaussian (LoG) images with autoselected scale (ATLAS) auto...Show More
Background Fluorescence Estimation and Vesicle Segmentation in Live Cell Imaging With Conditional Random Fields
Thierry Pécot;Patrick Bouthemy;Jérôme Boulanger;Anatole Chessel;Sabine Bardin;Jean Salamero;Charles Kervrann
Image analysis applied to fluorescence live cell microscopy has become a key tool in molecular biology since it enables to characterize biological processes in space and time at the subcellular level. In fluorescence microscopy imaging, the moving tagged structures of interest, such as vesicles, appear as bright spots over a static or nonstatic background. In this paper, we consider the problem of...Show More
Recovery of motion patterns and dominant paths in videos of crowded scenes
2014 IEEE International Conference on Image Processing (ICIP)
Year: 2014 | Conference Paper |
Cited by: Papers (4)
Assessing crowd behaviors from videos is a difficult task while of interest in many applications. We have defined a novel approach which identifies from two successive frames only, crowd behaviors expressed by simple image motion patterns. It relies on the estimation of a collection of sub-affine motion models in the image, a local motion classification based on a penalized likelihood criterion, a...Show More
Action Localization with Tubelets from Motion
Mihir Jain;Jan Van Gemert;Hervé Jégou;Patrick Bouthemy;Cees G.M. Snoek
2014 IEEE Conference on Computer Vision and Pattern Recognition
Year: 2014 | Conference Paper |
This paper considers the problem of action localization, where the objective is to determine when and where certain actions appear. We introduce a sampling strategy to produce 2D+t sequences of bounding boxes, called tubelets. Compared to state-of-the-art alternatives, this drastically reduces the number of hypotheses that are likely to include the action of interest. Our method is inspired by a r...Show More
Localization and classification of membrane dynamics in TIRF microscopy image sequences
Antoine Basset;Patrick Bouthemy;Jérôme Boulanger;Jean Salamero;Charles Kervrann
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)
Year: 2014 | Conference Paper |
Cited by: Papers (3)
The detection of proteins and the classification of their temporal behaviors in live cell fluorescence microscopy are of utmost importance to understand cell mechanisms. In this paper, we aim at locating and recognizing temporal events in TIRF microscopy image sequences related to membrane dynamics. After segmenting the time-varying vesicles in the image, we exploit space-time information extracte...Show More
Estimation of the flow of particles within a partition of the image domain in fluorescence video-microscopy
Thierry Pécot;Jérôme Boulanger;Charles Kervrann;Patrick Bouthemy;Jean Salamero
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)
Year: 2014 | Conference Paper |
Cited by: Papers (3)
Automatic analysis of the dynamic content in fluorescence video-microscopy is crucial for understanding molecular mechanisms involved in cell functions. In this paper, we propose an original approach for analyzing particle trafficking in these sequences. Instead of individually tracking every particle, we estimate the particle flows between predefined regions. This approach allows us to process im...Show More
SLT-LoG: A vesicle segmentation method with automatic scale selection and local thresholding applied to TIRF microscopy
Antoine Basset;Jérôme Boulanger;Patrick Bouthemy;Charles Kervrann;Jean Salamero
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)
Year: 2014 | Conference Paper |
Cited by: Papers (7)
Accurately detecting cellular structures in fluorescence microscopy is of primary interest for further quantitative analysis such as counting, tracking or classification. We aim at segmenting vesicles in TIRF images. The optimal segmentation scale is automatically selected, relying on a multiscale feature detection stage, and the segmentation consists in thresholding the Laplacian of Gaussian of t...Show More
Frame-by-frame crowd motion classification from affine motion models
2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance
Year: 2013 | Conference Paper |
Cited by: Papers (2)
Recognizing dynamic behaviors of dense crowds in videos is of great interest in many surveillance applications. In contrast to most existing methods which are based on trajectories or tracklets, our approach for crowd motion analysis provides a crowd motion classification on a frame-by-frame and pixel-wise basis. Indeed, we only compute affine motion models from pairs of two consecutive video imag...Show More
Better Exploiting Motion for Better Action Recognition
2013 IEEE Conference on Computer Vision and Pattern Recognition
Year: 2013 | Conference Paper |
Several recent works on action recognition have attested the importance of explicitly integrating motion characteristics in the video description. This paper establishes that adequately decomposing visual motion into dominant and residual motions, both in the extraction of the space-time trajectories and for the computation of descriptors, significantly improves action recognition algorithms. Then...Show More
Aggregation of patch-based estimations for illumination-invariant optical flow in live cell imaging
Denis Fortun;Patrick Bouthemy;Perrine Paul-Gilloteaux;Charles Kervrann
2013 IEEE 10th International Symposium on Biomedical Imaging
Year: 2013 | Conference Paper |
Cited by: Papers (4)
Live cell image sequences provide a large variety of challenging situations for motion estimation. We present a novel optical flow estimation method based on a two-stage aggregation framework and designed to handle this diversity of issues. First, semi-local candidates are estimated with a combination of patch correspondences and illumination-invariant affine motion estimations. Then, one candidat...Show More
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.