Authors:
Yuji Iwahori
1
;
Seiya Tsuda
1
;
Robert J. Woodham
2
;
M. K. Bhuyan
3
and
Kunio Kasugai
4
Affiliations:
1
Chubu University, Japan
;
2
University of British Columbia, Canada
;
3
Indian Institute of Technology Guwahati, India
;
4
Aichi Medical University, Japan
Keyword(s):
Endoscope Image, VBW Model, RBF-NN, Shape Modification, Reflection Factor.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Medical Imaging
;
Pattern Recognition
;
Sensors and Early Vision
;
Shape Representation
;
Software Engineering
Abstract:
The VBW (Vogel-Breuß-Weickert) model is proposed as a method to recover 3-D shape under point light
source illumination and perspective projection. However, the VBW model recovers relative, not absolute,
shape. Here, shape modification is introduced to recover the exact shape. Modification is applied to the output
of the VBW model. First, a local brightest point is used to estimate the reflectance parameter from two images
obtained with movement of the endoscope camera in depth. After the reflectance parameter is estimated, a
sphere image is generated and used for Radial Basis Function Neural Network (RBF-NN) learning. The NN
implements the shape modification. NN input is the gradient parameters produced by the VBW model for
the generated sphere. NN output is the true gradient parameters for the true values of the generated sphere.
Depth can then be recovered using the modified gradient parameters. Performance of the proposed approach
is confirmed via computer simulation and real exp
eriment.
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