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A New Content Based Image Retrieval Model Based on Wavelet Transform  [PDF]
Davar Giveki, Ali Soltanshahi, Fatemeh Shiri, Hadis Tarrah
Journal of Computer and Communications (JCC) , 2015, DOI: 10.4236/jcc.2015.33012
Abstract:

Searching interested images based on visual properties of images is a challenging problem and it has received considerable attention from researchers in the fields like image processing, computer vision and multimedia systems in the last 20 years. While the importance and the effect of the image features like color, texture and shape have been taken into account in many papers, there have not been many studies on the importance of the color spaces on the performance of Content Based Image Retrieval (CBIR) systems. In this paper we first experimentally study the effect of choosing color space on the performance of content based image retrieval using Wavelet decomposition of each color channel. To this end, the retrieval results of different color spaces like RGB, YUV, HSV, YCbCr and Lab are analyzed. Then as a result a new Content Based Retrieval model using Wavelet Transform in Lab color space and Color Moments is proposed. In order to increase the efficiency of the proposed model some division schemes are taken into account which improves the performance of the proposed model. The proposed model tackles one of the important restrictions in content based image retrieval, namely, the challenge between the accuracy of retrieval and its time complexity. The experimental results on two databases [19] [24] demonstrate the superiority of the proposed model compared to existing models.

Use of the Wavelet Transform for Digital Terrain Model Edge Detection (Special Issue—Wavelet Analysis)  [PDF]
Clovis Gaboardi
Journal of Applied Mathematics and Physics (JAMP) , 2018, DOI: 10.4236/jamp.2018.610171
Abstract: The purpose of this work is to analyze the feasibility of using the wavelet transform in the edge detection of digital terrain models (DTM) obtained by Laser Scanner. The Haar wavelet transform and the edge detection method called Wavelet Transform Modulus Maxima (WTMM), both implemented in Matlab language, were used. In order to validate and verify the efficiency of WTMM, the edge detection of the same DTM was performed by the Roberts, Sobel-Feldman and Canny methods, chosen due to the wide use in the scientific community in the area of Image Processing and Remote Sensing. The comparison of the results showed superior performance of WTMM in terms of processing time.
NEURAL MODEL FOR IMAGE ENHANCEMENT USING DISCRETE WAVELET TRANSFORM
SYED JAHANGIR BADASHAH, P.SUBBAIAH
International Journal of Electrical, Electronics and Data Communication , 2013,
Abstract: Surface roughness, is a measurement of texture of surface scale features on the surfaces, and is a branch of metrology. Many research had lead machine vision applications in industries, as they have the advantage of non-contact and fast process than contact methods. Machine Vision, is a technology and methods used to provide imaging based automatic inspection and analysis for such applications as automatic inspection, process control and robot guidance in industry be usedto analyze the area of the surface, to make intelligent decision in the applications. In this research work, surface roughness estimation has done by Machine vision system. Feature extraction for the enhanced images in frequency domain done with Transform techniques. A neural network (NN) is trained with the values as input acquired from wavelet Transform and obtain Rt as output. The estimated surface roughness (Rt) based on NN are compared with Rt values of the Stylus method.
Digital Modulation Identification Model Using Wavelet Transform and Statistical Parameters  [PDF]
P. Prakasam,M. Madheswaran
Journal of Computer Networks and Communications , 2008, DOI: 10.1155/2008/175236
Abstract: A generalized modulation identification scheme is developed and presented. With the help of this scheme, the automatic modulation classification and recognition of wireless communication signals with a priori unknown parameters are possible effectively. The special features of the procedure are the possibility to adapt it dynamically to nearly all modulation types, and the capability to identify. The developed scheme based on wavelet transform and statistical parameters has been used to identify M-ary PSK, M-ary QAM, GMSK, and M-ary FSK modulations. The simulated results show that the correct modulation identification is possible to a lower bound of 5 dB. The identification percentage has been analyzed based on the confusion matrix. When SNR is above 5 dB, the probability of detection of the proposed system is more than 0.968. The performance of the proposed scheme has been compared with existing methods and found it will identify all digital modulation schemes with low SNR.
Application of Continues Wavelet Transform to Investigate the Resolution Effect of Digital Elevation Model on Topographic Index Distribution  [PDF]
Vahid Nourani
International Journal of Soft Computing & Engineering , 2011,
Abstract: Current progress in computer, geographic information system (GIS) and remote sensing technology has led to the LIDAR (light detection and ranging) data, as a new generation of high-resolution digital elevation model (DEM). Although such a high resolution DEM can be a useful tool for terrain analysis, high amount of noise is also expected to be existent in a mega-data set. To deal with this problem in this study, continues wavelet transform (CWT), as a mathematical filter, was employed to extract slope, specific area and topographic index which are important parameters in hydro-environmental studies at different resolutions. In this way the proposed methodology was applied to 1-m resolution LIDAR DEM of the Elder Creek River watershed where is a sub-basin of the South Folk Eel River basin at California. The results were also compared favorably with the results of a classic method. The result indicates that smoothing ability of the wavelet can be a promising DEM processing alternative to cope with the noise and pits of a high resolution DEM.
Model Based Ceramic tile inspection using Discrete Wavelet Transform and Euclidean Distance  [PDF]
Samir Elmougy,Ibrahim El-Henawy,Ahmed El-Azab
Computer Science , 2010,
Abstract: Visual inspection of industrial products is used to determine the control quality for these products. This paper deals with the problem of visual inspection of ceramic tiles industry using Wavelet Transform. The third level the coefficients of two dimensions Haar Discrete Wavelet Transform (HDWT) is used in this paper to process the images and feature extraction. The proposed algorithm consists of two main phases. The first phase is to compute the wavelet transform for an image free of defects which known as reference image, and the image to be inspected which known as test image. The second phase is used to decide whether the tested image is defected or not using the Euclidean distance similarity measure. The experimentation results of the proposed algorithm give 97% for correct detection of ceramic defects.
Novel Model Based on Wavelet Transform and GA-fuzzy Neural Network Applied to Short Time Traffic Flow Prediction  [PDF]
Yuesheng Gu,Yancui Li,Jiucheng Xu,Yanpei Liu
Information Technology Journal , 2011,
Abstract: Precise prediction of short time traffic flow is the key point to realize reasonable traffic control and induce. The intelligent Artificial Neural Network (ANN) can provide effective forecasting performance. However, the prediction precision is influenced greatly by the structure of the ANN. Inadequate design of the ANN prediction model may lead to a low prediction rate. In addition, due to the nonlinear and stochastic of the data, it is often difficult to predict the traffic flow precisely. Hence, a new hybrid intelligent forecasting approach base on the integration of Wavelet Transform (WT), Genetic Algorithm (GA) optimization and Fuzzy Neural Network (FNN) is proposed for the short time traffic flow prediction in this study. The advantages of the proposed method are that the WT can process the nonlinear and stochastic characteristics of the original data and GA-FNN offer optimized ANN model to avoid the influence of the improper ANN structure. By doing so, the forecasting rate can be improved much higher than traditional ways. Three hundred and sixty samples of the practical traffic flow data were collected to validate the proposed prediction model. The analysis results showed that the proposed method can extract the underlying rules of the testing data and improve the prediction accuracy by 15% or better when compared with only ANN approach. Thus, the new WT-GA-FNN traffic flow prediction model can provide practical use.
Asymptotic Expansion of Wavelet Transform  [PDF]
Ashish Pathak, Prabhat Yadav, Madan Mohan Dixit
Advances in Pure Mathematics (APM) , 2015, DOI: 10.4236/apm.2015.51002
Abstract: In the present paper, we obtain asymptotic expansion of the wavelet transform for large value of dilation parameter a by using López technique. Asymptotic expansion of Shannon wavelet, Morlet wavelet and Mexican Hat wavelet transform are obtained as special cases.
Uniqueness for the continuous wavelet transform  [PDF]
H. -Q. Bui,R. S. Laugesen
Mathematics , 2011,
Abstract: Injectivity of the continuous wavelet transform acting on a square integrable signal is proved under weak conditions on the Fourier transform of the wavelet, namely that it is nonzero somewhere in almost every direction. For a bounded signal (not necessarily square integrable), we show that if the continuous wavelet transform vanishes identically, then the signal must be constant.
Discovery of a Strongly-Interrelated Gene Network in Corals under Constant Darkness by Correlation Analysis after Wavelet Transform on Complex Network Model  [PDF]
Longlong Liu, Jieqiong Qu, Xilong Zhou, Xuefeng Liu, Zhaobao Zhang, Xumin Wang, Tao Liu, Guiming Liu
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0092434
Abstract: Coral reefs occupy a relatively small portion of sea area, yet serve as a crucial source of biodiversity by establishing harmonious ecosystems with marine plants and animals. Previous researches mainly focused on screening several key genes induced by stress. Here we proposed a novel method—correlation analysis after wavelet transform of complex network model, to explore the effect of light on gene expression in the coral Acropora millepora based on microarray data. In this method, wavelet transform and the conception of complex network were adopted, and 50 key genes with large differences were finally captured, including both annotated genes and novel genes without accurate annotation. These results shed light on our understanding of coral's response toward light changes and the genome-wide interaction among genes under the control of biorhythm, and hence help us to better protect the coral reef ecosystems. Further studies are needed to explore how functional connections are related to structural connections, and how connectivity arises from the interactions within and between different systems. The method introduced in this study for analyzing microarray data will allow researchers to explore genome-wide interaction network with their own dataset and understand the relevant biological processes.
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