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\title{Text Attribute Noise Variation based Multi-Scale Image Analysis}
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\begin{document}

             \author[1]{S.Bhargav  Kumar}

             \author[2]{Dr.  M.Ashok}

             \author[3]{Dr.T.Bhaskara  Reddy}

             \author[4]{S.Bhargav  Kumar}

             \affil[1]{  Sridevi Womens Engineering College, JNTUH, Hyderabad, Telangana.}

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\date{\small \em Received: 12 December 2014 Accepted: 2 January 2015 Published: 15 January 2015}

\maketitle


\begin{abstract}
        


For image reconstruction, the particular constant quantity of the received image should be same as original image with the given analysis. This paper implements an analysis algorithm, where the particular constant quantity are analysed via image texture leaning with an appropriate variable variation's. In this paper, a three level decomposed multi-wavelet (3LMW)-based multi-scale image noise variation analysis scheme for image text attribute noise variation (TANV) and image analysis algorithm is proposed and the determination of the optimal 3LMW basis with respect to the proposed scheme is also discussed. The proposed method is applied to image noise variation analysis, and the experimental results validated its generality and effectiveness in multi-style image noise variation analysis.

\end{abstract}


\keywords{quantity; TANV; 3LMW; resolution; multi-style.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
In this paper, a generic model to solve these multi-style \hyperref[b6]{[7]} image TANV analysis problems has been proposed. The pair of book keeping aim to characterize the two domains, multi-scale \hyperref[b2]{[3]} and semi-quad \hyperref[b5]{[6]}, the mapping functions is to reveal the relation between two variable variation's \hyperref[b3]{[4]} \hyperref[b4]{[5]} for noise variation analysis. The proposed model is called as auto-coupled image noise variation analysis and apply it to image noise variation analysis to validate its performance.\par
The rest of the paper is organized as follows. Section 2 discusses about Multi-Scale Image Text Attribute Noise Variation (MSTANV) scheme. Analysis Model is presented in section 3. Section 4 presents the proposed model Multi-Scale Image Analysis method. Section 5 discuss the results and Section 6 concludes the paper. n many image recognition applications, people often send images from different sources and consequently they were received at different destinations. In addition, low resolution obtained at multiple receivers should be up-converted to a higher level of resolution for better interpretation at end user. Research works on such image analysis problems should benefit the practical applications under image interpretation and image human visual distinctive information analysis \hyperref[b0]{[1]} \hyperref[b1]{[2]}. 
\section[{I}]{I}\par
The MSTANV scheme presented in this work adopts partitioned and relevant (P\&R) 3LMW stretch (P\&R3L) and two-stage decomposition structure is implemented. Here w H jx , w V jy and w D jz are the 3LMW particular constant quantity at horizontal, vertical and diagonal particular constant quantity.\par
Let S xyz denote input image to analyse. Filters H jx , H jy and H jz used in P\&R3L are replaced with (2 ? ixyz = ? xyz ? ixyz\textbf{(5)}\par
where ? ixyz denotes a P\&R operator,? xyz =(? ? ixyz Txyz ? ixyz ) -xyz (? ? ixyz Txyz ? ixyz )\textbf{(6)}\par
For mapping ? xyz ,? ixyz = ? xyz ? ixyz\textbf{(7)}\par
Substituting ( \hyperref[formula_1]{6}) into \hyperref[b6]{(7)},? xyz =? xyz ? xyz =(? ? ixyz Txyz ? ixyz ) -xyz (? ? ixyz Txyz ? xyz ? ixyz ) (8)\par
where ?? K k=xyz ? i?Ck:xyz |? xyz ? ixyz -? xyz ? kxyz | xyz xyz (11) resulting |? xyz ? ixyz -? xyz ? kxyz | xyz xyz = |? ixyz -? kxyz | xyz xyz .\par
From these, by re-writing the (11) as,\par
By substituting norm in (12), resulting in   At reconstruction stage, the coefficients are analysed in to their original styles by the same dictionary mapping or book keeping.J(? k:yz,l:yz , ? k:yz , ? l:yz , ? k:yz,l:yz )= ? N k=1:yz ? M l=1:yz | Î?" P k:yz || Î?" Q l:yz |(? k,l:yz -Ä?" k,l:yz ) yz + ? N k:yz=1 ? k:yz (? M l=1:yz | Î?" Q l:yz |? k:yz,l:yz -yz)+ ? M l=1:yz ? l:yz (? N k=1:yz | Î?" P K:yz |? k:yz,l:yz -yz)-? N k:yz=1:yz ? M l=1:yz ? k:yz,l:yz ? k:yz,l:yz (15),\textbf{and}J(? k:xz,l:xz , ? k:xz , ? l:xz , ? k:xz,l:xz )= ? N k=1:xz ? M l=1:xz | Î?" P k:xz || Î?" Q l:xz |(? k,l:xz -Ä?" k,l:xz ) xz + ? N k:xz=1 ? k:xz (? M l=1:xz | Î?" Q l: 
\section[{V. Experiment Results and discussions}]{V. Experiment Results and discussions}\par
As stated in above chapters, the TANV performance increases in variable variation's-information CI i:xyz of original signal S i:xyz and noisy quantity w i:xyz , related as CI i:xyz =I(S i:xyz +w i:xyz ±? :xyz ), but decreases in noise error criteria CN i:xyz . Therefore, good 3LMW basis for TANV should aim at maximizing CI i:xyz and minimizing CN i:xyz is implemented in this research work. Denoting P\&R 3LMW as P\&R3L (n :xyz ),where n=1,2,3,....,N and bi-P\&R 3LMW is denoted by CDF(n :xyz ,n' :xyz ), where n is analytic 3LMW and n' is analyzed 3LMW.\par
Proposed method has been implemented on nine 256 X 256 images Barbara, Boats, Butterfly, Cameraman, House, Straw, Lena, Baboon and Peppers as shown in fig 2, to compute their CI i:xyz and CN i:xyz values with respect to wavelets CDF(3,3) and P\&R3L (4). In table 2 and table 3, listed the values of CI i:xyz and CN i:xyz when j :xyz =2 N-1 -1 and j :xyz = j :xyz =2 N-1 -2. These results represent the information of the first three 3LMW scales indication H, V and D as horizontal, vertical and diagonal subbands respectively.\par
From the experimental results tabulated in table  The PSNR results on a set of 9 images are reported in Table  {\ref 5}. From Table  {\ref 5}, clearly shows the proposed TANV method significantly outperforms for both uniform blurring and Gaussian blurring. 
\section[{VI.}]{VI.} 
\section[{Conclusions}]{Conclusions}\par
In this paper, an image analysis algorithm has been introduced to improve the effectiveness of quality for images. In this paper, also presented a MSTANV scheme with a P\&R3L 3LMW interscale model, which improved the signal estimation under noisy environment. Experimental results on image analysis demonstrated that the analysis approach can significantly outperform other leading image analysis methods. Finally, image analysis modelling techniques were employed to separate 3LMW particular constant quantity. The spatial classification of 3LMW pixels reduces the analysis estimation error and subsequently improving the TANV performance. 
\section[{Global Journal of C omp uter S cience and T echnology}]{Global Journal of C omp uter S cience and T echnology}\par
Volume XV Issue II Version I Year ( )\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}?}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
Analysis Model\\
j-xyz -\\
xyz) zeros of the variable quantity of original filter H o:xyz ,\\
so does for H o:xyz ||V jxyz . The analysed signal by\\
proposed 3LMW, is an average of several MSTANV\\
signal by P\&R3L. Noise variations of s j at scale j in a\\
direction is\end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.7097643097643097\textwidth}P{0.12878787878787878\textwidth}P{0.011447811447811446\textwidth}}
(? xyz ,? xyz )=arg ?xyz,?k min [1/xyz|X xyz -? xyz ? xyz | xyz xyz + ? xyz 1 |? xyz | xyz ]+? xyz 2 ? K\tabcellsep k=xyz ? i?Ck:xyz |? xyz ? ixyz -? k | xyz xyz\tabcellsep (10)\\
\multicolumn{2}{l}{where ? k stands for k xyz -th C k:xyz of particular constant quantity ? xyz . By rewriting the (10) as,}\tabcellsep \\
(? xyz ,? xyz )=arg ?xyz,?k min [1/xyz|X\tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: xyz -? xyz ? xyz | xyz xyz + ? xyz 1 |? xyz | xyz ]+? xyz 2]} 
\caption{\label{tab_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.6982142857142857\textwidth}P{0.04203296703296703\textwidth}P{0.10975274725274727\textwidth}}
\multicolumn{2}{l}{Text Attribute Noise Variation based Multi-Scale Image Analysis}\\
IV.\tabcellsep Multi-Scale Image Analysis Algorithm\\
\tabcellsep \tabcellsep 2015\\
\tabcellsep \tabcellsep Year\\
\tabcellsep \tabcellsep Volume XV Issue II Version I\\
\tabcellsep \tabcellsep ( ) F\\
\multicolumn{2}{l}{xz |? k:xz,l:xz -xz)+ where ? k:xyz,l:xyz ,? k:xyz , ? l:xyz and ? k:xyz,l:xyz are the index constraints. In the scenario of image analysis, re-writing the (13), ? M l=1:xz ? l:xz (? N k=1:xz | Î?" P K:xz |? k:xz,l:xz -xz)-? N k:xz=1:xz ? M l=1:xz ? k:xz,l:xz ? k:xz,l:xz (16) we get by considering ? initial values, (? xyz ,? xyz )=arg ?xyz,?k min [1/xyz|X xyz -? xyz ?| xyz xyz + ? xyz 1 |? xyz | xyz ]+? xyz 2 ? K k=1: xyz ? xyz xyz ) + norm(? xyz n1 |? xyz | xyz ])+norm(? xyz n2 ? K k=xyz ? i?Ck:xyz |? ixyz -? kxyz | xyz xyz )}\tabcellsep Global Journal of C omp uter S cience and T echnology\end{longtable} \par
  {\small\itshape [Note: i?Ck: xyz |? xyz ? i: xyz -? xyz ? k: xyz | 1: xyz (17) and ? y: xyz =arg ?: xyz min |? xyz | 1: xyz , by stating |S xyz -n xyz ? xy:yz:zx ? xyz | xyz <? xyz (18) and then the reconstructed S is obtained as S j: xyz =? xy:yz:zx ? y: xyz which is very close to the true image S xyz . (? xyz ,? xyz )=arg ?xyz,?k min [1/xyz|X xyz -? xyz ? xyz | xyz xyz + ? xyz 1 |? xyz | xyz ]+? xyz 2 ? K k=xyz ? i?Ck:xyz |? ixyz -? kxyz | xyz xyz (? xyz ,? xyz )=norm( arg ?xyz,?k ?min [1/xyz|X xyz -? xyz ? xyz |]} 
\caption{\label{tab_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{II} \par 
\begin{longtable}{P{0.06293617021276596\textwidth}P{0.0991063829787234\textwidth}P{0.102\textwidth}P{0.07161702127659574\textwidth}P{0.056425531914893606\textwidth}P{0.07451063829787234\textwidth}P{0.056425531914893606\textwidth}P{0.07451063829787234\textwidth}P{0.056425531914893606\textwidth}P{0.07161702127659574\textwidth}P{0.056425531914893606\textwidth}P{0.068\textwidth}}
\tabcellsep \tabcellsep \multicolumn{7}{l}{Text Attribute Noise Variation based Multi-Scale Image Analysis}\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{2}{l}{(a) Barbara}\tabcellsep (b) Boats\tabcellsep \tabcellsep \multicolumn{2}{l}{(c) Butterfly}\tabcellsep \multicolumn{2}{l}{(d) Cameraman}\tabcellsep (e) House\tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{2}{l}{CDF(3,3) P\&R3L}\tabcellsep \multicolumn{2}{l}{CDF(3,3) P\&R3L}\tabcellsep \multicolumn{2}{l}{CDF(3,3) P\&R3L}\tabcellsep \multicolumn{2}{l}{CDF(3,3) P\&R3L}\tabcellsep \multicolumn{2}{l}{CDF(3,3) P\&R3L}\tabcellsep \\
\tabcellsep \tabcellsep (4)\tabcellsep \tabcellsep (4)\tabcellsep \tabcellsep (4)\tabcellsep \tabcellsep (4)\tabcellsep \tabcellsep (4)\tabcellsep \\
CIj :xyz\tabcellsep xy 0.3456\tabcellsep 0.8125\tabcellsep 0.8125\tabcellsep 1.1010\tabcellsep 0.3250\tabcellsep 0.7522\tabcellsep 0.6525\tabcellsep 1.1526\tabcellsep 0.8526\tabcellsep 0.8152\tabcellsep \\
=2 N-1 -1,\tabcellsep yz 0.2256\tabcellsep 0.3251\tabcellsep 1.1256\tabcellsep 1.4521\tabcellsep 0.3010\tabcellsep 0.5261\tabcellsep 0.4456\tabcellsep 0.7522\tabcellsep 1.1256\tabcellsep 0.3956\tabcellsep \\
N=3\tabcellsep zx 0.1859\tabcellsep 0.3125\tabcellsep 0.7852\tabcellsep 0.8521\tabcellsep 0.1215\tabcellsep 0.2121\tabcellsep 0.1526\tabcellsep 0.2901\tabcellsep 0.7256\tabcellsep 0.2615\tabcellsep \\
CIj :xyz\tabcellsep xy 1.4256\tabcellsep 2.6521\tabcellsep 1.3689\tabcellsep 2.0562\tabcellsep 1.2568\tabcellsep 2.4512\tabcellsep 1.7582\tabcellsep 2.2561\tabcellsep 1.3156\tabcellsep 2.6952\tabcellsep \\
=2 N-1 -2,\tabcellsep yz 0.7612\tabcellsep 1.4589\tabcellsep 1.5164\tabcellsep 2.3215\tabcellsep 1.0785\tabcellsep 2.0658\tabcellsep 1.2156\tabcellsep 2.1325\tabcellsep 1.5262\tabcellsep 1.4256\tabcellsep \\
N=5\tabcellsep zx 0.7528\tabcellsep 1.2156\tabcellsep 1.2001\tabcellsep 1.3596\tabcellsep 0.5261\tabcellsep 1.1026\tabcellsep 0.7262\tabcellsep 1.1952\tabcellsep 1.1062\tabcellsep 1.2935\tabcellsep \\
CNj :xyz =2 N-1 -1,\tabcellsep xy 0.1305 yz 0.1459\tabcellsep 0.1256 0.2121\tabcellsep 0.0123 0.0126\tabcellsep 0.0156 0.0326\tabcellsep 0.0852 0.0758\tabcellsep 0.1023 0.1256\tabcellsep 0.2012 0.1652\tabcellsep 0.2859 0.1900\tabcellsep 0.0126 0.0159\tabcellsep 0.1212 0.1956\tabcellsep 2015\\
N=3 CNj :xyz =2 N-1 -2,\tabcellsep zx 0.2356 xy 0.0212 yz 0.0380\tabcellsep 0.2456 0.0415 0.1001\tabcellsep 0.0356 0.0070 0.0108\tabcellsep 0.0358 0.0121 0.0182\tabcellsep 0.1102 0.0162 0.0251\tabcellsep 0.1126 0.0325 0.0568\tabcellsep 0.2650 0.0725 0.0548\tabcellsep 0.2156 0.1521 0.0698\tabcellsep 0.0356 0.0069 0.0025\tabcellsep 0.1822 0.0485 0.0910\tabcellsep Year\\
N=5\tabcellsep zx 0.0592\tabcellsep 0.1011\tabcellsep 0.0123\tabcellsep 0.0121\tabcellsep 0.0261\tabcellsep 0.0589\tabcellsep 0.1025\tabcellsep 0.1985\tabcellsep 0.0056\tabcellsep 0.1023\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Volume XV Issue II Version I\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep ( ) F\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Global Journal of C omp uter S cience and T echnology\end{longtable} \par
 
\caption{\label{tab_3}Table II :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{III} \par 
\begin{longtable}{P{0.07587844254510921\textwidth}P{0.07507122507122507\textwidth}P{0.20261158594491926\textwidth}P{0.03148148148148148\textwidth}P{0.2147198480531814\textwidth}P{0.03148148148148148\textwidth}P{0.03148148148148148\textwidth}P{0.03148148148148148\textwidth}P{0.12431149097815765\textwidth}P{0.03148148148148148\textwidth}}
\tabcellsep \tabcellsep \multicolumn{7}{l}{Text Attribute Noise Variation based Multi-Scale Image Analysis}\tabcellsep \\
2015\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Year\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Volume XV Issue II Version I\tabcellsep CIj :xyz =2 N-1 -1, N=3 CIj :xyz =2 N-1 -2, N=5\tabcellsep \multicolumn{2}{l}{(f) Straw CDF(3,3) P\&R3L (4) xy 0.8952 1.1123 yz 1.1256 1.4859 zx 0.7819 0.8125 xy 1.3826 2.0589 yz 1.5246 2.3596 zx 1.1256 1.3589}\tabcellsep \multicolumn{4}{l}{(g) Lena CDF(3,3) P\&R3L (4) CDF(3,3) P\&R3L (h) Baboon (4) 0.3592 0.8215 0.6528 1.2356 0.2012 0.3056 0.4582 0.7856 0.1856 0.2589 0.2458 0.2985 1.4002 2.6589 1.7515 2.8596 0.7852 1.4512 1.2659 2.1256 0.7528 1.2659 0.7526 1.2689}\tabcellsep \multicolumn{2}{l}{(i) Peppers CDF(3,3) P\&R3L (4) 0.3056 0.7584 0.3025 0.5892 0.1265 0.2010 1.2689 2.4586 1.0789 2.0456 0.5286 1.1564}\\
( ) F\tabcellsep CNj :xyz =2 N-1 -1, N=3\tabcellsep xy 0.0125 yz 0.0192\tabcellsep 0.0185 0.0356\tabcellsep 0.1165 0.1489\tabcellsep 0.1456 0.2158\tabcellsep 0.1205 0.1658\tabcellsep 0.2456 0.1986\tabcellsep 0.0826 0.0784\tabcellsep 0.1025 0.1035\\
\tabcellsep \tabcellsep zx 0.0356\tabcellsep 0.0356\tabcellsep 0.2121\tabcellsep 0.1589\tabcellsep 0.3256\tabcellsep 0.2256\tabcellsep 0.1156\tabcellsep 0.1029\\
Global Journal of C omp uter S cience and T echnology\tabcellsep CNj :xyz =2 N-1 -2, N=5\tabcellsep xy 0.0072 yz 0.0105 zx 0.0123\tabcellsep 0.0125 0.0182 0.0201\tabcellsep 0.0256 0.0356 0.0589\tabcellsep 0.0452 0.0956 0.1105\tabcellsep 0.0756 0.0456 0.1005\tabcellsep 0.1456 0.0956 0.1986\tabcellsep 0.0125 0.0256 0.0214\tabcellsep 0.0356 0.0589 0.0592\end{longtable} \par
 
\caption{\label{tab_4}Table III :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{IV} \par 
\begin{longtable}{P{0.05916193181818181\textwidth}P{0.06761363636363636\textwidth}P{0.12013494318181817\textwidth}P{0.2155184659090909\textwidth}P{0.3815340909090909\textwidth}P{0.0012073863636363636\textwidth}P{0.0012073863636363636\textwidth}P{0.0012073863636363636\textwidth}P{0.0012073863636363636\textwidth}P{0.0012073863636363636\textwidth}}
\tabcellsep \multicolumn{2}{l}{? :xyz 5}\tabcellsep 10\tabcellsep 15\tabcellsep 20\tabcellsep 25\tabcellsep 30\tabcellsep 35\tabcellsep 40\\
(a) Barbara\tabcellsep ? xy\tabcellsep 5.45\tabcellsep 10.2\tabcellsep \multicolumn{5}{l}{14.56 19.56 24.56 29.56 34.56 40.41}\\
\tabcellsep ? yz\tabcellsep \multicolumn{2}{l}{2015 6.89}\tabcellsep \multicolumn{5}{l}{12.56 17.25 23.25 28.26 33.25 40.11}\\
\tabcellsep ? zx\tabcellsep 2.10\tabcellsep 7.56\tabcellsep \multicolumn{5}{l}{13.56 18.56 23.26 29.21 34.56 40.46}\\
(b) Boats\tabcellsep ? xy\tabcellsep 5.68\tabcellsep 9.56\tabcellsep \multicolumn{5}{l}{13.25 19.25 23.56 27.89 33.25 40.11}\\
\tabcellsep ? yz\tabcellsep 6.58\tabcellsep \multicolumn{6}{l}{11.26 15.96 20.25 25.26 30.25 34.56 40.74}\\
\tabcellsep ? zx\tabcellsep 3.96\tabcellsep 7.85\tabcellsep \multicolumn{5}{l}{13.58 18.69 24.56 29.86 34.58 40.70}\\
(c) Butterfly\tabcellsep ? xy\tabcellsep 3.12\tabcellsep 7.25\tabcellsep \multicolumn{5}{l}{12.25 18.25 23.56 28.26 33.25 40.02}\\
\tabcellsep ? yz\tabcellsep 6.58\tabcellsep \multicolumn{6}{l}{10.25 14.58 20.15 24.58 28.69 34.25 40.51}\\
\tabcellsep ? zx\tabcellsep 3.56\tabcellsep 8.25\tabcellsep \multicolumn{5}{l}{13.58 19.58 24.56 29.56 34.56 40.65}\\
\multicolumn{2}{l}{(d) Cameraman ? xy}\tabcellsep 6.58\tabcellsep \multicolumn{6}{l}{10.25 14.15 19.25 23.15 29.25 33.25 39.56}\\
\tabcellsep ? yz\tabcellsep 7.59\tabcellsep \multicolumn{6}{l}{11.58 15.15 20.25 24.56 30.25 34.25 40.15}\\
\tabcellsep ? zx\tabcellsep 3.58\tabcellsep 8.59\tabcellsep \multicolumn{5}{l}{14.56 19.58 24.56 29.58 34.58 40.85}\\
(e) House\tabcellsep ? xy\tabcellsep 6.01\tabcellsep 7.89\tabcellsep \multicolumn{5}{l}{11.25 16.25 22.12 27.56 33.15 40.12}\\
\tabcellsep ? yz\tabcellsep \multicolumn{7}{l}{13.25 17.25 21.26 25.25 29.58 34.38 38.59 43.25}\\
\tabcellsep ? zx\tabcellsep 6.25\tabcellsep 8.96\tabcellsep \multicolumn{5}{l}{12.65 17.22 23.26 28.56 34.56 40.25}\\
(f) Straw\tabcellsep ? xy\tabcellsep 3.22\tabcellsep 6.25\tabcellsep \multicolumn{5}{l}{12.56 17.25 23.15 28.15 33.56 40.12}\\
\tabcellsep ? yz\tabcellsep 6.89\tabcellsep \multicolumn{6}{l}{11.25 15.56 20.14 25.26 30.22 34.25 40.68}\\
\tabcellsep ? zx\tabcellsep 3.25\tabcellsep 7.85\tabcellsep \multicolumn{5}{l}{13.56 18.25 24.56 29.56 34.89 40.52}\\
(g) Lena\tabcellsep ? xy\tabcellsep 6.25\tabcellsep \multicolumn{6}{l}{10.25 15.24 20.25 24.56 29.25 34.59 40.12}\\
\tabcellsep ? yz\tabcellsep 3.26\tabcellsep 6.59\tabcellsep \multicolumn{5}{l}{12.56 17.56 23.45 28.56 33.56 40.52}\\
\tabcellsep ? zx\tabcellsep 3.22\tabcellsep 8.59\tabcellsep \multicolumn{5}{l}{13.58 19.28 24.62 29.52 34.56 39.25}\\
(h) Baboon\tabcellsep ? xy\tabcellsep 7.59\tabcellsep \multicolumn{6}{l}{11.25 16.59 20.26 25.25 30.26 35.49 40.12}\\
\tabcellsep ? yz\tabcellsep 3.22\tabcellsep 6.56\tabcellsep \multicolumn{5}{l}{12.25 17.25 23.56 28.25 33.26 40.12}\\
\tabcellsep ? zx\tabcellsep 3.56\tabcellsep 8.59\tabcellsep \multicolumn{5}{l}{14.25 19.56 24.56 29.58 34.56 40.12}\\
(i) Peppers\tabcellsep ? xy\tabcellsep 3.22\tabcellsep 6.25\tabcellsep \multicolumn{5}{l}{12.15 17.25 23.26 28.22 33.26 40.33}\\
\tabcellsep ? yz\tabcellsep \multicolumn{7}{l}{13.59 17.16 21.25 25.65 29.25 34.25 38.59 43.25}\\
\tabcellsep ? zx\tabcellsep 6.59\tabcellsep 8.96\tabcellsep \multicolumn{5}{l}{12.56 17.56 23.65 28.65 34.56 40.25}\end{longtable} \par
 
\caption{\label{tab_5}Table IV :}\end{figure}
 			\footnote{© 2015 Global Journals Inc. (US) 1} 			\footnote{© 2015 Global Journals Inc. (US)} 		 		\backmatter  			 \par
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