Title :
Neural-network-based adaptive hybrid-reflectance model for 3-D surface reconstruction
Author :
Lin, Chin-Teng ; Cheng, Wen-Chang ; Liang, Sheng-Fu
Author_Institution :
Control Eng. & the Dept. of Comput. Sci., Nat. Chiao-Tung Univ., Taipei, Taiwan
Abstract :
This paper proposes a novel neural-network-based adaptive hybrid-reflectance three-dimensional (3-D) surface reconstruction model. The neural network automatically combines the diffuse and specular components into a hybrid model. The proposed model considers the characteristics of each point and the variant albedo to prevent the reconstructed surface from being distorted. The neural network inputs are the pixel values of the two-dimensional images to be reconstructed. The normal vectors of the surface can then be obtained from the output of the neural network after supervised learning, where the illuminant direction does not have to be known in advance. Finally, the obtained normal vectors are applied to enforce integrability when reconstructing 3-D objects. Facial images and images of other general objects were used to test the proposed approach. The experimental results demonstrate that the proposed neural-network-based adaptive hybrid-reflectance model can be successfully applied to objects generally, and perform 3-D surface reconstruction better than some existing approaches.
Keywords :
computational complexity; image reconstruction; learning (artificial intelligence); neural nets; adaptive hybrid-reflectance model; diffuse component; facial image; hybrid model; illuminant direction; neural network; normal vector; pixel value; specular component; supervised learning; surface reconstruction; two-dimensional image; Brightness; Computer vision; Cost function; Image reconstruction; Light sources; Neural networks; Pixel; Shape; Supervised learning; Surface reconstruction; Enforcing integrability; Lambertian model; neural network; reflectance model; shape from shading; surface normal; Algorithms; Artificial Intelligence; Cluster Analysis; Face; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Neural Networks (Computer); Pattern Recognition, Automated; Photography; Reproducibility of Results; Sensitivity and Specificity;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2005.853333