TY  - JOUR
EP  - 245
SN  - 03029743
TI  - 3D shape from focus using LULU operators
SP  - 237
N1  - cited By 0; Conference of 14th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2012 ; Conference Date: 4 September 2012 Through 7 September 2012; Conference Code:92770
AV  - none
VL  - 7517 L
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84866383705&doi=10.1007%2f978-3-642-33140-4_21&partnerID=40&md5=a5c430affecf8a6206fbef44fdf9e877
A1  - Rahmat, R.
A1  - Mallik, A.S.
A1  - Kamel, N.
A1  - Choi, T.-S.
A1  - Hayes, M.H.
JF  - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Y1  - 2012///
KW  - 3-D shape; Discrete pulse transform; Focal lengths; Focus values; Gray levels; Morphological filters; Noise levels; Pre-processing step; Real images; Sequence of images; Shape from focus; Shape recovery; Structure preserving properties; Sum-Modified-Laplacian; Vision applications
KW  -  Impulse noise; Shape optimization
KW  -  Three dimensional
ID  - scholars2890
N2  - Extracting the shape of an object is one of the important tasks to be performed in many vision applications. One of the difficult challenges in 3D shape extraction is the roughness of the surfaces of objects. Shape from focus (SFF) is a shape recovery method that reconstructs the shape of an object from a sequence of images taken from the same viewpoint but with different focal lengths. This paper proposes the use of LULU operators as a preprocessing step to improve the signal-to-noise ratio in the estimation of 3D shape from focus. LULU operators are morphological filters that are used for their structure preserving properties. The proposed technique is tested on simulated and real images separately, as well as in combination with traditional SFF methods such as sum modified Laplacian (SML), and gray level variance (GLV). The proposed technique is tested in the presence of impulse noise with different noise levels. Based on the quantitative and qualitative experimental results it is shown that the proposed techniques is more accurate in focus value extraction and shape recovery in the presence of noise. © 2012 Springer-Verlag.
CY  - Brno
ER  -