Browsing by Author "Cheng, WC"

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A 3-D surface reconstruction approach based on postnonlinear ICA model
(2005-11-01)
Lin, CT
;
Cheng, WC
;
Liang, SF
;
生物科技學系
;
資訊工程學系
Photometric stereo technique deals with the reconstruction of three-dimensional (3-D) shape of an object by using several images of the same surface taken from the same viewpoint but under illuminations from different directions. In this paper, we propose a new photometric stereo scheme based on a new reflectance model and the postnonlinear (PNL) independent components analysis (ICA) method. The proposed nonlinear reflectance model consists of diffuse components and specular components for modeling the surface reflectance of a stereo object in an image. Unlike the previous approaches, these two components are not separated and processed individually in the proposed model. An unsupervised learning adaptation algorithm is developed to estimate the reflectance model based on image intensities. In this algorithm, the PNL ICA method is used to obtain the surface normal on each point of an image. Then, the 3-D surface model is reconstructed based on the estimated surface normal on each point of image by using the enforcing integrability method. Two experiments are performed to assess the performance of the proposed approach. We test our algorithm on synthetically generated images for the reconstruction of surface of objects and on a number of real images captured from the Yale Face Database B. These testing images contain variability due to illumination and varying albedo in each point of surface of human faces. All the experimental results are compared to those of the existing photometric stereo approaches tested on the same images. The results clearly indicate the superiority of the proposed nonlinear reflectance model over the conventional Lambertian model and the other linear hybrid reflectance model.
A recurrent fuzzy cellular neural network system with automatic structure and template learning
(2004-05-01)
Lin, CT
;
Chang, CL
;
Cheng, WC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
It is widely accepted that using a set of cellular neural networks (CNNs) in parallel can achieve higher level information processing and reasoning functions either from application or biologics points of views. Such an integrated CNN system can solve more complex intelligent problems. In this paper, we propose a novel framework for automatically constructing a multiple-CNN integrated neural system in the form of a recurrent fuzzy neural network. This system, called recurrent fuzzy CNN (RFCNN), can automatically learn its proper network structure and parameters simultaneously. The structure learning includes the fuzzy division of the problem domain and the creation of fuzzy rules and CNNs. The parameter learning includes the tuning of fuzzy membership functions and CNN templates. In the RFCNN, each learned fuzzy rule corresponds to a CNN. Hence, each CNN takes care of a fuzzily separated problem region, and the functions of all CNNs are integrated through the fuzzy inference mechanism. A new online adaptive independent component analysis mixture-model technique is proposed for the structure learning of RFCNN, and the ordered-derivative calculus is applied to derive the recurrent learning rules of CNN templates in the parameter-learning phase. The proposed RFCNN provides a solution to the current dilemma on the decision of templates and/or fuzzy rules in the existing integrated (fuzzy) CNN systems. The capability of the proposed RFCNN is demonstrated on the real-world defect inspection problems. Experimental results show that the proposed scheme is effective and promising.
An illumination estimation scheme for color constancy based on chromaticity histogram and neural network
(2005)
Lin, CT
;
Fan, KW
;
Cheng, WC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes an algorithm for estimation the illumination of an image for the purpose of color constancy using chromaticity histogram. This method evaluates the color temperature of the light source by detecting the distribution of chromaticity histogram in an image. It has the advantages of high efficiency, good robustness, and no strict assumptions. We subsequently propose to use a multilayer perceptrons trained by Backpropagation algorithm to model the nonlinear functional relationship between the chromaticity histogram and coefficients of illuminant functions. The trained neural network can then be used to estimate the spectral power distribution of light source. Finally, we use the trained neural network to estimate spectral power distribution in a finite-dimensional linear model of surface reflectance for color constancy. For performance evaluation, two color-recovery experiments on synthetic images and nature images captured from a still digital camera are performed in this paper. All the results are compared to those of two existing popular algorithms (Max-RGB and Gray-World algorithms).
An on-line ICA-mixture-model-based fuzzy neural network
(2004)
Lin, CT
;
Cheng, WC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a new fuzzy neural network (FNN) capable of parameter self-adapting and structure self-constructing to acquire a small number of fuzzy rules for interpreting the embedded knowledge of a system from the given training data set. The proposed FNN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule-based model with neural network's learning ability. There are no rules initiated at the beginning and they are created and adapted through the newly proposed on-line independent component analysis (ICA) mixture model and back-propagation algorithm learning processing that performs simultaneous structure and parameter identification. Several experiments covering the areas of system identification and classification are carried out. These results show that the proposed FNN can achieve significant improvements in the convergence speed and prediction accuracy with simpler network structure.
An on-line ICA-mixture-model-based self-constructing fuzzy neural network
(2005-01-01)
Lin, CT
;
Cheng, WC
;
Liang, SF
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a new fuzzy neural network (FNN) capable of parameter self-adapting and structure self-constructing to acquire a small number of fuzzy rules for interpreting the embedded knowledge of a system from the given training data set. The proposed FNN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy-rule-based model with neural network's learning ability. There are no rules initiated at the beginning and they are created and adapted through an on-line learning processing that performs simultaneous structure and parameter identification. In the structure identification of the precondition part, the input space is partitioned in a flexible way according to newly proposed on-line independent component analysis (ICA) mixture model. The input space is thus represented by linear combinations of independent, non-Gaussian densities. The first input training pattern is assigned to the first rule initially by the on-line ICA mixture model. Afterwards, some additional significant terms (input variables) selected by the on-line ICA mixture model will be added to the consequent part (forming a liner equation of input variables) incrementally or create a new rule in the learning processing. The combined precondition and consequent structure identification scheme can make the network grow dynamically and efficiently. In the parameter identification, the consequent parameters are tuned by the backpropagation rule and the precondition parameters are turned by the on-line ICA mixture model. Both the structure and parameter identifications are done simultaneously to form a fast learning scheme. The derived on-line ICA mixture model also provide a natural linear transformation for each input variable to enhance the knowledge representation ability of the proposed FNN and reduce the required rules and achieve higher accuracy efficiently. in order to demonstrate the performance of the proposed FNN, several experiments covering the areas of system identification, classification, and image segmentation are carried out. Our experiments show that the proposed FNN can achieve significant improvements in the convergence speed and prediction accuracy with simpler network structure.
Neural-network-based adaptive hybrid-reflectance model for 3-D surface reconstruction
(2005-11-01)
Lin, CT
;
Cheng, WC
;
Liang, SF
;
生物科技學系
;
資訊工程學系
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.