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EMMCVPR
1999
Springer
15 years 4 months ago
Maximum Likelihood Inference of 3D Structure from Image Sequences
The paper presents a new approach to recovering the 3D rigid shape of rigid objects from a 2D image sequence. The method has two distinguishing features: it exploits the rigidity o...
Pedro M. Q. Aguiar, José M. F. Moura
SIAMIS
2010
395views more  SIAMIS 2010»
14 years 10 months ago
A Geometric Approach to Joint 2D Region-Based Segmentation and 3D Pose Estimation Using a 3D Shape Prior
Abstract. In this work, we present an approach to jointly segment a rigid object in a two-dimensional (2D) image and estimate its three-dimensional (3D) pose, using the knowledge o...
Samuel Dambreville, Romeil Sandhu, Anthony J. Yezz...
114
Voted
ICCV
2003
IEEE
16 years 1 months ago
Inferring 3D Structure with a Statistical Image-Based Shape Model
We present an image-based approach to infer 3D structure parameters using a probabilistic "shape+structure" model. The 3D shape of an object class is represented by sets...
Kristen Grauman, Gregory Shakhnarovich, Trevor Dar...
ECCV
2006
Springer
16 years 1 months ago
Real-Time Non-rigid Shape Recovery Via Active Appearance Models for Augmented Reality
One main challenge in Augmented Reality (AR) applications is to keep track of video objects with their movement, orientation, size, and position accurately. This poses a challengin...
Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
PCM
2004
Springer
127views Multimedia» more  PCM 2004»
15 years 5 months ago
Using a Non-prior Training Active Feature Model
This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPTAFM) framework. The proposed algorithm mainly focus...
Sangjin Kim, Jinyoung Kang, Jeongho Shin, Seongwon...