Výsledky vyhledávání - "Epameinondas Antonakos"

A Comprehensive Performance Evaluation of Deformable Face Tracking 'In-the-Wild'

Autor: Epameinondas Antonakos, Grigorios Chrysos, Patrick Snape, Stefanos Zafeiriou, Akshay Asthana

Publikováno v: International Journal of Computer Vision

Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94490b0d89ead9a1e2505bc700954e7a
https://doi.org/10.1007/s11263-017-0999-5

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The Menpo project

Autor: Patrick Snape, James Booth, Joan Alabort-i-Medina, Stefanos Zafeiriou, Epameinondas Antonakos

Publikováno v: ACM SIGMultimedia Records. 8:4-9

The Menpo Project [1] is a BSD-licensed set of tools and software designed to provide an end-to-end pipeline for collection and annotation of image and 3D mesh data. In particular, the Menpo Project provides tools for annotating images and meshes wit

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https://doi.org/10.1145/2982857.2982858

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IPST: Incremental Pictorial Structures for Model-free Tracking of Deformable Objects

Autor: Epameinondas Antonakos, Grigorios Chrysos, Stefanos Zafeiriou

Publikováno v: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 27(7)

Model-free tracking is a well-studied task in computer vision. Typically, a rectangular bounding box containing a single object is provided in the first (few) frame(s) and then the method tracks the object in the rest frames. However, for deformable

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4289490834bc175a51ff19357592b0c9
https://pubmed.ncbi.nlm.nih.gov/29993804

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3D reconstruction of 'in-the-wild' faces in images and videos

Autor: Stylianos Ploumpis, Epameinondas Antonakos, Anastasios Roussos, Yannis Panagakis, James Booth, Stefanos Zafeiriou, Evangelos Ververas

3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and are among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::25de50bd9bc5c6099071799a5d3923d5
http://hdl.handle.net/10044/1/83711

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Complex representations for learning statistical shape priors

Autor: Epameinondas Antonakos, Athanasios Papaioannou, Stefanos Zafeiriou

Publikováno v: EUSIPCO

Parametrisation of the shape of deformable objects is of paramount importance in many computer vision applications. Many state-of-the-art statistical deformable models perform landmark localisation via optimising an objective function over a certain

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f0716022ecc4d286107f30c6d5c2652
https://doi.org/10.23919/eusipco.2017.8081394

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DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild

Autor: Stefanos Zafeiriou, Iasonas Kokkinos, Epameinondas Antonakos, Patrick Snape, Riza Alp Guler, George Trigeorgis

Publikováno v: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, CVF, Jul 2017, Honolulu, United States. pp.6799-6808
CVPR
2017 IEEE International Conference on Computer Vision and Pattern Recognition

In this paper we propose to learn a mapping from image pixels into a dense template grid through a fully convolutional network. We formulate this task as a regression problem and train our network by leveraging upon manually annotated facial landmark

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eccfe4f1d7a72f841f1f2da645c5f683
https://hal.science/hal-01637896

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'''3D Face Morphable Models ''''In-the-Wild'''''''

Autor: Stefanos Zafeiriou, Yannis Panagakis, James Booth, George Trigeorgis, Stylianos Ploumpis, Epameinondas Antonakos

Publikováno v: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
IEEE International Conference on Computer Vision and Pattern Recognition
CVPR

3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have bee

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a207c9a8cb03c3955ee95f3dbff7a059

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Adaptive Cascaded Regression

Autor: Stefanos Zafeiriou, Epameinondas Antonakos, Patrick Snape, George Trigeorgis

Publikováno v: 2016 IEEE International Conference on Image Processing (ICIP)
ICIP

The two predominant families of deformable models for the task of face alignment are: (i) discriminative cascaded regression models, and (ii) generative models optimised with Gauss-Newton. Although these approaches have been found to work well in pra

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4de28db8cb36aac5c9996d8d0ab64c36
http://hdl.handle.net/10044/1/32900

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Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment

Autor: Stefanos Zafeiriou, Patrick Snape, Mihalis A. Nicolaou, Epameinondas Antonakos, George Trigeorgis

Publikováno v: International Conference on Computer Vision and Pattern Recognition
CVPR
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Proceedings of IEEE International Conference on Computer Vision & Pattern Recognition (CVPR'16)

Cascaded regression has recently become the method of choice for solving non-linear least squares problems such as deformable image alignment. Given a sizeable training set, cascaded regression learns a set of generic rules that are sequentially appl

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http://hdl.handle.net/10044/1/32344

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300 faces In-the-wild challenge: database and results

Autor: Georgios Tzimiropoulos, Maja Pantic, Epameinondas Antonakos, Christos Sagonas, Stefanos Zafeiriou

Publikováno v: Image and Vision Computing
Image and vision computing, 47, 3-18. Elsevier

Computer Vision has recently witnessed great research advance towards automatic facial points detection. Numerous methodologies have been proposed during the last few years that achieve accurate and efficient performance. However, fair comparison bet

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::285b403da88f85c998300981700098de
http://hdl.handle.net/10044/1/32322

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