Discrete wavelet transform

: Detecting underwater objects is challenged by various kinds of aspect ratios, object size,material colour, cluttered
backgrounds, and in particular,not defined orientations. In our paper, we are using a Probabilistic Neural Network (PNN)
features from layers which are combined to perform orientation robust aerial underwater object detection. We explore the
essential characteristics of PNN as well as corelate the extracted features to the principle of disengaging feature learning. An
image segmentation based approach is used to localize ROIs of different aspect ratios, and furtherly ROIs are classified into
positives or negative using a DCNN features. On inquiring the two datasets collected from Google Earth, we illustrate that the
proposed aerial underwater object detection approach is simple and easy process. Fast and robust underwater object detection in
aerial images is potentially applicable in traffic surveillance, emergency, remote sensing and large scale image content analysis.

—Face recognition has become an important subject in modern life, especially in security and surveillance applications. This work introduces a face recognition method, which is characterized by high-speed, low-complexity, and high-efficiency in a noisy environment. The performance of this method is greatly improved by using a median filter, such that each image is filtered to eliminate the influence of noise and light illumination. Multiple levels of discrete wavelet transform are applied to the filtered image to reduce size and eliminate further noise. Subsequently, the resultant image is scanned using a window with a predefined overlap in raster fashion to construct a sequence of observation vectors used as the basis of a model. The model consists of two states of a continuous hidden Markov model, a unique model in the face recognition field that has not yet been used by other researchers, which interprets the novelty and low complexity of the method. In spite of the presence of 0.15, 0.4, and 0.25 measurement values of impulse noise density in images stored in the ORL, Yale, and EURECOM Kinect face databases, respectively, the proposed work has achieved a recognition rate of 100%. I. INTRODUCTION Face recognition has recently received considerable attention due to its important applications in many areas and fields such as credit card verification, security, and surveillance. The advantage of face recognition over other biometric techniques is the mechanism to recognize persons, which is frequently performed without requiring that the person have physical contact with the device, e.g. use of palm or fingers. The face image can be captured by a camera without informing the target person, especially in the case of criminal surveillance and apprehension. The methods introduced to recognize faces vary relative to the procedure used for feature extraction and type of classifier used. The artificial neural network [1], principle component analysis (PCA) [2], independent component analysis [3], linear discriminant analysis (LDA) [4], support vector machine [5] and K-nearest neighbour [6] are the most widely used methods. In addition, the hidden Markov model (HMM) [7] has been successfully used in face recognition during the last two decades. The advantage of applying HMM to face recognition is the flexibility of the selection of training models, such that it is easy to add or remove individuals. There is no need for retraining the overall system; however, a model with updated images must be retrained. For dimensionality reduction, a particular feature extraction method is applied to form a sequence of observation vectors to model faces. Thereafter, for each individual, one HMM model is constructed by training a specified set of images for that individual. The training process depends on the estimation of the HMM parameters [8], such that the process is iterated several times before it converges. In the testing process, the algorithm computes the probability of the observations of unknown face image calibrated with the parameters derived from the training. The unknown face belongs to the person whose model computes the highest probability. The disadvantages of using more states of the HMM are: high computational complexities, large memory requirements, and low processing speed. The proposed work addresses the unfavourable features by using only two states of the HMM.

Automatic network road extraction from high resolution remotely sensed images has been under study by computer scientists for over 30 years. In fact, Conventional methods to create and update road information rely heavily on manual work and therefore are very expensive and time consuming. This paper presents an efficient and computationally fast method to extract road from very high resolution images automatically. We propose in this paper a new approach for following roads path based on a quaternionic wavelet transform insuring a good local space-frequency analysis with very important directional selectivity. In fact, the rich phase information given by this hypercomplex transform overcomes the lack of shift invariance property shown by the real discrete wavelet transform and the poor directional selectivity of both real and complex wavelet transform.

This work represents a comparative study for the methods of registration for dental panoramic X-ray images. The study tried to find the best registration technique used to register images of the jaw. Three image registration techniques are implemented and applied to dental panoramic X-ray images in both spatial domain and wavelet domain. The first technique is based on cross-correlation. The second one depends on selecting control points from both the reference and the input images. The last technique is based on maximization of mutual information between the two images. In the wavelet domain, discrete wavelet transform (DWT) has been performed to the reference image and the test image before the three techniques have been applied. Then the registration methods have been applied using approximation coefficients in the registration procedure. After finishing the registration steps, inverse discrete wavelet transform (IDWT) has been applied in order to obtain the registered image. The...

En diversos países pequeños grupos de arqueólogos se han lanzado a utilizar medidas de dimensión fractal aplicadas a distintas clases de objetos, desde ornamentación cerámica a motivos de bordados, desde plantas urbanas a edificios. Una inmensa mayoría, en cambio, está absolutamente en contra de esas formas de medición, usualmente por las razones equivocadas..

Existen numerosos fenómenos y estructuras (tanto en la cultura como en la naturaleza) cuya geometría exhibe una dimensión fraccional que difiere de la de los objetos de la geometría euclideana. Esa fractalidad no sólo se manifiesta en imágenes o en formas visibles. La música, por ejemplo, se juzga transculturalmente más “musical” cuanto mayor es su dimensión fractal. Los patrones de asentamiento, asimismo, han demostrado ser tanto más fractales cuanto mayor es su auto-organización y más débil su planeamiento previo.

La dimensión fractal y otras medidas complejas equivalentes (lagunaridad, multifractalidad, etc) permiten comprender mejor muchos de los problemas de escala que afectan a las diversas ciencias, tales como la autocorrelación espacial, la falacia ecológica, el dilema de la unidad areal variable y los efectos de límite.

Esta presentación clarifica los usos del cálculo de la dimensión fractal en diversas disciplinas y examina las herramientas existentes para su cálculo. Se incluyen asimismo referencias al análisis espectral y al análisis basado en wavelets, una de las últimas revoluciones en la transdisciplinariedad compleja.

Using recent advances in time-varying spectral methods, this research analyses the growth cycles of productivity for three economies: that of the euro area, the US and the UK. The methodology uses a discrete wavelet transform (MODWT), a continuous wavelet transform (CWT) and also Hilbert wavelet pairs in order to analyze bivariate time series in terms of conventional frequency domain measures from spectral analysis. The …ndings are that there appears to be cycles at various frequencies, apart from the usual longer cycle that the economics literature focuses on, and there is apparently little signi…cant correlation or coherence between productivity cycles between the three, signifying that country-idiosyncratic factors are much more likely to drive productivity than international factors.

In this paper, we propose to extract the texture features of currency note images. To extract the features, first the Discrete Wavelet Transform (DWT) in particular Daubechies 1 (DB1) is utilized on a currency note and the approximate coefficient matrix of the transformed image is obtained. A set of coefficient statistical moments are then extracted from the approximate efficient matrix. The extracted features are stored in a feature vector. The extracted features can be used for recognition, classification and retrieval of currency notes.

Principal component analysis (PCA) is an orthogonal transformation that seeks the directions of maximum variance in the data and is commonly used to reduce the dimensionality of the data. In image denoising, a compromise has to be found between noise reduction and preserving significant image details. PCA is a statistical technique for simplifying a dataset by reducing datasets to lower dimensions. It is a standard technique commonly used for data reduction in statistical pattern recognition and signal processing. This paper proposes a denoising technique by using a new statistical approach, principal component analysis with local pixel grouping
(LPG). This procedure is iterated second time to further improve the denoising performance, and the noise level is adaptively adjusted in the second stage.

Compressing an image is significantly different than compressing raw binary data. compressing images is used by this different compression algorithm. Wavelet transforms used in Image compression methods to provide high compression rates while maintaining good image quality. Discrete Wavelet Transform (DWT) is one of the most common methods used in signal and image compression .It is very powerful compared to other transform because its ability to represent any type of signals both in time and frequency domain simultaneously. In this paper, we will moot the use of Wavelet Based Image compression algorithm- Embedded Zerotree Wavelet (EZW). We will obtain a bit stream with increasing accuracy from ezw algorithm because of basing on progressive encoding to compress an image into . All the numerical results were done by using matlab coding and the numerical analysis of this algorithm is carried out by sizing Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR) for standard Lena Image .Experimental results beam that the method is fast, robust and efficient enough to implement it in still and complex images with significant image compression.

The detection and localisation of partial discharge (PD) activities is gaining importance in maintaining operational transformers and diagnosing those which are faulty. Ultra-high-frequency (UHF) techniques have been applied successfully to detection of PD activities in HV transformers because of their sensitivity to fast transient pulses. When an UHF PD monitoring system is applied, one of the crucial pieces of information