Kitti Attakitmongcol

A new approach for optimization in image watermarking by using genetic algorithms

In this paper, the authors propose the spread spectrum image watermarking algorithm using the discrete multiwavelet transform. Performance improvement with respect to existing algorithms is obtained by genetic algorithms optimization. In the proposed optimization process, the authors search for parameters that consist of threshold values and the embedding strength to improve the visual quality of watermarked images and the robustness of the watermark. These parameters are varied to find the most suitable for images with different characteristics. The experimental results show that the proposed algorithm yields a watermark that is invisible to human eyes and robust to various image manipulations. The authors also compare their experimental results with the results of previous work using various test images.

Grape leaf disease detection from color imagery using hybrid intelligent system

Vegetables and fruits are the most important export agricultural products of Thailand. In order to obtain more value-added products, a product quality control is essentially required. Many studies show that quality of agricultural products may be reduced from many causes. One of the most important factors of such quality is plant diseases. Consequently, minimizing plant diseases allows substantially improving quality of the products. This work presents automatic plant disease diagnosis using multiple artificial intelligent techniques. The system can diagnose plant leaf disease without maintaining any expertise once the system is trained. Mainly, the grape leaf disease is focused in this work. The proposed system consists of three main parts: (i) grape leaf color segmentation, (ii) grape leaf disease segmentation, and (iii) analysis & classification of diseases. The grape leaf color segmentation is pre-processing module which segments out any irrelevant background information. A self-organizing feature map together with a back-propagation neural network is deployed to recognize colors of grape leaf. This information is used to segment grape leaf pixels within the image. Then the grape leaf disease segmentation is performed using modified self-organizing feature map with genetic algorithms for optimization and support vector machines for classification. Finally, the resulting segmented image is filtered by Gabor wavelet which allows the system to analyze leaf disease color features more efficient. The support vector machines are then again applied to classify types of grape leaf diseases. The system can be able to categorize the image of grape leaf into three classes: scab disease, rust disease and no disease. The proposed system shows desirable results which can be further developed for any agricultural product analysis/inspection system.

Wavelet-based Neural Network for Power Quality Disturbance Recognition and Classification

Abstract Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a new approach for the recognition of power quality disturbances using wavelet transform and neural networks. The proposed method employs the wavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, voltage swell, interruption, notching, impulsive transient, and harmonic distortion. The results show that the classifier can efficiently detect and classify different types of power quality disturbance.

Detection of defect in textile fabrics using optimal Gabor wavelet network and two-dimensional PCA

The aim of production line enhancement in any industry is to improve quality and reduce operating costs by applying various kinds of advanced technology. In order to become more competitive, many sensing, monitoring, and control approaches have been investigated in the textile industry. Automated visual inspection is one area of improvement where real cost savings can be realized over traditional inspection techniques. Manual visual inspection of textile products is expensive and error-prone because of the difficult working environment near the weaving machine. Automated visual detection of fabric defects is particularly challenging due to the large variety of fabric defects and their various degrees of vagueness and ambiguity. This work presents a hybrid application of Gabor filter and two-dimensional principal component analysis (2DPCA) for automatic defect detection of texture fabric images. An optimal filter design method for Gabor Wavelet Network (GWN) is applied to extract texture features from textile fabric images. The optimal network parameters are achieved by using Genetic Algorithm (GA) based on the non-defect fabric images. The resulting GWN can be deployed to segment and identify defect within the fabric image. By using 2DPCA, improvement of defect detection can significantly be obtained. Experimental results indicate that the applied Gabor filters efficiently provide a straight-forward and effective method for defect detection by using a small number of training images but still can generally handle fabric images with complex textile pattern background. By integrating with 2DPCA, desirable results have been simply and competently achieved with 98% of accuracy.

Recognition of Power Quality Events by Using Multiwavelet-Based Neural Network

Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a novel approach for the recognition of power quality disturbances using multiwavelet transform and neural networks. The proposed method employs the multiwavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, swell, interruption, notching, impulsive transient, and harmonic distortion show that the classifier can detect and classify different.

Digital Audio Watermarking for Copyright Protection Based on Multiwavelet Transform

In this paper, a robust watermarking scheme for copyright protection of digital audio signal is proposed. The watermarks are embedded into the low frequency coefficients in discrete multiwavelet transform domain to achieve robust performance against common signal processing procedures and noise corruptions. The embedding technique is based on quantization process which does not require the original audio signal in the watermark extraction. The experimental results show that the proposed scheme yields the watermark audio signal with high quality and the watermark survives to most of the attacks which were included in this study.

Wavelet-Based intelligent system for recognition of power quality disturbance signals

Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a new approach for the recognition of power quality disturbances using wavelet transform and neural networks. The proposed method employs the wavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, swell, interruption, notching, impulsive transient, and harmonic distortion show that the classifier can detect and classify different power quality signal types efficiency.

Wavelet-based neural classification for power quality disturbances

The objective of this paper is to present a new method for automatically detecting, localizing and classifying various types of power quality disturbances. The new method is based on wavelet transform analysis, artificial neural networks, and the mathematical theory of evidence. The proposed detection and localization algorithm is carried out in the wavelet transform domain using multiresolution signal decomposition techniques and the proposed classification method is carried out in the sets of multiple neural networks using a learning vector quantization network. The outcomes of the networks are then integrated using a voting decision making scheme. The performance of the automatic detection and localization have 90.14% accuracy and the error is less than 5%.

Multiwavelet-based image watermarking using genetic algorithm

In this paper, we propose an image watermarking algorithm using the discrete multiwavelet transform. The watermark insertion and watermark detection are based on the techniques for the DWT-based image watermarking proposed by Dugad et al. In our method, the watermark is embedded to the multiwavelet transform coefficients larger than some threshold values. We have developed an optimization technique using the genetic algorithm to search for optimal threshold values and the strength of the watermark to improve the quality of watermarked image and robustness of the watermark. The experimental results show that the proposed algorithm yields watermark which is invisible to human eyes and robust to various image manipulations. We then compare our experimental results with the results of previous work using various test images.

The effects of transformation methods in image watermarking

Image watermarking provides copyright protection and becomes very crucial for ownership verification of digital images. In this paper, we investigate the effects of different types of transformations in image watermarking algorithm including discrete cosine transform, discrete wavelet transform, and discrete multiwavelet transform. We also provide a brief overview of the multiwavelet transform since it is relatively new as compared to the other transforms. The efficiencies of these transforms are discussed by evaluating watermarked image quality and robustness of the watermark. Experimental results show that the multiwavelet transform method is superior to other two methods in term of image quality.