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	<title>AI Shack</title>
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	<link>http://www.aishack.in</link>
	<description>Clear tutorials for the enthusiast</description>
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		<title>Scanning QR Codes</title>
		<link>http://www.aishack.in/2012/02/scanning-qr-codes/</link>
		<comments>http://www.aishack.in/2012/02/scanning-qr-codes/#comments</comments>
		<pubDate>Fri, 03 Feb 2012 06:12:48 +0000</pubDate>
		<dc:creator><![CDATA[Utkarsh]]></dc:creator>
				<category><![CDATA[Computer Vision]]></category>
		<category><![CDATA[code]]></category>
		<category><![CDATA[detect]]></category>
		<category><![CDATA[opencv]]></category>
		<category><![CDATA[qr]]></category>
		<category><![CDATA[scan]]></category>

		<guid isPermaLink="false">http://www.aishack.in/?p=2177</guid>
		<description><![CDATA[These days, you can see QR codes almost everywhere. You see them at stores, on products, on screens. So one day, I was curious how the gears are put together to read QR codes. I ended up reading the ISO/IEC 18004 standard. It&#8217;s a very robust and detailed documentation on how QR codes are supposed [&#8230;]]]></description>
		<wfw:commentRss>http://www.aishack.in/2012/02/scanning-qr-codes/feed/</wfw:commentRss>
		<slash:comments>5</slash:comments>
		</item>
		<item>
		<title>Implementing Canny Edges from scratch</title>
		<link>http://www.aishack.in/2011/07/implementing-canny-edges-from-scratch/</link>
		<comments>http://www.aishack.in/2011/07/implementing-canny-edges-from-scratch/#comments</comments>
		<pubDate>Fri, 01 Jul 2011 13:17:27 +0000</pubDate>
		<dc:creator><![CDATA[Utkarsh]]></dc:creator>
				<category><![CDATA[Computer Vision]]></category>
		<category><![CDATA[canny]]></category>
		<category><![CDATA[edge detection]]></category>
		<category><![CDATA[opencv]]></category>

		<guid isPermaLink="false">http://www.aishack.in/?p=2036</guid>
		<description><![CDATA[Here&#8217;s an interesting article &#8211; we&#8217;ll implement canny edges. We won&#8217;t use any prepackaged functions. I&#8217;ll be using OpenCV for this article, but I&#8217;m sure translating it to some other computer vision package won&#8217;t be difficult. I assume you know how the algorithm works. If not, read up about the Canny edge detection algorithm! Getting Started [&#8230;]]]></description>
		<wfw:commentRss>http://www.aishack.in/2011/07/implementing-canny-edges-from-scratch/feed/</wfw:commentRss>
		<slash:comments>8</slash:comments>
		</item>
		<item>
		<title>The Canny Edge Detector</title>
		<link>http://www.aishack.in/2011/06/the-canny-edge-detector/</link>
		<comments>http://www.aishack.in/2011/06/the-canny-edge-detector/#comments</comments>
		<pubDate>Tue, 28 Jun 2011 05:41:23 +0000</pubDate>
		<dc:creator><![CDATA[Utkarsh]]></dc:creator>
				<category><![CDATA[Computer Vision]]></category>
		<category><![CDATA[canny]]></category>
		<category><![CDATA[edge detection]]></category>

		<guid isPermaLink="false">http://www.aishack.in/?p=1999</guid>
		<description><![CDATA[A lot of people consider the Canny Edge Detector the ultimate edge detector. You get clean, thin edges that are well connected to nearby edges. If you use some image processing package, you probably get a function that does everything. Here, I&#8217;ll go into exactly how they work. Overview The canny edge detector is a multistage [&#8230;]]]></description>
		<wfw:commentRss>http://www.aishack.in/2011/06/the-canny-edge-detector/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
		</item>
		<item>
		<title>Image Moments</title>
		<link>http://www.aishack.in/2011/06/image-moments/</link>
		<comments>http://www.aishack.in/2011/06/image-moments/#comments</comments>
		<pubDate>Tue, 21 Jun 2011 06:34:56 +0000</pubDate>
		<dc:creator><![CDATA[Utkarsh]]></dc:creator>
				<category><![CDATA[Computer Vision]]></category>
		<category><![CDATA[area]]></category>
		<category><![CDATA[blobs]]></category>
		<category><![CDATA[centroid]]></category>
		<category><![CDATA[moment]]></category>
		<category><![CDATA[tracking]]></category>

		<guid isPermaLink="false">http://www.aishack.in/?p=1937</guid>
		<description><![CDATA[An Image moment is a number calculated using a certain formula. Understand what that formula means might be hard at first. In fact, I got a lot of questions about moments from the tracking tutorial I did long back. So, here it is &#8211; an explanation of what moments area! The math of moments In [&#8230;]]]></description>
		<wfw:commentRss>http://www.aishack.in/2011/06/image-moments/feed/</wfw:commentRss>
		<slash:comments>6</slash:comments>
		</item>
		<item>
		<title>The OpenCV 2 Computer Vision Application Programming Cookbook</title>
		<link>http://www.aishack.in/2011/06/the-opencv-2-computer-vision-application-programming-cookbook/</link>
		<comments>http://www.aishack.in/2011/06/the-opencv-2-computer-vision-application-programming-cookbook/#comments</comments>
		<pubDate>Thu, 16 Jun 2011 06:34:20 +0000</pubDate>
		<dc:creator><![CDATA[Utkarsh]]></dc:creator>
				<category><![CDATA[Blog]]></category>
		<category><![CDATA[Reviews]]></category>
		<category><![CDATA[book]]></category>
		<category><![CDATA[opencv]]></category>
		<category><![CDATA[packtpub]]></category>

		<guid isPermaLink="false">http://www.aishack.in/?p=1895</guid>
		<description><![CDATA[The good people at Packt Publishing sent me a copy of OpenCV 2 Computer Vision Application Programming Cookbook, by Robert Laganiere. I&#8217;ve been reading it for a few days now, and here&#8217;s my take on the book &#8211; this book covers a lot of practical problems coders face when writing a computer vision application! The [&#8230;]]]></description>
		<wfw:commentRss>http://www.aishack.in/2011/06/the-opencv-2-computer-vision-application-programming-cookbook/feed/</wfw:commentRss>
		<slash:comments>6</slash:comments>
		</item>
		<item>
		<title>A super fast thresholding technique</title>
		<link>http://www.aishack.in/2011/05/a-super-fast-thresholding-technique/</link>
		<comments>http://www.aishack.in/2011/05/a-super-fast-thresholding-technique/#comments</comments>
		<pubDate>Tue, 31 May 2011 15:26:54 +0000</pubDate>
		<dc:creator><![CDATA[Utkarsh]]></dc:creator>
				<category><![CDATA[Computer Vision]]></category>
		<category><![CDATA[thresholding]]></category>

		<guid isPermaLink="false">http://www.aishack.in/?p=1838</guid>
		<description><![CDATA[In computer vision, thresholding is probably the most basic operation. It needs to go through every single pixel of every single frame to detect objects. If you can make it efficient, you&#8217;ll have CPU time to do other stuff. Today (after a long time, I might add) I&#8217;ll introduce you to an interesting technique that [&#8230;]]]></description>
		<wfw:commentRss>http://www.aishack.in/2011/05/a-super-fast-thresholding-technique/feed/</wfw:commentRss>
		<slash:comments>7</slash:comments>
		</item>
		<item>
		<title>The Sobel and Laplacian Edge Detectors</title>
		<link>http://www.aishack.in/2011/04/the-sobel-and-laplacian-edge-detectors/</link>
		<comments>http://www.aishack.in/2011/04/the-sobel-and-laplacian-edge-detectors/#comments</comments>
		<pubDate>Sun, 24 Apr 2011 09:18:50 +0000</pubDate>
		<dc:creator><![CDATA[Utkarsh]]></dc:creator>
				<category><![CDATA[Computer Vision]]></category>
		<category><![CDATA[edge detection]]></category>
		<category><![CDATA[laplacian]]></category>
		<category><![CDATA[sobel]]></category>

		<guid isPermaLink="false">http://www.aishack.in/?p=1985</guid>
		<description><![CDATA[Detecting edges is one of the fundamental operations you can do in image processing. It helps you reduce the amount of data (pixels) to process and maintains the &#8220;structural&#8221; aspect of the image. We&#8217;ll look at two  commonly used edge detection schemes &#8211; the gradient based edge detector and the laplacian based edge detector. Both [&#8230;]]]></description>
		<wfw:commentRss>http://www.aishack.in/2011/04/the-sobel-and-laplacian-edge-detectors/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Predator: Tracking + Learning</title>
		<link>http://www.aishack.in/2011/04/predator-tracking-learning/</link>
		<comments>http://www.aishack.in/2011/04/predator-tracking-learning/#comments</comments>
		<pubDate>Thu, 31 Mar 2011 19:05:46 +0000</pubDate>
		<dc:creator><![CDATA[Utkarsh]]></dc:creator>
				<category><![CDATA[Blog]]></category>
		<category><![CDATA[features]]></category>
		<category><![CDATA[learning]]></category>
		<category><![CDATA[Lucas-Kanade]]></category>
		<category><![CDATA[online]]></category>
		<category><![CDATA[tracking]]></category>

		<guid isPermaLink="false">http://www.aishack.in/2011/04/predator-tracking-learning/</guid>
		<description><![CDATA[Researcher Zdenek Kalal has come up with a system that can quickly learn how to track objects in a video stream. The demo is quite interesting. The related papers are mentioned at the end of the video (and also on Zdenek&#8217;s website). To decide if the tracking algorithm is actually so robust or not, I&#8217;ll [&#8230;]]]></description>
		<wfw:commentRss>http://www.aishack.in/2011/04/predator-tracking-learning/feed/</wfw:commentRss>
		<slash:comments>8</slash:comments>
		</item>
		<item>
		<title>OpenCV Face Detection Visualized</title>
		<link>http://www.aishack.in/2011/03/opencv-face-detection-visualized/</link>
		<comments>http://www.aishack.in/2011/03/opencv-face-detection-visualized/#comments</comments>
		<pubDate>Mon, 21 Mar 2011 08:40:59 +0000</pubDate>
		<dc:creator><![CDATA[Utkarsh]]></dc:creator>
				<category><![CDATA[Blog]]></category>

		<guid isPermaLink="false">http://www.aishack.in/2011/03/opencv-face-detection-visualized/</guid>
		<description><![CDATA[This video visualizes the detection process of OpenCV&#8217;s face detector. The algorithm uses the Viola Jones method of calculating the integral image and then performing some calculations on all the areas defined by the black and white rectangles to analyze the differences between the dark and light regions of a face. The sub-window (in red) [&#8230;]]]></description>
		<wfw:commentRss>http://www.aishack.in/2011/03/opencv-face-detection-visualized/feed/</wfw:commentRss>
		<slash:comments>3</slash:comments>
		</item>
		<item>
		<title>New statistical model of vision explains various pecularities</title>
		<link>http://www.aishack.in/2011/02/new-statistical-model-of-vision/</link>
		<comments>http://www.aishack.in/2011/02/new-statistical-model-of-vision/#comments</comments>
		<pubDate>Wed, 09 Feb 2011 09:15:01 +0000</pubDate>
		<dc:creator><![CDATA[Utkarsh]]></dc:creator>
				<category><![CDATA[Blog]]></category>
		<category><![CDATA[mathematics]]></category>
		<category><![CDATA[MIT]]></category>
		<category><![CDATA[model]]></category>

		<guid isPermaLink="false">http://www.aishack.in/2011/02/new-statistical-model-of-vision/</guid>
		<description><![CDATA[The human retina is made up of approximately 100 million light sensitive cells. This is enough to overload all neurons in the brain. So, people believed that the brain somehow reduced this information overload &#8211; by interpreting things in terms of horizontal, vertical and diagonal lines. Then, combining these lines and edges into objects that [&#8230;]]]></description>
		<wfw:commentRss>http://www.aishack.in/2011/02/new-statistical-model-of-vision/feed/</wfw:commentRss>
		<slash:comments>3</slash:comments>
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