Data Tracking Quadrature Condition Using Joint Histogram Method

1. Case Report Data Tracking Quadrature Condition Using Joint Histogram Method Arash Kalami* Department of Electrical Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran ABSTRACT Address for Correspondence To use the characteristic points of the image is an excellent choice for accurate monitoring data. The main disadvantages are that the properties of the monitoring body easily affected by noise and the data must often large enough to facilitate the monitoring to be dimensioned. In contrast, the facial features Gabor jet features are better than other types of functions of mother wavelets. This paper shows a novel filter for Joint Histogram Method. Department of Electrical Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran. E-mail: Ar.kalami@gmail.com Keywords: Joint histogram method, Monitoring, Mother wavelets, Noise. INTRODUCTION Dense facilitates movement achieved when the images geometrically aligned measure. This is a desirable feature for many applications. wavelets are formed with a minimum size of the beam introduced by a technique similar to the design of the Daubechies wavelets. For example, the coefficients of the filter of perfect reconstruction spline with compact support for symmetric and asymmetric bi- orthogonal wavelet wavelets. Lifting is a modification of reconstruction filter is used to improve the properties of the wavelet2. Bi-orthogonal wavelet bases with compact support can be built fairly finite impulse response filter, which the hare biorthogonal filter of the hand, said to be two-fold. identifies all filters of compact support, are dual. Filter with finite support4-5. A filter with finite support and duplicate only when there is a finite filter that this theory is that when the information to detect the data. E 'possible smooth bi-orthogonal wavelet has compact support, the1-3 is either symmetric or asymmetric to construct. An important constraint is that the estimation of the optical flow is subject to the action of the opening and does not allow for large movements in general. Symmetric or asymmetric wavelets are synthesized with linear phase perfect reconstruction filter2-4. A new generalized divergence measure is proposed. We show the convexity of the divergent action, passes its maximum value, and to analyze their upper limits in respect of the Bayes-error in statistical pattern recognition. For the deviation basis a new method is presented to image registration problem5. This is indicated by the deviation, the statistical dependence successive image, and the maximum is Bi-orthogonal elementary perfect between American Journal of Computer Science and Engineering Surveywww.pubicon.co.in

2. Kalami____________________________________________________ ISSN 2349 –7238 filter biorthogonal obtained by the inverse Fourier transform and the new filters are increased, because the use in order to improve their properties. Family of scaling functions and biorthogonal wavelet is defined by six biorthogonal wavelet bases3. The lifting support typically increases the size of the length of the carrier. Method for designing filters calculates the minimum size specific properties achieved. Pay attention that the number of disappearance for zeros can be distinguished. The coefficients of the moments are computed often produce increased by a transfer function with zeros. METHODS AND MATERIALS mirror, you can verify that this family is an orthonormal basis. These carriers are discrete close to a uniform sampling of continuous-time scaling wavelets. If the number of successive increases, we can verify that convergent. We therefore refer to as the discrete wavelet basis. Fast discrete wavelet transform signal decomposed into low-pass and high-pass component sub-sampled, transform performs the reconstruction. Study of the classical multiage filter bank was a major problem signal when it was discovered that it is possible to perform such decompositions and reconstructions with quadrature mirror filters. However, beyond the simple hair-filter does not filter quadrature mirror a finite impulse response. RESULTS AND DISCUSSION functions and the inverse Representation orthogonal wavelet coefficients of wavelet scales and the convergence remaining on the large scale. It is calculated by iteration numerical example is calculated using the Daubechies filter. The theory has been supplemented by biorthogonal filter mirror. We follow this approach in digital signal processing, which is a simple understanding of the perfect reconstruction filter banks. A double filter bank multi-rate channel bends a signal of a low-pass filter with a high pass filter and sub-samples at the output. A fast wavelet transformation is calculated with a cascade of filters with the hand, followed by a sub sampling factor of 2. The decomposition of a discrete signal in conjugate mirror filters can be interpreted as an extension of a base, a family resulting orthogonal basis. The original signal is recovered from this by an iteration wavelet representation. The conjugate mirror filters are often used in filter banks having a plurality of levels of filtering and sub- sampling cascade. It is therefore necessary to understand the behavior of such a cascade. In a tree wavelet filter bank output of the low-pass filter or filter sub-conjugate is composed of The amplification factor, the reverse of the disassembly and reconstruction filter and a reverse shift. Setting the internal relationship between the time domain is generally biorthogonal banks, since the two pairs of filter paper are stored and vice versa balanced filter. The condition simplifies the biorthogonal. If we impose that the filter decomposition equal to the reconstruction filter is exactly the state of the conjugate mirror filter according to the equation shows, with some additional conditions and are the Fourier transforms of functions of finite energy. The two families of wavelet bases are biorthogonal. Using orthogonality has two possible decompositions in these bases, Data stability implies that there is, so that the wavelet bases biorthogonal be connected to the approximations. The family is a Data basis of the space generated during a Data are. Both the same size as the wavelet media, showing the number of vanishing moments. They depend on the number of zero to the conclusion that the evacuation time is only an order of zero, if that happens, that two the equations of reconstruction multi-resolution AJCSES[2][2][2014]087-090

3. Kalami____________________________________________________ ISSN 2349 –7238 resolutions and many are approximate. extended and perpendicular decomposition and reconstruction is that it is not orthogonal but not holder, the number of Vanishing moments, regularity controlled Biorthogonal with the correct design of a hand. Similarly to the case of the orthogonal wavelets can be shown that is different from zero, each on the same carrier. note that the support and time. disappeared moments when, and only one zero. On the other hand, the softness and the sequence of zeros can be correlated. This is intuitively sense of the number of zeros, the more smooth. Figure 1 shows the inverse fast wavelet transformation to reconstruct a progressive filtering each other by inserting zeros between the samples. Moments of escape. Increases, that the moments of the flight, in the same way, the regular. Filters for perfect reconstruction biorthogonal splice wavelets with compact support, vanishing moments. To produce small wavelet coefficients in regular regions, we need calculate the scalar products with wavelet with the highest number of vanishing moments. The reconstructions are then carried out smoothly with the other wavelet, generally more. CONCLUSION flow, Gabor jets, etc. The color feature of the image is utilized to control the image movement in the rotation process of system. The color can easily provide information on the location of the data, but cannot encode the structural knowledge of the data. It is suitable for the collection of data, but is not suitable for the monitoring of data when motion is important. A range of optical flow is assumed for the monitoring of data. REFERENCES 1.R. Luo and M. Kay, Data fusion and sensor integration: state of the art in 1990s, Data fusion in robotics and machine intelligence, M. Abidi and R. Gonzalez Eds., Academic Press, San Diego, 2012. 2.P.J. Burt and R. J. Kolczynski, “Enhanced image capture through fusion,” Proc. 4th Intl Conference on Computer Vision, pp. 173– 182, 2003. 3.Offurum J.C. , Chukwuma F.O., Iheme C., Production and Blending of Sodium Based Water-Resistant Lubricating Greases from Petroleum and Petrochemical By–Products, AJCSES, Vol. 2 Issue 1, 2014 4.Sedghi T., “A Fast and Effective Model for cyclic Analysis and its application in classification” Arabian Journal for Science and Engineering Vol. 38 October 2012. 5.Manisha Malhotra, Comparative Analysis Scheduling Algorithms in Cloud Computing, AJCSES, Vol. 1, Issue 1, 2013. and symmetry Aarti Sing, Resource A of To monitor the facial types used by properties of the image. These features include the color, edges, landmarks, optical AJCSES[2][2][2014]087-090

4. Kalami____________________________________________________ ISSN 2349 –7238 Figure 1. Fast inverse wavelet transform reconstructs progressively each a by inserting zeros between samples of filtering AJCSES[2][2][2014]087-090