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S. Gausepohl - IEEE Xplore Author Profile

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Author details

S. Gausepohl

Also published under: Steven Gausepohl, S. C. Gausepohl, Steve C. Gausepohl

Publications

17

Citations

203

Publications by Year

19992015

Co-Authors:

Kerem AkarvardarWilliam AllenMichael L. AllesK. W. AngRock Hyun Baek

Show All Co-Authors (84)

S. Gausepohl

Also published under: Steven Gausepohl, S. C. Gausepohl, Steve C. Gausepohl

Affiliation

College of Nanoscale Science and Engineering

SUNY Polytechnic Institute

Albany, NY, USA

Publication Topics

Contact Resistance,
Gate Stack,
Resistive Random Access Memory,
Schottky Barrier,
Schottky Barrier Height,
Silicide,
Silicon-on-insulator,
Specific Resistance,
Accumulation Mode,
Carrier Mobility,
Dipole Formation,
Final Width

Biography

Steven Gausepohl was born in New Jersey, in 1970. He received the B.A. degree in physics (cum laude) from Drew University, Madison, NJ, in 1992, and the M.A. and Ph.D. degrees in physics with emphasis on superconducting devices from the University of Virginia, Charlottesville, in 1995 and 1997, respectively.,He joined MiCRUS Semiconductors in 1997, which was later acquired by Philips Semiconductors, Fishkill, NY, in 2000. He is currently a Senior Process Engineer with responsibilities in managing the transfer of new technology into the Philips manufacturing site.(Based on document published on 30 May 2006).

Publications

17

Citations

203

Publications by Year

19992015

Co-Authors:

Kerem Akarvardar
William Allen
Michael L. Alles
K. W. Ang
Rock Hyun Baek

Show All Co-Authors (84)

Author's Published Works

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Showing 1-25 of 236 results

Conferences (176)

Journals (60)

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Pascal Fua(180)
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Computer Vision Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland(14)
CVLab, EPFL, Lausanne, Switzerland(12)
Computer Vision Laboratory, EPFL, Lausanne, Switzerland(10)
EPFL, Lausanne, Switzerland(8)
CVLab, EPFL(8)
IEEE Transactions on Pattern Analysis and Machine Intelligence(46)
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(8)
2009 IEEE Conference on Computer Vision and Pattern Recognition(7)
IEEE Transactions on Medical Imaging(7)
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(6)
IEEE(236)
Media(18)
Salt Lake City, UT, USA(8)
Seattle, WA, USA(8)
Las Vegas, NV, USA(7)
Miami, FL, USA(7)
San Francisco, CA, USA(7)
Pose Estimation(44)
3D Point(35)
Input Image(35)
Training Set(35)
Objective Function(31)

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CLOAF: CoLlisiOn-Aware Human Flow

Andrey Davydov;Martin Engilberge;Mathieu Salzmann;Pascal Fua

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

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Even the best current algorithms for estimating body 3D shape and pose yield results that include body self- intersections. In this paper, we present CLOAF, which exploits the diffeomorphic nature of Ordinary Differential Equations to eliminate such self-intersections while still im- posing body shape constraints. We show that, unlike earlier approaches to addressing this issue, ours completely el...Show More

CLOAF: CoLlisiOn-Aware Human Flow

Andrey Davydov;Martin Engilberge;Mathieu Salzmann;Pascal Fua

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Garment Recovery with Shape and Deformation Priors

Ren Li;Corentin Dumery;Benoît Guillard;Pascal Fua

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

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While modeling people wearing tight-fitting clothing has made great strides in recent years, loose-fitting clothing remains a challenge. We propose a method that delivers realistic garment models from real-world images, regardless of garment shape or deformation. To this end, we introduce a fitting approach that utilizes shape and deformation priors learned from synthetic data to accurately captur...Show More

Garment Recovery with Shape and Deformation Priors

Ren Li;Corentin Dumery;Benoît Guillard;Pascal Fua

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Occlusion Resilient 3D Human Pose Estimation

Soumava Kumar Roy;Ilia Badanin;Sina Honari;Pascal Fua

2024 International Conference on 3D Vision (3DV)

Year: 2024 | Conference Paper |

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Occlusions remain one of the key challenges in 3D body pose estimation from single-camera video sequences. Temporal consistency has been extensively used to mitigate their impact but the existing algorithms in the literature do not explicitly model them.Here, we apply this by representing the deforming body as a spatio-temporal graph. We then introduce a refinement network that performs graph conv...Show More

Occlusion Resilient 3D Human Pose Estimation

Soumava Kumar Roy;Ilia Badanin;Sina Honari;Pascal Fua

2024 International Conference on 3D Vision (3DV)

Year: 2024 | Conference Paper |

DeepMesh: Differentiable Iso-Surface Extraction

Benoît Guillard;Edoardo Remelli;Artem Lukoianov;Pierre Yvernay;Stephan R. Richter;Timur Bagautdinov;Pierre Baque;Pascal Fua

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 11 | Journal Article |

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Geometric Deep Learning has recently made striking progress with the advent of continuous deep implicit fields. They allow for detailed modeling of watertight surfaces of arbitrary topology while not relying on a 3D euclidean grid, resulting in a learnable parameterization that is unlimited in resolution. Unfortunately, these methods are often unsuitable for applications that require an explicit m...Show More

DeepMesh: Differentiable Iso-Surface Extraction

Benoît Guillard;Edoardo Remelli;Artem Lukoianov;Pierre Yvernay;Stephan R. Richter;Timur Bagautdinov;Pierre Baque;Pascal Fua

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 11 | Journal Article |

A Closed-Form, Pairwise Solution to Local Non-Rigid Structure-From-Motion

Shaifali Parashar;Yuxuan Long;Mathieu Salzmann;Pascal Fua

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 11 | Journal Article |

Cited by: Papers (1)

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A recent trend in Non-Rigid Structure-from-Motion (NRSfM) is to express local, differential constraints between pairs of images, from which the surface normal at any point can be obtained by solving a system of polynomial equations. While this approach is more successful than its counterparts relying on global constraints, the resulting methods face two main problems: First, most of the equation s...Show More

A Closed-Form, Pairwise Solution to Local Non-Rigid Structure-From-Motion

Shaifali Parashar;Yuxuan Long;Mathieu Salzmann;Pascal Fua

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 11 | Journal Article |

Detecting Road Obstacles by Erasing Them

Krzysztof Lis;Sina Honari;Pascal Fua;Mathieu Salzmann

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 4 | Journal Article |

Cited by: Papers (11)

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Vehicles can encounter a myriad of obstacles on the road, and it is impossible to record them all beforehand to train a detector. Instead, we select image patches and inpaint them with the surrounding road texture, which tends to remove obstacles from those patches. We then use a network trained to recognize discrepancies between the original patch and the inpainted one, which signals an erased ob...Show More

Detecting Road Obstacles by Erasing Them

Krzysztof Lis;Sina Honari;Pascal Fua;Mathieu Salzmann

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 4 | Journal Article |

GECCO: Geometrically-Conditioned Point Diffusion Models

Michał J. Tyszkiewicz;Pascal Fua;Eduard Trulls

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (6)

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Diffusion models generating images conditionally on text, such as Dall-E 2 [51] and Stable Diffusion [53], have recently made a splash far beyond the computer vision community. Here, we tackle the related problem of generating point clouds, both unconditionally, and conditionally with images. For the latter, we introduce a novel geometrically-motivated conditioning scheme based on projecting spars...Show More

GECCO: Geometrically-Conditioned Point Diffusion Models

Michał J. Tyszkiewicz;Pascal Fua;Eduard Trulls

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

DrapeNet: Garment Generation and Self-Supervised Draping

Luca De Luigi;Ren Li;Benoît Guillard;Mathieu Salzmann;Pascal Fua

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (12)

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Recent approaches to drape garments quickly over arbitrary human bodies leverage self-supervision to eliminate the need for large training sets. However, they are designed to train one network per clothing item, which severely limits their generalization abilities. In our work, we rely on self-supervision to train a single network to drape multiple garments. This is achieved by predicting a 3D def...Show More

DrapeNet: Garment Generation and Self-Supervised Draping

Luca De Luigi;Ren Li;Benoît Guillard;Mathieu Salzmann;Pascal Fua

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Persistent Homology With Improved Locality Information for More Effective Delineation

Doruk Oner;Adélie Garin;Mateusz Koziński;Kathryn Hess;Pascal Fua

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 8 | Journal Article |

Cited by: Papers (3)

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Persistent Homology (PH) has been successfully used to train networks to detect curvilinear structures and to improve the topological quality of their results. However, existing methods are very global and ignore the location of topological features. In this paper, we remedy this by introducing a new filtration function that fuses two earlier approaches: thresholding-based filtration, previously u...Show More

Persistent Homology With Improved Locality Information for More Effective Delineation

Doruk Oner;Adélie Garin;Mateusz Koziński;Kathryn Hess;Pascal Fua

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 8 | Journal Article |

Perspective Aware Road Obstacle Detection

Krzysztof Lis;Sina Honari;Pascal Fua;Mathieu Salzmann

IEEE Robotics and Automation Letters

Year: 2023 | Volume: 8, Issue: 4 | Journal Article |

Cited by: Papers (4)

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While road obstacle detection techniques have become increasingly effective, they typically ignore the fact that, in practice, the apparent size of the obstacles decreases as their distance to the vehicle increases. In this letter, we account for this by computing a scale map encoding the apparent size of a hypothetical object at every image location. We then leverage this perspective map to (i) g...Show More

Perspective Aware Road Obstacle Detection

Krzysztof Lis;Sina Honari;Pascal Fua;Mathieu Salzmann

IEEE Robotics and Automation Letters

Year: 2023 | Volume: 8, Issue: 4 | Journal Article |

Multi-view Tracking Using Weakly Supervised Human Motion Prediction

Martin Engilberge;Weizhe Liu;Pascal Fua

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2023 | Conference Paper |

Cited by: Papers (6)

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Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections. In this paper, we argue that an even more effective approach is to predict people motion over time and infer people’s presence in individual f...Show More

Multi-view Tracking Using Weakly Supervised Human Motion Prediction

Martin Engilberge;Weizhe Liu;Pascal Fua

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2023 | Conference Paper |

Two-level Data Augmentation for Calibrated Multi-view Detection

Martin Engilberge;Haixin Shi;Zhiye Wang;Pascal Fua

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2023 | Conference Paper |

Cited by: Papers (8)

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Data augmentation has proven its usefulness to improve model generalization and performance. While it is commonly applied in computer vision application when it comes to multi-view systems, it is rarely used. Indeed geometric data augmentation can break the alignment among views. This is problematic since multi-view data tend to be scarce and it is expensive to annotate.In this work we propose to ...Show More

Two-level Data Augmentation for Calibrated Multi-view Detection

Martin Engilberge;Haixin Shi;Zhiye Wang;Pascal Fua

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2023 | Conference Paper |

Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation

Sina Honari;Victor Constantin;Helge Rhodin;Mathieu Salzmann;Pascal Fua

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 5 | Journal Article |

Cited by: Papers (9)

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In this article we propose an unsupervised feature extraction method to capture temporal information on monocular videos, where we detect and encode subject of interest in each frame and leverage contrastive self-supervised (CSS) learning to extract rich latent vectors. Instead of simply treating the latent features of nearby frames as positive pairs and those of temporally-distant ones as negativ...Show More

Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation

Sina Honari;Victor Constantin;Helge Rhodin;Mathieu Salzmann;Pascal Fua

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 5 | Journal Article |

On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation

Soumava Kumar Roy;Leonardo Citraro;Sina Honari;Pascal Fua

2022 International Conference on 3D Vision (3DV)

Year: 2022 | Conference Paper |

Cited by: Papers (4)

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Supervised approaches to 3D pose estimation from single images are remarkably effective when labeled data is abundant. However, as the acquisition of ground-truth 3D labels is labor intensive and time consuming, recent attention has shifted towards semi- and weakly-supervised learning. Generating an effective form of supervision with little annotations still poses major challenge in crowded scenes...Show More

On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation

Soumava Kumar Roy;Leonardo Citraro;Sina Honari;Pascal Fua

2022 International Conference on 3D Vision (3DV)

Year: 2022 | Conference Paper |

Long Term Motion Prediction Using Keyposes

Sena Kiciroglu;Wei Wang;Mathieu Salzmann;Pascal Fua

2022 International Conference on 3D Vision (3DV)

Year: 2022 | Conference Paper |

Cited by: Papers (3)

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Long term human motion prediction is essential in safety-critical applications such as human-robot interaction and autonomous driving. In this paper we show that to achieve long term forecasting, predicting human pose at every time instant is unnecessary. Instead, it is more effective to predict a few keyposes and approximate intermediate ones by interpolating the keyposes. We demonstrate that our...Show More

Long Term Motion Prediction Using Keyposes

Sena Kiciroglu;Wei Wang;Mathieu Salzmann;Pascal Fua

2022 International Conference on 3D Vision (3DV)

Year: 2022 | Conference Paper |

HybridSDF: Combining Deep Implicit Shapes and Geometric Primitives for 3D Shape Representation and Manipulation

Subeesh Vasu;Nicolas Talabot;Artem Lukoianov;Pierre Baqué;Jonathan Donier;Pascal Fua

2022 International Conference on 3D Vision (3DV)

Year: 2022 | Conference Paper |

Cited by: Papers (6)

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Deep implicit surfaces excel at modeling generic shapes but do not always capture the regularities present in manufactured objects, which is something simple geometric primitives are particularly good at. In this paper, we propose a representation combining latent and explicit parameters that can be decoded into a set of deep implicit and geometric shapes that are consistent with each other. As a ...Show More

HybridSDF: Combining Deep Implicit Shapes and Geometric Primitives for 3D Shape Representation and Manipulation

Subeesh Vasu;Nicolas Talabot;Artem Lukoianov;Pierre Baqué;Jonathan Donier;Pascal Fua

2022 International Conference on 3D Vision (3DV)

Year: 2022 | Conference Paper |

Learning to Simulate Realistic LiDARs

Benoît Guillard;Sai Vemprala;Jayesh K. Gupta;Ondrej Miksik;Vibhav Vineet;Pascal Fua;Ashish Kapoor

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2022 | Conference Paper |

Cited by: Papers (9)

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Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling. To alleviate this, we introduce a pipeline for data-driven simulation of a realistic LiDAR sensor. We propose a model that learns a mapping between RGB images and corresponding LiDAR features such as raydrop or pe...Show More

Learning to Simulate Realistic LiDARs

Benoît Guillard;Sai Vemprala;Jayesh K. Gupta;Ondrej Miksik;Vibhav Vineet;Pascal Fua;Ashish Kapoor

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2022 | Conference Paper |

ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging

Jiancheng Yang;Udaranga Wickramasinghe;Bingbing Ni;Pascal Fua

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Cited by: Papers (16)

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Deep implicit shape models have become popular in the computer vision community at large but less so for biomed-ical applications. This is in part because large training databases do not exist and in part because biomedical an-notations are often noisy. In this paper, we show that by introducing templates within the deep learning pipeline we can overcome these problems. The proposed framework, nam...Show More

ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging

Jiancheng Yang;Udaranga Wickramasinghe;Bingbing Ni;Pascal Fua

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Learning to Align Sequential Actions in the Wild

Weizhe Liu;Bugra Tekin;Huseyin Coskun;Vibhav Vineet;Pascal Fua;Marc Pollefeys

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Cited by: Papers (11)

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State-of-the-art methods for self-supervised sequential action alignment rely on deep networks that find correspondences across videos in time. They either learn frame-to-frame mapping across sequences, which does not leverage temporal information, or assume monotonic alignment between each video pair, which ignores variations in the order of actions. As such, these methods are not able to deal wi...Show More

Learning to Align Sequential Actions in the Wild

Weizhe Liu;Bugra Tekin;Huseyin Coskun;Vibhav Vineet;Pascal Fua;Marc Pollefeys

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Adversarial Parametric Pose Prior

Andrey Davydov;Anastasia Remizova;Victor Constantin;Sina Honari;Mathieu Salzmann;Pascal Fua

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Cited by: Papers (23)

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The Skinned Multi-Person Linear (SMPL) model represents human bodies by mapping pose and shape parameters to body meshes. However, not all pose and shape parameter values yield physically-plausible or even realistic body meshes. In other words, SMPL is under-constrained and may yield invalid results. We propose learning a prior that restricts the SMPL parameters to values that produce realistic po...Show More

Adversarial Parametric Pose Prior

Andrey Davydov;Anastasia Remizova;Victor Constantin;Sina Honari;Mathieu Salzmann;Pascal Fua

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Leveraging Self-Supervision for Cross-Domain Crowd Counting

Weizhe Liu;Nikita Durasov;Pascal Fua

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Cited by: Papers (20)

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State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, these data-driven approaches rely on large amount of data annotation to achieve good performance, which stops these models from being deployed in emergencies during which data annotation is either too costly or cannot be obtained fast enough. One popular solution is to u...Show More

Leveraging Self-Supervision for Cross-Domain Crowd Counting

Weizhe Liu;Nikita Durasov;Pascal Fua

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate

Doruk Oner;Mateusz Koziński;Lenoardo Citraro;Pascal Fua

IEEE Transactions on Medical Imaging

Year: 2022 | Volume: 41, Issue: 12 | Journal Article |

Cited by: Papers (1)

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Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks. Yet in practice, people, no matter how conscientious, have trouble precisely delineating in 3D and on a large scale, in part because the data is often hard to interpret visually and in part because the 3D interfaces are awkward to use. In this paper, we introduce a method that explicitl...Show More

Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate

Doruk Oner;Mateusz Koziński;Lenoardo Citraro;Pascal Fua

IEEE Transactions on Medical Imaging

Year: 2022 | Volume: 41, Issue: 12 | Journal Article |

Self-Supervised Human Detection and Segmentation via Background Inpainting

Isinsu Katircioglu;Helge Rhodin;Victor Constantin;Jörg Spörri;Mathieu Salzmann;Pascal Fua

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 12 | Journal Article |

Cited by: Papers (5)

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While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this when annotating data is prohibitively expensive, we introduce a self-supervised detection and segmentation approach that can work with single images captured by a potentially moving c...Show More

Self-Supervised Human Detection and Segmentation via Background Inpainting

Isinsu Katircioglu;Helge Rhodin;Victor Constantin;Jörg Spörri;Mathieu Salzmann;Pascal Fua

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 12 | Journal Article |

Counting People by Estimating People Flows

Weizhe Liu;Mathieu Salzmann;Pascal Fua

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 11 | Journal Article |

Cited by: Papers (19)

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Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness constraints across consecutive frames. In this paper, we advocate estimating people flows across image locations between consecutive images and in...Show More

Counting People by Estimating People Flows

Weizhe Liu;Mathieu Salzmann;Pascal Fua

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 11 | Journal Article |

Promoting Connectivity of Network-Like Structures by Enforcing Region Separation

Doruk Oner;Mateusz Koziński;Leonardo Citraro;Nathan C. Dadap;Alexandra G. Konings;Pascal Fua

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 9 | Journal Article |

Cited by: Papers (7)

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We propose a novel, connectivity-oriented loss function for training deep convolutional networks to reconstruct network-like structures, like roads and irrigation canals, from aerial images. The main idea behind our loss is to express the connectivity of roads, or canals, in terms of disconnections that they create between background regions of the image. In simple terms, a gap in the predicted ro...Show More

Promoting Connectivity of Network-Like Structures by Enforcing Region Separation

Doruk Oner;Mateusz Koziński;Leonardo Citraro;Nathan C. Dadap;Alexandra G. Konings;Pascal Fua

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 9 | Journal Article |

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