Browsing by Author "Pu, HC"

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A HVS-directed neural-network-based approach for impulse-noise removal from highly corrupted images
(2003)
Lu, SM
;
Pu, HC
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper a novel two-stage noise removal algorithm to deal with fixed-value impulse noise is proposed In the first stage, the decision-based recursive adaptive median filter is applied to remove the noise cleanly and keep the uncorrupted information as well as possible. In the second stage, the fuzzy decision rules inspired by human visual system (HVS) are proposed to classify pixels of the image into human perception sensitive class and non-sensitive class. A neural network is proposed to enhance the sensitive regions to perform better visual quality. According to the experiment results, the proposed method is superior to conventional methods in perceptual image quality as well as the clarity and the smoothness in edge regions.
A novel neural-network-based image resolution enhancement
(2003)
Pu, HC
;
Lin, CT
;
Liang, SF
;
Kumar, N
;
電控工程研究所
In this paper, a novel HVS-directed neural-network-based adaptive interpolation scheme for natural image is proposed. A fuzzy decision system built from the characteristics of the human visual system (HVS) is proposed to classify pixels of the input image into human perception nonsensitive class and sensitive class. High-resolution digital images along with supervised learning algorithms are used to automatically train the proposed neural network. Simulation results demonstrate that the proposed new resolution enhancement algorithm can produce higher visual quality of the interpolated image than the conventional interpolation methods.
Genetic algorithm-based neural fuzzy decision tree for mixed scheduling in ATM networks
(2002-12-01)
Lin, CT
;
Chung, IF
;
Pu, HC
;
Lee, TH
;
Chang, JY
Future broad-band integrated services networks based on the asynchronous transfer mode (ATM), technology are expected to support multiple types of multimedia information with diverse statistical characteristics and quality of service (QoS) requirements. To meet these requirements, efficient scheduling methods are important for traffic control in the ATM networks. Among the general scheduling schemes, the rate monotonic algorithm is simple enough to be used in high-speed networks, but it does not attain as high a system utilization as the deadline driven algorithm does. However, the deadline driven scheme is computationally complex and hard to implement in hardware. The mixed scheduling algorithm is the combination of the rate monotonic algorithm and the deadline driven algorithm; thus it can provide most of the benefits of these two algorithms. In this paper, we use the mixed scheduling algorithm to achieve high system utilization under the hardware constraint. Because there is no analytic method for the schedulability test of the mixed scheduling, we propose a genetic algorithm-based neural fuzzy decision tree (GANFDT) to realize it in a real-time environment. The GANFDT combines the GA and a neural fuzzy network into a binary classification tree. This approach also exploits the power of the classification tree. Simulation results show that the GANFDT provides an efficient way to carry out the mixed scheduling in the ATM networks.
Satellite sensor image classification using cascaded architecture of neural fuzzy network
(2000-03-01)
Lin, CT
;
Lee, YC
;
Pu, HC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Satellite sensor images usually contain many complex factors and mixed pixels, so a high classification accuracy is not easy to attain. Especially, for a nonhomogeneous region, gray values of satellite sensor images vary greatly and thus, direct statistic gray values fail to do the categorization task correctly. The goal of this paper is to develop a cascaded architecture of neural fuzzy networks with feature mapping (CNFM) to help the clustering of satellite sensor images, In the CNFM, a Kohonen's self-organizing feature map (SOFM) is used as a preprocessing layer for the reduction of feature domain, which combines original multi-spectral gray values, structural measurements from co-occurrence matrices, and spectrum features from wavelet decomposition. In addition to the benefit of dimensional reduction of feature space, Kohonen's SOFM can remove some noisy areas and prevent the following training process from being overoriented to the training patterns, The condensed measurements are then forwarded into a neural fuzzy network, which performs supervised learning for pattern classification. The proposed cascaded approach is an appropriate technique for handling the classification problem in areas that exhibit large spatial variation and interclass heterogeneity (e.g., urban-rural infringing areas). The CNFM is a general and useful structure that can give us favorable results in terms of classification accuracy and learning speed, Experimental results indicate that our structure can retain high accuracy of classification (90% in average), while the training time is substantially reduced if our system is compared to the commonly used backpropagation network. The CNFM appears to be more reasonable and practical than the conventional implementation.