Prayoth Kumsawat

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.

A genetic algorithm optimization technique for multiwavelet-based digital audio watermarking

We propose a new approach for optimization in digital audio watermarking using genetic algorithm. The watermarks are embedded into the low frequency coefficients in discrete multiwavelet transform domain. The embedding technique is based on quantization process which does not require the original audio signal in the watermark extraction. We have developed an optimization technique using the genetic algorithm to search for four optimal quantization steps in order to improve both quality of watermarked audio and robustness of the watermark. In addition, we analyze the performance of the proposed algorithm in terms of signal-to-noise ratio, normalized correlation, and bit error rate. The experimental results show that the proposed scheme can achieve a good robustness against most of the attacks which were included in this study.

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.

Wavelet-Based Image Watermarking Using the Genetic Algorithm

Image watermarking provides copyright protection of digital image by hiding appropriate information in the original image in such a way that it does not cause degradation of the perceptual image quality and cannot be removed. The watermarking methods for transform domains are usually achieved by using the discrete cosine transform or the discrete wavelet transform. In this paper, we develop a technique for optimizing the image watermarking using the genetic algorithm applied to the wavelet transform domain to improve the quality of the watermarked image and the robustness of the watermark. We then compare our experimental results with the results of previous works.

A Robust Wavelet-Based Text-Independent Speaker Identification

This study proposes novel robust text-independent speaker identification based on the discrete wavelet transform (DWT), the mel-frequency discrete wavelet coefficients (MFDWC), the wavelet-based sub-band weighting and the likelihood combination Gaussian mixture model (LCGMM). This method is used in the text-independent speaker identification in compare to the widely used MFCC features recognizer, full-band recognizer and equal sub-band weighting recognizer. Our experimental results show that our proposal achieves higher recognition rate than the others for our Vietnamese speech corpus with clean and white noisy speech.

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.

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.

An efficient digital audio watermarking scheme based on genetic algorithm

This paper proposes an efficient digital audio watermarking scheme using artificial intelligent technique. The watermarks are embedded into the low frequency coefficients in discrete multiwavelet transform domain. The embedding technique is based on quantization process which does not require the original audio signal in the watermark extraction. We have developed an optimization technique using the genetic algorithm to search for optimal quantization step in order to improve both quality of watermarked audio signal and robustness of the watermark. Experiment results shows that the proposed watermarking scheme is robust against various signals processing such as re-sampling, re-quantization, low-pass filtering, noise adding, random cropping, MPEG 1 Layer III compression and digital-to-analog/analog-to-digital conversion.

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.