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		<title><![CDATA[کلیدستان - پردازش تصویر در متلب (MATLAB)]]></title>
		<link>https://www.kelidestan.com/forum/</link>
		<description><![CDATA[کلیدستان - https://www.kelidestan.com/forum]]></description>
		<pubDate>Wed, 28 Oct 2026 17:33:24 +0000</pubDate>
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			<title><![CDATA[آزمون همبستگی جفت پیکسل های تصویر در جهت افقی عمودی و مورب]]></title>
			<link>https://www.kelidestan.com/forum/showthread.php?tid=20726</link>
			<pubDate>Wed, 15 Feb 2023 15:54:16 +0000</pubDate>
			<dc:creator><![CDATA[<a href="https://www.kelidestan.com/forum/member.php?action=profile&uid=14087">miillad</a>]]></dc:creator>
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			<description><![CDATA[<span style="font-size: small;" class="mycode_size"><span style="font-family:IranSans, Yekan, icons,;">با عرض سلام .<br />
دوستان کسی نمونه <span style="color: #4e5863;" class="mycode_color">پروژه</span></span></span><span style="color: #333333;" class="mycode_color"><span style="font-size: small;" class="mycode_size"><span style="font-family:IranSans, Yekan, icons,;"> داره که با دادن یک تصویر ورودی، بشه توزیع همبستگی مثلا 4000 جفت پیکسل مجاور در جهت های افقی و عمودی و مورب مختلف بدست آورد ؟<br />
به زبان پایتون باشه بهتره، ولی متلب هم بود چاره ای نیست از همون استفاده میکنم<br />
<br />
</span></span></span>یه همچین چیزی مطابق عکس زیر باید بشه:<br />
<a href="https://www.noavarangermi.ir/forum/images/posts/correlation-distribution-image.png" target="_blank" rel="noopener" class="mycode_url">https://www.noavarangermi.ir/forum/image...-image.png</a><br />
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اینم فرمول های معادله خط و همبستگی که ازاینا برای تست استفاده میشه:<br />
<br />
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" />]]></description>
			<content:encoded><![CDATA[<span style="font-size: small;" class="mycode_size"><span style="font-family:IranSans, Yekan, icons,;">با عرض سلام .<br />
دوستان کسی نمونه <span style="color: #4e5863;" class="mycode_color">پروژه</span></span></span><span style="color: #333333;" class="mycode_color"><span style="font-size: small;" class="mycode_size"><span style="font-family:IranSans, Yekan, icons,;"> داره که با دادن یک تصویر ورودی، بشه توزیع همبستگی مثلا 4000 جفت پیکسل مجاور در جهت های افقی و عمودی و مورب مختلف بدست آورد ؟<br />
به زبان پایتون باشه بهتره، ولی متلب هم بود چاره ای نیست از همون استفاده میکنم<br />
<br />
</span></span></span>یه همچین چیزی مطابق عکس زیر باید بشه:<br />
<a href="https://www.noavarangermi.ir/forum/images/posts/correlation-distribution-image.png" target="_blank" rel="noopener" class="mycode_url">https://www.noavarangermi.ir/forum/image...-image.png</a><br />
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اینم فرمول های معادله خط و همبستگی که ازاینا برای تست استفاده میشه:<br />
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<img 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" />]]></content:encoded>
		</item>
		<item>
			<title><![CDATA[مجاسبه فاصله بین دو نقطه  در تصاویر]]></title>
			<link>https://www.kelidestan.com/forum/showthread.php?tid=13694</link>
			<pubDate>Sun, 01 Jul 2018 16:33:28 +0100</pubDate>
			<dc:creator><![CDATA[<a href="https://www.kelidestan.com/forum/member.php?action=profile&uid=7092">javad_vali95</a>]]></dc:creator>
			<guid isPermaLink="false">https://www.kelidestan.com/forum/showthread.php?tid=13694</guid>
			<description><![CDATA[با عرض سلام .میخوام فاصله بین دونقطه رو محاسبه کنم   امکانش باشه کمک کنین .ممنون]]></description>
			<content:encoded><![CDATA[با عرض سلام .میخوام فاصله بین دونقطه رو محاسبه کنم   امکانش باشه کمک کنین .ممنون]]></content:encoded>
		</item>
		<item>
			<title><![CDATA[پیدا کردن ابرو و دهان در چهره]]></title>
			<link>https://www.kelidestan.com/forum/showthread.php?tid=9037</link>
			<pubDate>Tue, 13 Dec 2016 13:00:18 +0000</pubDate>
			<dc:creator><![CDATA[<a href="https://www.kelidestan.com/forum/member.php?action=profile&uid=5016">viveka</a>]]></dc:creator>
			<guid isPermaLink="false">https://www.kelidestan.com/forum/showthread.php?tid=9037</guid>
			<description><![CDATA[با سلام<br />
من میخوام با روش cascadeobjectمتلب ابرو و دهان رو پیدا کنم.کسی میتونه راهنماییم کنه.<br />
ممنون میشم]]></description>
			<content:encoded><![CDATA[با سلام<br />
من میخوام با روش cascadeobjectمتلب ابرو و دهان رو پیدا کنم.کسی میتونه راهنماییم کنه.<br />
ممنون میشم]]></content:encoded>
		</item>
		<item>
			<title><![CDATA[ضرب 2تصویر]]></title>
			<link>https://www.kelidestan.com/forum/showthread.php?tid=8703</link>
			<pubDate>Sun, 09 Oct 2016 12:53:39 +0100</pubDate>
			<dc:creator><![CDATA[<a href="https://www.kelidestan.com/forum/member.php?action=profile&uid=4735">siavashmajd</a>]]></dc:creator>
			<guid isPermaLink="false">https://www.kelidestan.com/forum/showthread.php?tid=8703</guid>
			<description><![CDATA[سلام من میخواهم طوری 2تصویر را ضرب کنم که بعد اگر جواب را بر هرکدام تقسیم کنم نتیجه اولیه بدست بیاد دقیقا مثه ضرب وتقسیم در ریاضی ولی روی تصاویر<br />
لطفا کمک کنید؟]]></description>
			<content:encoded><![CDATA[سلام من میخواهم طوری 2تصویر را ضرب کنم که بعد اگر جواب را بر هرکدام تقسیم کنم نتیجه اولیه بدست بیاد دقیقا مثه ضرب وتقسیم در ریاضی ولی روی تصاویر<br />
لطفا کمک کنید؟]]></content:encoded>
		</item>
		<item>
			<title><![CDATA[رسم دایره برروی تصویر با استفاده از for]]></title>
			<link>https://www.kelidestan.com/forum/showthread.php?tid=8660</link>
			<pubDate>Fri, 30 Sep 2016 00:09:41 +0100</pubDate>
			<dc:creator><![CDATA[<a href="https://www.kelidestan.com/forum/member.php?action=profile&uid=4697">aliakbar</a>]]></dc:creator>
			<guid isPermaLink="false">https://www.kelidestan.com/forum/showthread.php?tid=8660</guid>
			<description><![CDATA[با عرض سلام و خسته نباشید خدمت بزرگواران<br />
یه سوالی هست که گیجم کرده<br />
سوال میگه که دور سر رو در تصویر پیشفرض cameraman.tif رو توسط یک حلقه for دایره رسم کن<br />
نمیدونم چطوری این کار باید انجام بشه<br />
متن سوالش اینه:<br />
<br />
<div style="text-align: left;" class="mycode_align">Write code that overlays a white circle on top of the cameraman’s head. Use “for” loops to do this</div>
<div style="text-align: right;" class="mycode_align">اصلا چطوری میشه با یه for یه دایره رسم کنیم</div>
<div style="text-align: right;" class="mycode_align">تصویر ما در سطح خاکستری قرار داره</div>
<div style="text-align: right;" class="mycode_align">خب اگه بتونیم تک تک پیکسلهای روی محیط دایره رو sweep کنیم میتونیم مقادیرشونو 255 قرار بدیم و بعد درست میشه<br />
اما چطوری sweep کردنشو نمیدونم <img src="https://www.kelidestan.com/forum/images/smilies/dodgy.gif" alt="Dodgy" title="Dodgy" class="smilie smilie_14" /> </div>]]></description>
			<content:encoded><![CDATA[با عرض سلام و خسته نباشید خدمت بزرگواران<br />
یه سوالی هست که گیجم کرده<br />
سوال میگه که دور سر رو در تصویر پیشفرض cameraman.tif رو توسط یک حلقه for دایره رسم کن<br />
نمیدونم چطوری این کار باید انجام بشه<br />
متن سوالش اینه:<br />
<br />
<div style="text-align: left;" class="mycode_align">Write code that overlays a white circle on top of the cameraman’s head. Use “for” loops to do this</div>
<div style="text-align: right;" class="mycode_align">اصلا چطوری میشه با یه for یه دایره رسم کنیم</div>
<div style="text-align: right;" class="mycode_align">تصویر ما در سطح خاکستری قرار داره</div>
<div style="text-align: right;" class="mycode_align">خب اگه بتونیم تک تک پیکسلهای روی محیط دایره رو sweep کنیم میتونیم مقادیرشونو 255 قرار بدیم و بعد درست میشه<br />
اما چطوری sweep کردنشو نمیدونم <img src="https://www.kelidestan.com/forum/images/smilies/dodgy.gif" alt="Dodgy" title="Dodgy" class="smilie smilie_14" /> </div>]]></content:encoded>
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			<title><![CDATA[تشخیص بین یک شی بزرگ و چند شی کوچک در تصویر (متلب - MATLAB)]]></title>
			<link>https://www.kelidestan.com/forum/showthread.php?tid=8437</link>
			<pubDate>Fri, 26 Aug 2016 08:40:23 +0100</pubDate>
			<dc:creator><![CDATA[<a href="https://www.kelidestan.com/forum/member.php?action=profile&uid=4532">مرتضی</a>]]></dc:creator>
			<guid isPermaLink="false">https://www.kelidestan.com/forum/showthread.php?tid=8437</guid>
			<description><![CDATA[سلام<br />
خسته نباشید<br />
<br />
<br />
میخوام در یک تصویر که یک شی بزرگ و چند شی کوچک هست تشخیص بدم شی بزرگ واقعن یک شی بزرگه یا تجمعیه از چند شی کوچیک؟؟؟<br />
<br />
میشه سریعتر راهنماییم کنید.<br />
من تازه با زبان متلب آشنا شدم.]]></description>
			<content:encoded><![CDATA[سلام<br />
خسته نباشید<br />
<br />
<br />
میخوام در یک تصویر که یک شی بزرگ و چند شی کوچک هست تشخیص بدم شی بزرگ واقعن یک شی بزرگه یا تجمعیه از چند شی کوچیک؟؟؟<br />
<br />
میشه سریعتر راهنماییم کنید.<br />
من تازه با زبان متلب آشنا شدم.]]></content:encoded>
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			<title><![CDATA[تشخیص دایره یا بیضی با یک اندازه معین در تصویر]]></title>
			<link>https://www.kelidestan.com/forum/showthread.php?tid=7994</link>
			<pubDate>Thu, 16 Jun 2016 08:40:02 +0100</pubDate>
			<dc:creator><![CDATA[<a href="https://www.kelidestan.com/forum/member.php?action=profile&uid=2382">almasm85</a>]]></dc:creator>
			<guid isPermaLink="false">https://www.kelidestan.com/forum/showthread.php?tid=7994</guid>
			<description><![CDATA[<span style="color: #000000;" class="mycode_color"><span style="font-size: small;" class="mycode_size"><span style="font-family:tahoma;">سلام به همگی <br />
من میخام در یک تصویر یک شی رو تشخیص بدم .<br />
این شی معمولا دایره یا بیضی هست.<br />
<br />
و رنگش هم تقریبا مشخص هست . یعنی بازه رنگش رو میدونم.<br />
همچنین اندازش هم معین هست . یعنی میتونم بگم مثلا تعدا پیکسلش بین x , y هست.<br />
<br />
البته در تصویر ممکن هست اشیایی با همان رنگ باشند ولی مثلا دایره نباشند و یا اندازشان فرق کند<br />
<br />
با این ویژگی های گفته شده .<br />
<br />
آسان ترین روش و البته دقیق که من بتوانم شی را تشخیص بدم . چیست ؟<br />
<br />
متشکرم از راهنمایی شما</span></span></span>]]></description>
			<content:encoded><![CDATA[<span style="color: #000000;" class="mycode_color"><span style="font-size: small;" class="mycode_size"><span style="font-family:tahoma;">سلام به همگی <br />
من میخام در یک تصویر یک شی رو تشخیص بدم .<br />
این شی معمولا دایره یا بیضی هست.<br />
<br />
و رنگش هم تقریبا مشخص هست . یعنی بازه رنگش رو میدونم.<br />
همچنین اندازش هم معین هست . یعنی میتونم بگم مثلا تعدا پیکسلش بین x , y هست.<br />
<br />
البته در تصویر ممکن هست اشیایی با همان رنگ باشند ولی مثلا دایره نباشند و یا اندازشان فرق کند<br />
<br />
با این ویژگی های گفته شده .<br />
<br />
آسان ترین روش و البته دقیق که من بتوانم شی را تشخیص بدم . چیست ؟<br />
<br />
متشکرم از راهنمایی شما</span></span></span>]]></content:encoded>
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		<item>
			<title><![CDATA[نرمال کردن مقادیر]]></title>
			<link>https://www.kelidestan.com/forum/showthread.php?tid=7825</link>
			<pubDate>Wed, 18 May 2016 08:45:02 +0100</pubDate>
			<dc:creator><![CDATA[<a href="https://www.kelidestan.com/forum/member.php?action=profile&uid=3507">سحر</a>]]></dc:creator>
			<guid isPermaLink="false">https://www.kelidestan.com/forum/showthread.php?tid=7825</guid>
			<description><![CDATA[<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">سلام دوست عزیز </span></span><br />
<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">لطفا راهنمایی کنید </span></span><br />
<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">در ادرس زیر</span></span><br />
<a href="http://www.kelidestan.com/forum/showthread.php?tid=4555" target="_blank" rel="noopener" class="mycode_url"><span style="color: #0072bc;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">http://www.kelidestan.com/forum/showthread.php?tid=4555</span></span></a><br />
<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">نحوه نرمال کردن داده ها در متلب هست</span></span><br />
<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">تشکر چون کد بسیار خوبیه </span></span><br />
<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">ولی باید چی کار  کنم که داده های نرمال شده به صورت دستی وارد نشوند</span></span><br />
<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">و خوشون وارد شوند</span></span><br />
<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">و یک کد هم دارم که می خوام خصوصیات بافت چند نوع میوه را بدست بیارم نمی دونم درست هست یا نه لطفا اگه برای شما امکان داره در این مورد به من کمک کنید دوست عزیز</span></span><br />
<br />
<br />
<div class="codeblock"><div class="title">Code:</div><div class="body" dir="ltr"><code>clc;<br />
close all;<br />
clear all;<br />
%%%%%%%%%%%%%%%%<br />
Img = imread('2an.jpg');<br />
imshow(Img);<br />
%%%%%%%%%%%%%%%%%%%%<br />
%??? ??????? ???? ??? ?? ?? ????<br />
% a = imresize(Img,0.25);<br />
%Agray = rgb2gray(a);<br />
%figure;<br />
%imshow(Agray)<br />
%%%%%%%%%%%%%%%%<br />
I=rgb2gray(Img);<br />
figure,imshow(I);<br />
%%%%%%%%%%%%%%%%%%%%%%<br />
% ??????? ?? ??? ?? ??? ?? ????? ? ?????? ???? ???? ???? ???????????<br />
%??????<br />
<br />
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br />
GLCM2 = graycomatrix(I,'offset', [0 1;-1 0; -1 1; -1 -1], 'Symmetric', true,'NumLevels',256);<br />
allst = graycoprops(GLCM2,'all')<br />
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br />
%????? ??? ?? ?? ?? ??  <br />
contrastInfo = allst.Contrast;<br />
display(contrastInfo);<br />
<br />
energyInfo =  allst.Energy;<br />
display(energyInfo);<br />
<br />
homogeneityInfo = allst.Homogeneity;<br />
display(homogeneityInfo);<br />
<br />
correlationInfo = allst.Correlation;<br />
display(correlationInfo);<br />
<br />
%entropyInfo = allst.entropy;<br />
%display(entropyInfo)<br />
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br />
%????? ???? ??<br />
data=ones(10,16); %Sample 2-dimensional data<br />
filename = 'data.xlsx';<br />
%x=[contrastInfo; correlationInfo; energyInfo; homogeneityInfo];<br />
col_header = {'contrastInfo1','contrastInfo2','contrastInfo3','contrastInfo4',...<br />
   'correlationInfo1','correlationInfo2','correlationInfo3',...<br />
'correlationInfo4','energyInfo1','energyInfo2','energyInfo3',...<br />
'energyInfo4','homogeneityInfo1','homogeneityInfo2',...<br />
'homogeneityInfo3','homogeneityInfo4';246.642603002913 199.461087420046 368.654791023378 356.430161661826 0.979277381035021 0.983231462683239 0.969043913269275 0.970070420169451 0.0503615982523675 0.0497760555027684 0.0470730893984990 0.0471528546549713 0.552727930170200 0.547379602892966 0.512308234242167 0.517383960290966};<br />
row_header(1:10,1)={'1'}; %???? ?? ????? 1 ?? 10 ?? ????? ??? ?????? ???? ?? ????? 1 ???<br />
xlswrite('data1.xlsx',col_header,'Sheet1','B1'); %Write column header<br />
xlswrite('data1.xlsx',row_header,'Sheet1','A2'); %Write row header   <br />
% ???? ???? ??? ???? ?? ??????? ???<br />
%???<br />
 %%%%%%%%%%%%%%%%%<br />
%???? ???? ?? ?? ????? ???<br />
%????<br />
%%%%%%%%%%%%%%<br />
x=[246.6426  199.4611  368.6548  356.4302; 0.0504    0.0498    0.0471    0.0472;0.5527  0.5474    0.5123    0.5174; 0.9793    0.9832    0.9690    0.9701];<br />
normalize_min_max=[0 1];<br />
x_size=size(x);<br />
x_normalized=zeros(x_size(1),x_size(2));<br />
x_max=max(max(x));<br />
x_min=min(min(x));<br />
nesbat=abs(normalize_min_max(2)-normalize_min_max(1))/(abs(x_max-x_min));<br />
for nn=1:x_size(1)<br />
   for mm=1:x_size(2)<br />
       x_normalized(nn,mm)=normalize_min_max(1)+(x(nn,mm)-x_min)*nesbat;<br />
   end<br />
end<br />
x_normalized</code></div></div><span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">سپاسگزارم</span></span>]]></description>
			<content:encoded><![CDATA[<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">سلام دوست عزیز </span></span><br />
<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">لطفا راهنمایی کنید </span></span><br />
<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">در ادرس زیر</span></span><br />
<a href="http://www.kelidestan.com/forum/showthread.php?tid=4555" target="_blank" rel="noopener" class="mycode_url"><span style="color: #0072bc;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">http://www.kelidestan.com/forum/showthread.php?tid=4555</span></span></a><br />
<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">نحوه نرمال کردن داده ها در متلب هست</span></span><br />
<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">تشکر چون کد بسیار خوبیه </span></span><br />
<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">ولی باید چی کار  کنم که داده های نرمال شده به صورت دستی وارد نشوند</span></span><br />
<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">و خوشون وارد شوند</span></span><br />
<span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">و یک کد هم دارم که می خوام خصوصیات بافت چند نوع میوه را بدست بیارم نمی دونم درست هست یا نه لطفا اگه برای شما امکان داره در این مورد به من کمک کنید دوست عزیز</span></span><br />
<br />
<br />
<div class="codeblock"><div class="title">Code:</div><div class="body" dir="ltr"><code>clc;<br />
close all;<br />
clear all;<br />
%%%%%%%%%%%%%%%%<br />
Img = imread('2an.jpg');<br />
imshow(Img);<br />
%%%%%%%%%%%%%%%%%%%%<br />
%??? ??????? ???? ??? ?? ?? ????<br />
% a = imresize(Img,0.25);<br />
%Agray = rgb2gray(a);<br />
%figure;<br />
%imshow(Agray)<br />
%%%%%%%%%%%%%%%%<br />
I=rgb2gray(Img);<br />
figure,imshow(I);<br />
%%%%%%%%%%%%%%%%%%%%%%<br />
% ??????? ?? ??? ?? ??? ?? ????? ? ?????? ???? ???? ???? ???????????<br />
%??????<br />
<br />
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br />
GLCM2 = graycomatrix(I,'offset', [0 1;-1 0; -1 1; -1 -1], 'Symmetric', true,'NumLevels',256);<br />
allst = graycoprops(GLCM2,'all')<br />
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br />
%????? ??? ?? ?? ?? ??  <br />
contrastInfo = allst.Contrast;<br />
display(contrastInfo);<br />
<br />
energyInfo =  allst.Energy;<br />
display(energyInfo);<br />
<br />
homogeneityInfo = allst.Homogeneity;<br />
display(homogeneityInfo);<br />
<br />
correlationInfo = allst.Correlation;<br />
display(correlationInfo);<br />
<br />
%entropyInfo = allst.entropy;<br />
%display(entropyInfo)<br />
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br />
%????? ???? ??<br />
data=ones(10,16); %Sample 2-dimensional data<br />
filename = 'data.xlsx';<br />
%x=[contrastInfo; correlationInfo; energyInfo; homogeneityInfo];<br />
col_header = {'contrastInfo1','contrastInfo2','contrastInfo3','contrastInfo4',...<br />
   'correlationInfo1','correlationInfo2','correlationInfo3',...<br />
'correlationInfo4','energyInfo1','energyInfo2','energyInfo3',...<br />
'energyInfo4','homogeneityInfo1','homogeneityInfo2',...<br />
'homogeneityInfo3','homogeneityInfo4';246.642603002913 199.461087420046 368.654791023378 356.430161661826 0.979277381035021 0.983231462683239 0.969043913269275 0.970070420169451 0.0503615982523675 0.0497760555027684 0.0470730893984990 0.0471528546549713 0.552727930170200 0.547379602892966 0.512308234242167 0.517383960290966};<br />
row_header(1:10,1)={'1'}; %???? ?? ????? 1 ?? 10 ?? ????? ??? ?????? ???? ?? ????? 1 ???<br />
xlswrite('data1.xlsx',col_header,'Sheet1','B1'); %Write column header<br />
xlswrite('data1.xlsx',row_header,'Sheet1','A2'); %Write row header   <br />
% ???? ???? ??? ???? ?? ??????? ???<br />
%???<br />
 %%%%%%%%%%%%%%%%%<br />
%???? ???? ?? ?? ????? ???<br />
%????<br />
%%%%%%%%%%%%%%<br />
x=[246.6426  199.4611  368.6548  356.4302; 0.0504    0.0498    0.0471    0.0472;0.5527  0.5474    0.5123    0.5174; 0.9793    0.9832    0.9690    0.9701];<br />
normalize_min_max=[0 1];<br />
x_size=size(x);<br />
x_normalized=zeros(x_size(1),x_size(2));<br />
x_max=max(max(x));<br />
x_min=min(min(x));<br />
nesbat=abs(normalize_min_max(2)-normalize_min_max(1))/(abs(x_max-x_min));<br />
for nn=1:x_size(1)<br />
   for mm=1:x_size(2)<br />
       x_normalized(nn,mm)=normalize_min_max(1)+(x(nn,mm)-x_min)*nesbat;<br />
   end<br />
end<br />
x_normalized</code></div></div><span style="color: #000000;" class="mycode_color"><span style="font-family:Tahoma, Georgia, 'Times New Roman', Times, serif;">سپاسگزارم</span></span>]]></content:encoded>
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		<item>
			<title><![CDATA[استخراج خصوصیات بافت]]></title>
			<link>https://www.kelidestan.com/forum/showthread.php?tid=7820</link>
			<pubDate>Tue, 17 May 2016 07:33:11 +0100</pubDate>
			<dc:creator><![CDATA[<a href="https://www.kelidestan.com/forum/member.php?action=profile&uid=3507">سحر</a>]]></dc:creator>
			<guid isPermaLink="false">https://www.kelidestan.com/forum/showthread.php?tid=7820</guid>
			<description><![CDATA[سلام <br />
می خواستم خصوصیات بافت چند نوع میوه را استخراج کنم<br />
یک تکه کد نوشتم ولی نمی دونم درست هست <br />
لطفا راهنمایی کنید که چه کارهایی باید انجام شود ولی من انجام ندادم<br />
با تشکر<br />
<hr class="mycode_hr" />
<div class="codeblock"><div class="title">Code:</div><div class="body" dir="ltr"><code>clc;<br />
close all;<br />
clear all;<br />
%%%%%%%%%%%%%%%%<br />
 Img = imread('2an.jpg');<br />
 imshow(Img);<br />
 %%%%%%%%%%%%%%%%%%%%<br />
 %??? ??????? ???? ??? ?? ?? ????<br />
% a = imresize(Img,0.25);<br />
%Agray = rgb2gray(a);<br />
%figure;<br />
%imshow(Agray)<br />
%%%%%%%%%%%%%%%%<br />
I=rgb2gray(Img);<br />
figure,imshow(I);<br />
%%%%%%%%%%%%%%%%%%%%%%<br />
% ??????? ?? ??? ?? ??? ?? ????? ? ?????? ???? ???? ???? ???????????<br />
%??????<br />
<br />
% Photo downloaded from “http://i425.photobucket.com/albums/pp337/jewarmy/python.jpg”<br />
GLCM2 = graycomatrix(I,'offset', [0 1;-1 0; -1 1; -1 -1], 'Symmetric', true,'NumLevels',256);<br />
allst = graycoprops(GLCM2,'all')<br />
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br />
%????? ??? ?? ?? ?? ??  <br />
contrastInfo = allst.Contrast;<br />
display(contrastInfo);<br />
<br />
energyInfo =  allst.Energy;<br />
display(energyInfo);<br />
<br />
homogeneityInfo = allst.Homogeneity;<br />
display(homogeneityInfo);<br />
<br />
correlationInfo = allst.Correlation;<br />
display(correlationInfo);</code></div></div>]]></description>
			<content:encoded><![CDATA[سلام <br />
می خواستم خصوصیات بافت چند نوع میوه را استخراج کنم<br />
یک تکه کد نوشتم ولی نمی دونم درست هست <br />
لطفا راهنمایی کنید که چه کارهایی باید انجام شود ولی من انجام ندادم<br />
با تشکر<br />
<hr class="mycode_hr" />
<div class="codeblock"><div class="title">Code:</div><div class="body" dir="ltr"><code>clc;<br />
close all;<br />
clear all;<br />
%%%%%%%%%%%%%%%%<br />
 Img = imread('2an.jpg');<br />
 imshow(Img);<br />
 %%%%%%%%%%%%%%%%%%%%<br />
 %??? ??????? ???? ??? ?? ?? ????<br />
% a = imresize(Img,0.25);<br />
%Agray = rgb2gray(a);<br />
%figure;<br />
%imshow(Agray)<br />
%%%%%%%%%%%%%%%%<br />
I=rgb2gray(Img);<br />
figure,imshow(I);<br />
%%%%%%%%%%%%%%%%%%%%%%<br />
% ??????? ?? ??? ?? ??? ?? ????? ? ?????? ???? ???? ???? ???????????<br />
%??????<br />
<br />
% Photo downloaded from “http://i425.photobucket.com/albums/pp337/jewarmy/python.jpg”<br />
GLCM2 = graycomatrix(I,'offset', [0 1;-1 0; -1 1; -1 -1], 'Symmetric', true,'NumLevels',256);<br />
allst = graycoprops(GLCM2,'all')<br />
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br />
%????? ??? ?? ?? ?? ??  <br />
contrastInfo = allst.Contrast;<br />
display(contrastInfo);<br />
<br />
energyInfo =  allst.Energy;<br />
display(energyInfo);<br />
<br />
homogeneityInfo = allst.Homogeneity;<br />
display(homogeneityInfo);<br />
<br />
correlationInfo = allst.Correlation;<br />
display(correlationInfo);</code></div></div>]]></content:encoded>
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		<item>
			<title><![CDATA[ذخیره سازی تصاویر به دست امده در حلقه]]></title>
			<link>https://www.kelidestan.com/forum/showthread.php?tid=7782</link>
			<pubDate>Thu, 12 May 2016 23:51:42 +0100</pubDate>
			<dc:creator><![CDATA[<a href="https://www.kelidestan.com/forum/member.php?action=profile&uid=3991">AsalR</a>]]></dc:creator>
			<guid isPermaLink="false">https://www.kelidestan.com/forum/showthread.php?tid=7782</guid>
			<description><![CDATA[با سلام <br />
<br />
دوستان من یک حلقه دارم که با هر بار اجرا شدن در صورت تحقق یک شرط یک تصویر  از داخل یک پوشه میخونه و نیاز دارم این تصاویرو در یک پوشه دیگه ذخیره کنم. میشه لطفا در مورد نحوه ذخیره کردن تصاویر طی حلقه کمکم کنید.  <img src="https://www.kelidestan.com/forum/images/smilies/confused.gif" alt="Confused" title="Confused" class="smilie smilie_13" /> <br />
<br />
خیلی ممنونم]]></description>
			<content:encoded><![CDATA[با سلام <br />
<br />
دوستان من یک حلقه دارم که با هر بار اجرا شدن در صورت تحقق یک شرط یک تصویر  از داخل یک پوشه میخونه و نیاز دارم این تصاویرو در یک پوشه دیگه ذخیره کنم. میشه لطفا در مورد نحوه ذخیره کردن تصاویر طی حلقه کمکم کنید.  <img src="https://www.kelidestan.com/forum/images/smilies/confused.gif" alt="Confused" title="Confused" class="smilie smilie_13" /> <br />
<br />
خیلی ممنونم]]></content:encoded>
		</item>
		<item>
			<title><![CDATA[رنگ در متلب(خیلی فوری)]]></title>
			<link>https://www.kelidestan.com/forum/showthread.php?tid=7507</link>
			<pubDate>Tue, 05 Apr 2016 06:38:39 +0100</pubDate>
			<dc:creator><![CDATA[<a href="https://www.kelidestan.com/forum/member.php?action=profile&uid=3682">faride93</a>]]></dc:creator>
			<guid isPermaLink="false">https://www.kelidestan.com/forum/showthread.php?tid=7507</guid>
			<description><![CDATA[سلام<br />
کسی میتونه درباره سوالی که پیوست میکنم نظری بده؟خیلی واجبه خواهشا یکی کمک کنه.(من تازه متلبو شروع کردم).<br /><!-- start: postbit_attachments_attachment -->
<div class="w3-row w3-container" style="margin-top:20px; margin-bottom:10px;"><!-- w3-row (start) -->  
        <header class="w3-container w3-teal w3-card-4">
            <h2 style="color:#FFF; font-size:15px; padding:10px; margin:0px; direction:rtl;">
               دانلود Capture.JPG  
            </h2>
        </header> 
        <div class="w3-col m12 w3-container w3-white k-all-blocks w3-card-4"> 
			

				<style>

			td.attachrow        {font: normal 11px Tahoma;}
			td.attachheader     {font: normal 11px Tahoma; background-color: #ededed; }
			table.attachtable    {font: normal 12px Tahoma;border-collapse : collapse; }

			</style>
				<style>

			td.attachrow        {font: normal 11px Tahoma;}
			td.attachheader     {font: normal 11px Tahoma; background-color: #ededed; }
			table.attachtable    {font: normal 12px Tahoma;border-collapse : collapse; }

			</style>
				<style>
			<!--
			table.attachtable    {font: normal 12px Tahoma;border-collapse : collapse; }
			td.attachheader     {font: normal 11px Tahoma; background-color: #ededed; }
			td.attachrow        {font: normal 11px Tahoma;}
			-->
			</style>


				<table border="0" cellpadding="0" cellspacing="3">
			<!-----Postbit_Attachment---->
				</table>
				<table class="attachtable" id="table2" width="95%" align="center" border="1" cellpadding="2" cellspacing="0">

					<tr>
						<td class="attachrow" width="15%"><span class="genmed">نام فایل</span></td>
						<td class="attachrow" width="75%"><span class="genmed">Capture.JPG</span></td>
						<td rowspan="5" class="attachrow" width="20%" align="center">
						<a href="attachment.php?aid=1290">
						<img src="kdevelop_down.png" alt="" border="0"></a><br>
					</tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">نوع فایل</span></td>
						<td class="attachrow" width="75%">
						<!-- start: attachment_icon -->
<img src="https://www.kelidestan.com/forum/images/attachtypes/image.gif" title="JPG Image" border="0" alt=".jpg" />
<!-- end: attachment_icon --></td>

			</td>        </tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">دفعات دانلود</span></td>
						<td class="attachrow" width="75%"><span class="genmed">346</span></td>
					</tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">اندازه</span></td>
						<td class="attachrow" width="75%">23 KB</td>
					</tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">ارسال کننده فایل</span></td>
						<td class="attachrow" width="75%">
						<a href="https://www.kelidestan.com/forum/member.php?action=profile&amp;uid=3682"></a></td>

			</td>        </tr>
				</table>


    </div>
</div><!-- w3-row (end) -->
<!-- end: postbit_attachments_attachment -->]]></description>
			<content:encoded><![CDATA[سلام<br />
کسی میتونه درباره سوالی که پیوست میکنم نظری بده؟خیلی واجبه خواهشا یکی کمک کنه.(من تازه متلبو شروع کردم).<br /><!-- start: postbit_attachments_attachment -->
<div class="w3-row w3-container" style="margin-top:20px; margin-bottom:10px;"><!-- w3-row (start) -->  
        <header class="w3-container w3-teal w3-card-4">
            <h2 style="color:#FFF; font-size:15px; padding:10px; margin:0px; direction:rtl;">
               دانلود Capture.JPG  
            </h2>
        </header> 
        <div class="w3-col m12 w3-container w3-white k-all-blocks w3-card-4"> 
			

				<style>

			td.attachrow        {font: normal 11px Tahoma;}
			td.attachheader     {font: normal 11px Tahoma; background-color: #ededed; }
			table.attachtable    {font: normal 12px Tahoma;border-collapse : collapse; }

			</style>
				<style>

			td.attachrow        {font: normal 11px Tahoma;}
			td.attachheader     {font: normal 11px Tahoma; background-color: #ededed; }
			table.attachtable    {font: normal 12px Tahoma;border-collapse : collapse; }

			</style>
				<style>
			<!--
			table.attachtable    {font: normal 12px Tahoma;border-collapse : collapse; }
			td.attachheader     {font: normal 11px Tahoma; background-color: #ededed; }
			td.attachrow        {font: normal 11px Tahoma;}
			-->
			</style>


				<table border="0" cellpadding="0" cellspacing="3">
			<!-----Postbit_Attachment---->
				</table>
				<table class="attachtable" id="table2" width="95%" align="center" border="1" cellpadding="2" cellspacing="0">

					<tr>
						<td class="attachrow" width="15%"><span class="genmed">نام فایل</span></td>
						<td class="attachrow" width="75%"><span class="genmed">Capture.JPG</span></td>
						<td rowspan="5" class="attachrow" width="20%" align="center">
						<a href="attachment.php?aid=1290">
						<img src="kdevelop_down.png" alt="" border="0"></a><br>
					</tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">نوع فایل</span></td>
						<td class="attachrow" width="75%">
						<!-- start: attachment_icon -->
<img src="https://www.kelidestan.com/forum/images/attachtypes/image.gif" title="JPG Image" border="0" alt=".jpg" />
<!-- end: attachment_icon --></td>

			</td>        </tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">دفعات دانلود</span></td>
						<td class="attachrow" width="75%"><span class="genmed">346</span></td>
					</tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">اندازه</span></td>
						<td class="attachrow" width="75%">23 KB</td>
					</tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">ارسال کننده فایل</span></td>
						<td class="attachrow" width="75%">
						<a href="https://www.kelidestan.com/forum/member.php?action=profile&amp;uid=3682"></a></td>

			</td>        </tr>
				</table>


    </div>
</div><!-- w3-row (end) -->
<!-- end: postbit_attachments_attachment -->]]></content:encoded>
		</item>
		<item>
			<title><![CDATA[rgb to name]]></title>
			<link>https://www.kelidestan.com/forum/showthread.php?tid=7506</link>
			<pubDate>Mon, 04 Apr 2016 17:33:43 +0100</pubDate>
			<dc:creator><![CDATA[<a href="https://www.kelidestan.com/forum/member.php?action=profile&uid=3682">faride93</a>]]></dc:creator>
			<guid isPermaLink="false">https://www.kelidestan.com/forum/showthread.php?tid=7506</guid>
			<description><![CDATA[سلام<br />
میشه توی متلب برنامه ای نوشت که مشخصات rgb رنگ را بگیره و بگه چه رنگیه؟<br />
اصلا میشه اینجور برنامه ای نوشت؟<br />
میشه یکم راهنماییم کنید]]></description>
			<content:encoded><![CDATA[سلام<br />
میشه توی متلب برنامه ای نوشت که مشخصات rgb رنگ را بگیره و بگه چه رنگیه؟<br />
اصلا میشه اینجور برنامه ای نوشت؟<br />
میشه یکم راهنماییم کنید]]></content:encoded>
		</item>
		<item>
			<title><![CDATA[.gif]]></title>
			<link>https://www.kelidestan.com/forum/showthread.php?tid=7496</link>
			<pubDate>Fri, 01 Apr 2016 16:43:41 +0100</pubDate>
			<dc:creator><![CDATA[<a href="https://www.kelidestan.com/forum/member.php?action=profile&uid=3682">faride93</a>]]></dc:creator>
			<guid isPermaLink="false">https://www.kelidestan.com/forum/showthread.php?tid=7496</guid>
			<description><![CDATA[سلام <br />
من یه سری عکس رنگی دارم که میخوام باهاشون یک فایل متحرک gifدرست کنم!چجوری میتونم اینکارو بکنم؟]]></description>
			<content:encoded><![CDATA[سلام <br />
من یه سری عکس رنگی دارم که میخوام باهاشون یک فایل متحرک gifدرست کنم!چجوری میتونم اینکارو بکنم؟]]></content:encoded>
		</item>
		<item>
			<title><![CDATA[segmentation(خیلی فوری)]]></title>
			<link>https://www.kelidestan.com/forum/showthread.php?tid=7495</link>
			<pubDate>Fri, 01 Apr 2016 08:13:36 +0100</pubDate>
			<dc:creator><![CDATA[<a href="https://www.kelidestan.com/forum/member.php?action=profile&uid=3682">faride93</a>]]></dc:creator>
			<guid isPermaLink="false">https://www.kelidestan.com/forum/showthread.php?tid=7495</guid>
			<description><![CDATA[سلام <br />
من یک فایل تصویر .gif از یک عدد دیجیتال دارم چحوری میتونم این عددو قسمت بندی کنم تا بتونم هر وقت خواستم یک قسمت تصویر را رنگ خاص بدم؟<br />
(تصویرو فرستادم من میخوام این 7 قسمت عددمو بتونم جدا جدا داشته باشم که بتونم عدد رو عوض کنم مثلا 0 به 8 تبدیل کنم)<br /><!-- start: postbit_attachments_attachment -->
<div class="w3-row w3-container" style="margin-top:20px; margin-bottom:10px;"><!-- w3-row (start) -->  
        <header class="w3-container w3-teal w3-card-4">
            <h2 style="color:#FFF; font-size:15px; padding:10px; margin:0px; direction:rtl;">
               دانلود 7seg.gif  
            </h2>
        </header> 
        <div class="w3-col m12 w3-container w3-white k-all-blocks w3-card-4"> 
			

				<style>

			td.attachrow        {font: normal 11px Tahoma;}
			td.attachheader     {font: normal 11px Tahoma; background-color: #ededed; }
			table.attachtable    {font: normal 12px Tahoma;border-collapse : collapse; }

			</style>
				<style>

			td.attachrow        {font: normal 11px Tahoma;}
			td.attachheader     {font: normal 11px Tahoma; background-color: #ededed; }
			table.attachtable    {font: normal 12px Tahoma;border-collapse : collapse; }

			</style>
				<style>
			<!--
			table.attachtable    {font: normal 12px Tahoma;border-collapse : collapse; }
			td.attachheader     {font: normal 11px Tahoma; background-color: #ededed; }
			td.attachrow        {font: normal 11px Tahoma;}
			-->
			</style>


				<table border="0" cellpadding="0" cellspacing="3">
			<!-----Postbit_Attachment---->
				</table>
				<table class="attachtable" id="table2" width="95%" align="center" border="1" cellpadding="2" cellspacing="0">

					<tr>
						<td class="attachrow" width="15%"><span class="genmed">نام فایل</span></td>
						<td class="attachrow" width="75%"><span class="genmed">7seg.gif</span></td>
						<td rowspan="5" class="attachrow" width="20%" align="center">
						<a href="attachment.php?aid=1287">
						<img src="kdevelop_down.png" alt="" border="0"></a><br>
					</tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">نوع فایل</span></td>
						<td class="attachrow" width="75%">
						<!-- start: attachment_icon -->
<img src="https://www.kelidestan.com/forum/images/attachtypes/image.gif" title="GIF Image" border="0" alt=".gif" />
<!-- end: attachment_icon --></td>

			</td>        </tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">دفعات دانلود</span></td>
						<td class="attachrow" width="75%"><span class="genmed">156</span></td>
					</tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">اندازه</span></td>
						<td class="attachrow" width="75%">10.6 KB</td>
					</tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">ارسال کننده فایل</span></td>
						<td class="attachrow" width="75%">
						<a href="https://www.kelidestan.com/forum/member.php?action=profile&amp;uid=3682"></a></td>

			</td>        </tr>
				</table>


    </div>
</div><!-- w3-row (end) -->
<!-- end: postbit_attachments_attachment -->]]></description>
			<content:encoded><![CDATA[سلام <br />
من یک فایل تصویر .gif از یک عدد دیجیتال دارم چحوری میتونم این عددو قسمت بندی کنم تا بتونم هر وقت خواستم یک قسمت تصویر را رنگ خاص بدم؟<br />
(تصویرو فرستادم من میخوام این 7 قسمت عددمو بتونم جدا جدا داشته باشم که بتونم عدد رو عوض کنم مثلا 0 به 8 تبدیل کنم)<br /><!-- start: postbit_attachments_attachment -->
<div class="w3-row w3-container" style="margin-top:20px; margin-bottom:10px;"><!-- w3-row (start) -->  
        <header class="w3-container w3-teal w3-card-4">
            <h2 style="color:#FFF; font-size:15px; padding:10px; margin:0px; direction:rtl;">
               دانلود 7seg.gif  
            </h2>
        </header> 
        <div class="w3-col m12 w3-container w3-white k-all-blocks w3-card-4"> 
			

				<style>

			td.attachrow        {font: normal 11px Tahoma;}
			td.attachheader     {font: normal 11px Tahoma; background-color: #ededed; }
			table.attachtable    {font: normal 12px Tahoma;border-collapse : collapse; }

			</style>
				<style>

			td.attachrow        {font: normal 11px Tahoma;}
			td.attachheader     {font: normal 11px Tahoma; background-color: #ededed; }
			table.attachtable    {font: normal 12px Tahoma;border-collapse : collapse; }

			</style>
				<style>
			<!--
			table.attachtable    {font: normal 12px Tahoma;border-collapse : collapse; }
			td.attachheader     {font: normal 11px Tahoma; background-color: #ededed; }
			td.attachrow        {font: normal 11px Tahoma;}
			-->
			</style>


				<table border="0" cellpadding="0" cellspacing="3">
			<!-----Postbit_Attachment---->
				</table>
				<table class="attachtable" id="table2" width="95%" align="center" border="1" cellpadding="2" cellspacing="0">

					<tr>
						<td class="attachrow" width="15%"><span class="genmed">نام فایل</span></td>
						<td class="attachrow" width="75%"><span class="genmed">7seg.gif</span></td>
						<td rowspan="5" class="attachrow" width="20%" align="center">
						<a href="attachment.php?aid=1287">
						<img src="kdevelop_down.png" alt="" border="0"></a><br>
					</tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">نوع فایل</span></td>
						<td class="attachrow" width="75%">
						<!-- start: attachment_icon -->
<img src="https://www.kelidestan.com/forum/images/attachtypes/image.gif" title="GIF Image" border="0" alt=".gif" />
<!-- end: attachment_icon --></td>

			</td>        </tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">دفعات دانلود</span></td>
						<td class="attachrow" width="75%"><span class="genmed">156</span></td>
					</tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">اندازه</span></td>
						<td class="attachrow" width="75%">10.6 KB</td>
					</tr>
					<tr>
						<td class="attachrow" width="15%"><span class="genmed">ارسال کننده فایل</span></td>
						<td class="attachrow" width="75%">
						<a href="https://www.kelidestan.com/forum/member.php?action=profile&amp;uid=3682"></a></td>

			</td>        </tr>
				</table>


    </div>
</div><!-- w3-row (end) -->
<!-- end: postbit_attachments_attachment -->]]></content:encoded>
		</item>
		<item>
			<title><![CDATA[روش درون یابی خطی در پردازش تصویر]]></title>
			<link>https://www.kelidestan.com/forum/showthread.php?tid=7478</link>
			<pubDate>Mon, 28 Mar 2016 19:36:32 +0100</pubDate>
			<dc:creator><![CDATA[<a href="https://www.kelidestan.com/forum/member.php?action=profile&uid=3747">n.saffari</a>]]></dc:creator>
			<guid isPermaLink="false">https://www.kelidestan.com/forum/showthread.php?tid=7478</guid>
			<description><![CDATA[من قصد دارم از پردازش تصویر در متلب برای بزرگنمایی یه نصویر استفاده کنم در نت دو روش پیداکردم <br />
1/روش نزدیک ترین همسایه         2/روش درون یابی خطی<br />
دوستانی که با هر کدام کار کردن ممنون میشم برام پیام بگذارند به یک اشکال خوردم<br />
سپاس]]></description>
			<content:encoded><![CDATA[من قصد دارم از پردازش تصویر در متلب برای بزرگنمایی یه نصویر استفاده کنم در نت دو روش پیداکردم <br />
1/روش نزدیک ترین همسایه         2/روش درون یابی خطی<br />
دوستانی که با هر کدام کار کردن ممنون میشم برام پیام بگذارند به یک اشکال خوردم<br />
سپاس]]></content:encoded>
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