Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Rigas Kouskouridas"'
Publikováno v:
Multimedia Tools and Applications. 77:9211-9231
This paper presents a comparison framework for algorithms that can diminish the effects of illumination in images. Its main objective is to reveal the positive and negative characteristics of such algorithms, allowing researchers to select the most a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e9e299fe5a78dd2972f2ca0da5581a53
https://doi.org/10.1007/s11042-017-4783-x
https://doi.org/10.1007/s11042-017-4783-x
Autor:
Tae-Kyun Kim, Tomas Hodan, Ales Leonardis, Bertram Drost, Rigas Kouskouridas, Carsten Rother, Vincent Lepetit, Carsten Steger, Federico Tombari, Frank Michel, Krzysztof Walas, Caner Sahin, Jiri Matas, Thibault Groueix, Kostas E. Bekris
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030110086
ECCV Workshops (1)
ECCV Workshops (1)
This document summarizes the 4th International Workshop on Recovering 6D Object Pose which was organized in conjunction with ECCV 2018 in Munich. The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::941a0076f5e94baba6d1cbaca31098a6
https://doi.org/10.1007/978-3-030-11009-3_36
https://doi.org/10.1007/978-3-030-11009-3_36
Publikováno v:
Expert Systems with Applications. 42:8123-8133
A novel method for object grasping.Unified grasping system capable of answering queries such as What, Where and How."What" is handled by a state-of-the-art object recognition framework."Where" is answered by a manifold modeling-based 6 DoF pose estim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e88dbd58be7ad9d73659d15855a9f01
https://doi.org/10.1016/j.eswa.2015.06.039
https://doi.org/10.1016/j.eswa.2015.06.039
Autor:
Caner Sahin, Vassileios Balntas, Juil Sock, Tae-Kyun Kim, Rigas Kouskouridas, Andreas Doumanoglou
Publikováno v:
ICCV
In this paper we examine the effects of using object poses as guidance to learning robust features for 3D object pose estimation. Previous works have focused on learning feature embeddings based on metric learning with triplet comparisons and rely on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4e50cea8cbae3af458ac07cc6db16835
https://doi.org/10.1109/iccv.2017.416
https://doi.org/10.1109/iccv.2017.416
Publikováno v:
Autonomous Robots. 37:191-207
The efficient manipulation of randomly placed objects relies on the accurate estimation of their 6 DoF geometrical configuration. In this paper we tackle this issue by following the intuitive idea that different objects, viewed from the same perspect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::25b64bcf8e6bf315d13a94060f3a2cc9
https://doi.org/10.1007/s10514-014-9388-x
https://doi.org/10.1007/s10514-014-9388-x
Publikováno v:
Neurocomputing. 120:90-100
This paper introduces an efficient representation and feature extraction technique for 3D pose estimation of objects, incorporating a novel mechanism for the exploitation of the extracted visual cues. A combination of a fuzzy clustering technique for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::faf5fafc90c0e5dd84140ae1c28e1a5f
https://doi.org/10.1016/j.neucom.2012.11.047
https://doi.org/10.1016/j.neucom.2012.11.047
Publikováno v:
IROS
State-of-the-art techniques proposed for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space. To reduce this dependency, we introduce a novel architecture called Iterative Hough Forest with Histogr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f41ce2ded1f08c72fccd60f81f4f84c
https://doi.org/10.1109/iros.2016.7759605
https://doi.org/10.1109/iros.2016.7759605
Publikováno v:
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR
CVPR
Object detection and 6D pose estimation in the crowd (scenes with multiple object instances, severe foreground occlusions and background distractors), has become an important problem in many rapidly evolving technological areas such as robotics and a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5569b743d9760be55861b5452802810e
Publikováno v:
ACPR
With the number of videos growing rapidly in modern society, automatically recognizing objects from video input becomes increasingly pressing. Videos contain abundant yet noisy information, with easily obtained video-level labels. This paper targets
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::064582cb8da46a13ef630816f6616e77
https://doi.org/10.1109/acpr.2015.7486463
https://doi.org/10.1109/acpr.2015.7486463
Publikováno v:
Computer Vision – ECCV 2014 ISBN: 9783319105987
ECCV (6)
ECCV (6)
In this paper we propose a novel framework, Latent-Class Hough Forests, for 3D object detection and pose estimation in heavily cluttered and occluded scenes. Firstly, we adapt the state-of-the-art template matching feature, LINEMOD [14], into a scale
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::42ac30e0983499d1de076e14eb29655e
https://doi.org/10.1007/978-3-319-10599-4_30
https://doi.org/10.1007/978-3-319-10599-4_30