EFFECT OF NOISE AND SOURCE ILLUMINATION ON 3D SHAPE RECOVERY

2008 ◽  
Vol 22 (05) ◽  
pp. 945-958 ◽  
Author(s):  
AAMIR SAEED MALIK ◽  
TAE-SUN CHOI
Keyword(s):  
Shape Recovery ◽  
Noise Analysis ◽  
Depth Estimation ◽  
Optical Methods ◽  
Optical Transfer Function ◽  
Factors Affecting ◽  
3D Shape ◽  
Focus Measure ◽  
Source Illumination ◽  
3D Shape Recovery

There are many factors affecting the depth estimation for 3D shape recovery using passive optical methods. In this paper, we consider the effects of noise, source illumination and texture reflectance for shape from focus technique. We present a focus measure which shows consistent performance for varying noise levels, source illumination levels and different texture reflectance. The focus measure is based on an optical transfer function implemented in the Fourier domain and its results are compared with four other focus measures. The additive Gaussian noise is considered for noise analysis. Three illumination levels are considered for source illumination and three different textures are studied for reflectance analysis.

Combining Focus Measures for Three Dimensional Shape Estimation Using Genetic Programming

2011 ◽  
pp. 209-228
Author(s):  
Muhammad Tariq Mahmood ◽  
Tae-Sun Choi
Keyword(s):  
Genetic Programming ◽  
Fundamental Problem ◽  
Three Dimensional ◽  
Depth Map ◽  
Depth Estimation ◽  
Shape Reconstruction ◽  
Optical Methods ◽  
3D Shape ◽  
Focus Measure ◽  
Single Focus

Three-dimensional (3D) shape reconstruction is a fundamental problem in machine vision applications. Shape from focus (SFF) is one of the passive optical methods for 3D shape recovery, which uses degree of focus as a cue to estimate 3D shape. In this approach, usually a single focus measure operator is applied to measure the focus quality of each pixel in image sequence. However, the applicability of a single focus measure is limited to estimate accurately the depth map for diverse type of real objects. To address this problem, we introduce the development of optimal composite depth (OCD) function through genetic programming (GP) for accurate depth estimation. The OCD function is developed through optimally combining the primary information extracted using one (homogeneous features) or more focus measures (heterogeneous features). The genetically developed composite function is then used to compute the optimal depth map of objects. The performance of this function is investigated using both synthetic and real world image sequences. Experimental results demonstrate that the proposed estimator is more accurate than existing SFF methods. Further, it is found that heterogeneous function is more effective than homogeneous function.

A new focus measure operator for enhancing image focus in 3D shape recovery

2021 ◽  
Author(s):  
Hoon‐Seok Jang ◽  
Guhnoo Yun ◽  
Husna Mutahira ◽  
Mannan Saeed Muhammad
Keyword(s):  
Shape Recovery ◽  
3D Shape ◽  
Focus Measure ◽  
3D Shape Recovery ◽  
Image Focus

scholarly journals Automatic building detection and 3D shape recovery from single monocular electro-optic imagery

2007 ◽  
Author(s):  
Daniel A. Lavigne ◽  
Parvaneh Saeedi ◽  
Andrew Dlugan ◽  
Norman Goldstein ◽  
Harold Zwick
Keyword(s):  
Shape Recovery ◽  
Building Detection ◽  
3D Shape ◽  
Electro Optic ◽  
3D Shape Recovery

scholarly journals Accurate 3D shape recovery of live cattle with three depth cameras

2019 ◽  
Vol 341 ◽  
pp. 012147 ◽  
Author(s):  
A N Ruchay ◽  
K A Dorofeev ◽  
V V Kalschikov ◽  
V I Kolpakov ◽  
K M Dzhulamanov
Keyword(s):  
Shape Recovery ◽  
Depth Cameras ◽  
3D Shape ◽  
3D Shape Recovery

A heat initiated 3D shape recovery and biodegradable thermoplastic tolerating a strain of 5

2020 ◽  
Vol 154 ◽  
pp. 104680 ◽  
Author(s):  
Ying Shi ◽  
Cui Zheng ◽  
Guixiang Zhu ◽  
Yi Ren ◽  
Li-Zhi Liu ◽  
...  
Keyword(s):  
Shape Recovery ◽  
3D Shape ◽  
3D Shape Recovery

scholarly journals 3D shape recovery using a deformable model

1995 ◽  
Vol 13 (5) ◽  
pp. 377-383 ◽  
Author(s):  
Xinquan Shen ◽  
David Hogg
Keyword(s):  
Shape Recovery ◽  
Deformable Model ◽  
3D Shape ◽  
3D Shape Recovery
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