Dimensional Discrete Wavelet Transform Research Articles

Traffic Monitoring System for Vehicle Detection in Day and Night Conditions

Abstract In this work, a day and night time vehicle detection system for traffic surveillance is proposed. Our system is composed of two main processes, day time and night time processes. In the night time, the vehicles are detected based on their taillights and headlights. First of all, the 2D-DWT (Two Dimensional Discrete Wavelet Transform) and the background subtraction are applied to the input image. Then, the connected component technique is used to extract the region of interest. If it is the daytime, the connected component candidates are taken as potential vehicles after applying a pre-processing algorithm to improve the result. If it is the night-time, a filtering operation is used to keep only the bright white and red connected component candidates (which represent potential headlights and taillights, respectively). Finally, potential lamp sets are formed by grouping the extracted components on the basis of their positions, sizes, and colours. The potential extracted vehicles are classified as a vehicle or non-vehicle by using a pre-trained CNN (Convolutional Neural Network) classifier. The proposed system was tested and evaluated using different works from the literature. The experiments show that our proposed system has reached a high accuracy in terms of vehicle detection process whether in day or night time. The experiments were performed using four different videos and were implemented using the C++ language, which facilitates mathematical computation, and its OpenCV library, which is used to run the image processing algorithms used, as well as the TensorFlow library, which facilitates transfer learning of pre-trained CNN models.

Arbitrary oriented multilingual text detection and segmentation using level set and Gaussian mixture model

A pioneer conceptual combination of level set method and Gaussian Mixture Model (GMM) is presented in the described multilingual, arbitrary-oriented character segmentation. The method is a serial accomplishment processes of Gaussian low pass filter, single level 2 Dimensional Discrete Wavelet Transform (2D DWT) for better feature extraction and implemented level set method, k-means clustering algorithm with GMM to achieve a veracious character segmentation results by detecting great measure of true text region of an image. The proposed method segments a character chiefly by distinguishing the touching character constituents and also deals discontinuities presence in a character by using Laplacian of Gaussian filter and morphological bridge function in a intellectual way. The exhibited method was explored on Multi-script Robust Reading Competition dataset and on our privately collected graphical and handwritten multilingual, arbitrarily-oriented text images. The suggested method is compared with the well known multilingual and arbitrarily-oriented charter segmentation methods. The described method attains better segmentation outcomes when compared to the familiar functioning methods. Hence, the suggested method is highly suitable to consider as an improved, standard and procedural technique.

Implementation of Efficient Architecture of Fine Grain Pipelined Lifting Scheme Based Two Dimensional Discrete Wavelet Transform

Many Discrete Wavelet Transform (DWT) based VLSI architectures have been projected to meet the necessities of the synchronized signal processing. It includes image processing, speech processing, signal and video processing, etc. The practical implementation of DWT has fewer hitches in terms of hardware complexity and memory requirement since it needs to process huge volume of data. The traditional convolution based system needs more multipliers and larger memory and is also not suitable to provide speed or power efficient image or video processing designs. The lifting scheme involves very few mathematical computations compared to the convolution-based DWT. In this paper, we propose an architecture that performs Discrete Wavelet Transform (DWT) using a lifting-based scheme with fine grained pipelined architecture. The basic DWT filters used in image compression are 5/3(lossless) and 9/7(lossy) filters. In fine grain pipelining, multiplier is split into two units by placing the latches on the horizontal cutset across the multiplier. Thus the critical path is reduced to half of the multiplier delay. As a result, it is a speed efficient architecture and is symmetrical with a lower hardware complexity. The architecture is designed using verilog HDL and implemented on Xilinx Spartan 3E FPGA.

Separation and Interpretation of Gravity Field Data Based on Two Dimensional Normal Space-Scale Transform (NSST2D) Algorithm: A Case Study of Kauring Airborne Gravity Test Site, Western Australia

The gravity field signal observed on the surface of the earth is the superposition of the gravitational effects produced mainly by the mass sources at different locations and depths from the surface down to the underground. Reliable geophysical interpretation of gravity anomalies is highly dependent on the validity of the data processing methods and the rationality of the calculated results. This paper proposes a novel potential field separation method based on two-dimensional Normal Space-Scale Transform (NSST2D) algorithm to implement the residual-regional field separation. Compared with the two dimensional Discrete Wavelet Transform (DWT2D) method often appeared in the previous potential field separation studies, the NSST2D algorithm can keep the shape of the separated field from distortion by normalizing the conventional two-dimensional wavelet transform kernels. Based on gravity field of synthetic models, the separated residual-regional fields respectively derived from DWT2D with five different wavelet bases and those from NSST2D are compared. The experimental results verify the effectiveness and superiority of the proposed NSST2D algorithm. Finally, the NSST2D algorithm is also applied to the separation of the complete Bouguer gravity anomaly in the Kauring airborne gravity test site, Western Australia. The results show that the characteristics of the separated field from the corresponding mass sources with different depths maintain relatively good consistency with the feature of the three-dimensional inversion density contrast distribution.

Comparison of Discrete Cosine Transforms (DCT), Discrete Fourier Transforms (DFT), and Discrete Wavelet Transforms (DWT) in Digital Image Watermarking

Digital Image Watermarking is used recently to secure the image by embedding another digital image. It is typically used to identify ownership of the copyright of the signal. Frequency domain transformation methods used widely in Digital Image Compression and Digital Image Watermarking. They reduce the weakness of classics digital image watermarking such as Least Significant Bit (LSB) methods which is more noise-tolerant. Popular transformation method used are Two Dimensional Discrete Cosine Transform (2D DCT), Two Dimensional Discrete Fourier Transforms (2D DFT), and Two Dimensional Discrete Wavelet Transform (2D DWT). This paper will show the comparison result of those three transformation method. The experiments are comparison analysis of image watermark quality using Peak Signal to Noise Ratio (PSNR), color converting, image resizing, image optical scanning and the noise-tolerant of the image watermarked by giving Gaussian noise.

Adaptive Directional Lifting Structure of Three Dimensional Non-Separable Discrete Wavelet Transform for High Resolution Volumetric Data Compression

Adaptive Directional Lifting Structure of Three Dimensional Non-Separable Discrete Wavelet Transform for High Resolution Volumetric Data Compression

Design of Secure Ad Hoc Network Using Three Dimensional Discrete Wavelet Transformation Based on Performance Enhancement

This work shows new and efficient algorithm of cryptographic purpose based symmetric and conventional techniques that considers the representation of the cipher text by using the three dimensional Discrete Wavelet Transform to find the wavelet decomposition vector containing the approximation and the detail coefficients then build the three dimensional data structure approach. The decryption is done by extracting the encrypted data from the wavelet decomposition vector using the algorithm of inverse Discrete Wavelet Transformation. The encrypted message consists the wavelet decomposition vector. The key is used for authorization purpose to access the network. Results shows great data security and BER over wireless channels based Ad Hoc network.

Text Extraction of Vehicle Number Plate and Document Images Using Discrete Wavelet Transform in MATLAB

Text Extraction from colour images is a challenging task in computer vision. The concept of text extraction is derived from the vehicle plate recognization and their characters extractions individually. Some examples of the applications are automatic image indexing, visual impaired people assistance or optical character reading, keyword searching in a document image. The continuous research has been done majorly that focuses on the text detection and localization in images. The paper focuses on the colour images text extraction using Two Dimensional Discrete Wavelet Transform (2D-DWT). The transform used for DWT is HAAR DWT with mathematical morphological concept. The speed of Haar DWT operates the fastest among all wavelets because its coefficients are either 1 or -1. It is one of the reasons that Haar DWT is used to detect edges of candidate text regions. Image sub bands contain both text edges and non-text edges. The work is carried out in MATLAB 12.0 image processing tool and simulated results are tested on vehicle plate text extraction and the document images.

FPGA Implementation of a High-Speed Two Dimensional Discrete Wavelet Transform

This paper proposes high-speed VLSI architecture for implementing a forward two-dimensional discrete wavelet transform (2D DWT). The architecture is based on 2D DWT mathematical formulae. A pipelined scheme is used to increase the clock rate, which allows its critical path to take only one adder delay. The proposed design enables 100% hardware use and faster computing than other 2D DWT architecture. It is easily extended to multilevel decomposition because of its regular structure. It requires N/2 by N/2 clock cycles for k-level analysis of an N by N image. The proposed architecture was coded in VerilogHDL and verified on a real time platform which uses a CMOS image sensor, a field-programmable gate array (FPGA) and a TFT-LCD panel. In the simulation, the design worked with a clock period of 132.38MHz. It can be used as an independent IP core for various real-time applications.

WAVELET STATISTICAL TEXTURE FEATURES WITH ORTHOGONAL OPERATORS TUMOUR CLASSIFICATION IN MAGNETIC RESONANCE IMAGING BRAIN

Tumors medically also called neoplasms are an abnormal mass of tissue resulting from uncontrolled proliferation or division of cells occurring in the human body. If such growth is located in the brain then it is called as brain tumor. Identification of such tumors is a major challenge in the field of medical science. Early identification of tumors prove to be critical as serious consequences can be averted. Its threat level depends on a combination of various factors like the type of tumor, its location, its size and its developmental stage. Tumor can occur in any part of the body. Magnetic Resonance Imaging (MRI) technique is mainly used for analyzing the brain, as the images produced are of high precision and applicability. The main objective of this study is to classify the brain MRI dataset for the existence or non existence of tumors. The proposed method uses Two Dimensional Discrete Wavelet Transform (2D-DWT) for pre-processing and further classification with orthogonal operators and SVM. The usage of 2D-DWT for pre-processing improves the classification accuracy by 2% when compared to the existing classification techniques.

Wavelet Feature Extraction and Genetic Algorithm for Biomarker Detection in Colorectal Cancer Data

Biomarkers which predict patient’s survival can play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers of survival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were located based on the position of optimized features. Kaplan-Meier curve and Cox regression model 2 were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to significantly associate with survival time.

Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data

Biomarkers which predict patient’s survival play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers of survival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were located based on the position of optimized features. Kaplan–Meier curve and Cox regression model were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to be significantly associated with survival time.

Fingerprint detection based on DWT a new approach

Fingerprint detection is one of the most important applications of image processing of a security system. Recent Literatures deal with fingerprint detection based on pattern recognition, moment based image recognition and discrete wavelet transform (DWT). Among them DWT based analysis requires the least amount of memory space which considers only translation of image of fingerprint (not rotation) in matching with preserved images. This paper proposes a new approach which combines one and two dimensional DWT along with convolution of filtered signal of DWT yields better results in context of image rotation and translation.

Design Techniques and Implementation of Low Power High-Throughput Discrete Wavelet Transform Tilters for JPEG 2000 Standard

Abstract—In this paper, the implementation of low power, high throughput convolutional filters for the one dimensional Discrete Wavelet Transform and its inverse are presented. The analysis filters have already been used for the implementation of a high performance DWT encoder [15] with minimum memory requirements for the JPEG 2000 standard. This paper presents the design techniques and the implementation of the convolutional filters included in the JPEG2000 standard for the forward and inverse DWT for achieving low-power operation, high performance and reduced memory accesses. Moreover, they have the ability of performing progressive computations so as to minimize the buffering between the decomposition and reconstruction phases. The experimental results illustrate the filters’ low power high throughput characteristics as well as their memory efficient operation.

Evaluating the Robustness of Objective Pilling Classification with the Two- Dimensional Discrete Wavelet Transform

Previously, we proposed a new method of frequency domain analysis based on the two-dimensional discrete wavelet transform to objectively measure pilling intensity in sample fabric images. We have further evaluated this method, and our results indicate that it is robust to small horizontal and/or vertical translations and to significant variations in the brightness of the image under analysis, and is sensitive to rotation and to dilation of the image. These results suggest that as long as precautions are taken to ensure fabric test samples are imaged under consistent conditions of weave/knit pattern alignment (rotation) and apparent interyarn pitch (dilation), the method will yield repeatable results.

An efficient VLSI architecture for the computation of 1-D discrete wavelet transform

This paper presents a new architecture for VLSI implementation of one dimensional discrete wavelet transform (DWT). The architecture uses a single filter for the generation of both DWT coefficients and a scaling function for orthogonal wavelets as opposed to the conventional two filter approach. For subsequent levels, we rely on the fold back architecture principles which interleave the decimated scaling functions back into the filters for the subsequent DWT coefficients. Limited use of memory in the design enables efficient implementation of the DWT computation in VLSI.

VLSI Implementation of Discrete Wavelet Transform for Lossless Compression of Medical Images

This paper presents a VLSI architecture to implement the forward and inverse two dimensional Discrete Wavelet Transform (DWT), to compress medical images for storage and retrieval. Lossless compression is usually required in the medical image field. The word length required for lossless compression makes too expensive the area cost of the architectures that appear in the literature. Thus, there is a clear need for designing a cost-effective architecture to implement the lossless compression of medical images using DWT. The data path word length has been selected to ensure the lossless accuracy criteria leading a high speed implementation with small chip area. The pyramid algorithm is reorganized and the algorithm locality is improved in order to obtain an efficient hardware implementation. The result is a pipelined architecture that supports single chip implementation in VLSI technology. The implementation employs only one multiplier and 352 memory elements to compute all scales what results in a considerable smaller chip area (45 mm2) than former implementations. The hardware design has been captured by means of the VHDL language and simulated on data taken from random images. Implemented in a 0.7 μm technology, it can compute both the forward and inverse DWT at a rate of 3.5 512×512 12 bit images/s corresponding to a clock speed of 33 MHz. This chip is the core of a PCI board that will speedup the DWT computation on desktop computers.