Multi-object shape estimation and tracking from silhouette cues

Joint optimal object shape estimation and encoding

10.1117/12.298329 ◽

1998 ◽

Cited By ~ 11

Author(s):

Lisimachos P. Kondi ◽

Fabian W. Meier ◽

Guido M. Schuster ◽

Aggelos K. Katsaggelos

Keyword(s):

Shape Estimation ◽

Object Shape

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Object Shape Estimation Through Touch-Based Continuum Manipulation

Springer Proceedings in Advanced Robotics - Robotics Research ◽

10.1007/978-3-030-28619-4_42 ◽

2019 ◽

pp. 573-588

Author(s):

Huitan Mao ◽

Jing Xiao

Keyword(s):

Shape Estimation ◽

Object Shape

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Joint Optimal Object Shape Estimation and Encoding

IEEE Transactions on Circuits and Systems for Video Technology ◽

10.1109/tcsvt.2004.825569 ◽

2004 ◽

Vol 14 (4) ◽

pp. 528-533 ◽

Cited By ~ 20

Author(s):

L.P. Kondi ◽

G. Melnikov ◽

A.K. Katsaggelos

Keyword(s):

Shape Estimation ◽

Object Shape

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Object shape estimation and modeling, based on sparse Gaussian process implicit surfaces, combining visual data and tactile exploration

Robotics and Autonomous Systems ◽

10.1016/j.robot.2020.103433 ◽

2020 ◽

Vol 126 ◽

pp. 103433

Author(s):

Gabriela Zarzar Gandler ◽

Carl Henrik Ek ◽

Mårten Björkman ◽

Rustam Stolkin ◽

Yasemin Bekiroglu

Keyword(s):

Gaussian Process ◽

Implicit Surfaces ◽

Visual Data ◽

Shape Estimation ◽

Object Shape

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Object shape estimation from tomographic measurements—A performance analysis

Signal Processing ◽

10.1016/0165-1684(89)90063-7 ◽

1989 ◽

Vol 18 (1) ◽

pp. 63-87 ◽

Cited By ~ 8

Author(s):

David J. Rossi ◽

Alan S. Willsky ◽

Daniel M. Spielman

Keyword(s):

Performance Analysis ◽

Shape Estimation ◽

Object Shape ◽

A Performance

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Single-shot Coded Diffraction System for 3D Object Shape Estimation

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.14.coimg-059 ◽

2020 ◽

Vol 2020 (14) ◽

pp. 59-1-59-7

Author(s):

Samuel Pinilla ◽

Laura Galvis ◽

Karen Egiazarian ◽

Henry Arguello

Keyword(s):

Precision Agriculture ◽

Single Shot ◽

Full Time ◽

Shape Estimation ◽

Object Shape ◽

3D Object ◽

Optical Phase ◽

3D Shape ◽

3D Surface ◽

3D Shape Reconstruction

The three-dimensional (3D) shape reconstruction problem of an object is a task of high interest in autonomous vehicles, detection of moving objects, and precision agriculture. A common methodology to recover the 3D shape of an object is using its optical phase. However, this approach involves solving a non-convex computationally demanding inverse problem known as phase retrieval (PR) in a setup that records coded diffraction patterns (CDP). Usually, the acquisition of several snapshots from the scene is required to solve the PR problem. This work proposes a single-shot 3D shape estimation technique using the optical phase of the object from CDP. The presented approach consists on accurately estimating the optical phase of the object by low-passfiltering the leading eigenvector of a carefully constructed matrix. Then, the estimated phase is used to infer the 3D object shape. It is important to mention that the estimation procedure does not involve a full time demanding reconstruction of the objects. Numerical results on synthetic data demonstrate that the proposed methodology closely estimates the 3D surface of an object with a normalized Mean-Square-Error of up to 0.27, under both noiseless and noisy scenarios. Additionally, the proposed method requires up to 60% less measurements to accurately estimate the 3D surface compared to a state-of-the-art methodology.

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