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AI Shack - Tutorials for OpenCV, computer vision, deep learning, image processing, neural networks and artificial intelligence.
http://www.aishack.in/tracks/image-processing-algorithms-level-1
Image processing algorithms (level 1). Image processing algorithms (level 1). This track is an introduction to algorithms with image processing. You learn about how some of the most basic techniques in computer vision can be used to your advantage. With these in your toolkit, you'll be able to think of image processing problems in terms of techniques that have been around for decades. 1 Generating uniform noise. 4 An introduction to contours. Play Name that Dataset! 2D matrices with CvMat in OpenCV.
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Image Moments - AI Shack - Tutorials for OpenCV, computer vision, deep learning, image processing, neural networks and artificial intelligence.
http://www.aishack.in/tutorials/image-moments
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 - an explanation of what moments area! The math of moments. In pure math, the n. Order moment about the point c is defined as:. Here, the f(x, y) is the actual image and is assumed to be continuous. For our purposes, we need a discrete way (think pixels) to describe moments:.
aishack.in
AI Shack - Tutorials for OpenCV, computer vision, deep learning, image processing, neural networks and artificial intelligence.
http://www.aishack.in/contribute
Contribute to AI Shack. AI Shack is a one man endeavour. While I don't expect readers to contribute, any help however minor is always useful! Fact checks and intuition. I try and make sure everything on the website is technically accurate. However, there may be times when something isn't just right. Reaching out to me with such corrections would be very helpful! Do you have expertise in a certain area of AI? Worked on an awesome project recently? An overview of how a certain task can be achieved. Directo...
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SIFT: Theory and Practice: Introduction - AI Shack - Tutorials for OpenCV, computer vision, deep learning, image processing, neural networks and artificial intelligence.
http://www.aishack.in/tutorials/sift-scale-invariant-feature-transform-introduction
SIFT: Theory and Practice. SIFT: Theory and Practice. SIFT: Theory and Practice:. Getting rid of low contrast keypoints. Matching features across different images in a common problem in computer vision. When all images are similar in nature (same scale, orientation, etc) simple corner detectors. Can work. But when you have images of different scales and rotations, you need to use the Scale Invariant Feature Transform. Why care about SIFT. Here's an example. We're looking for these:. The Laplacian of Gaus...
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SIFT: Theory and Practice: Generating a feature - AI Shack - Tutorials for OpenCV, computer vision, deep learning, image processing, neural networks and artificial intelligence.
http://www.aishack.in/tutorials/sift-scale-invariant-feature-transform-features
SIFT: Theory and Practice. SIFT: Theory and Practice. SIFT: Theory and Practice:. Getting rid of low contrast keypoints. Now for the final step of SIFT. Till now, we had scale and rotation invariance. Now we create a fingerprint for each keypoint. This is to identify a keypoint. If an eye is a keypoint, then using this fingerprint, we'll be able to distinguish it from other keypoints, like ears, noses, fingers, etc. This is done using a "gaussian weighting function". This function simply generates a ...
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SIFT: Theory and Practice: The scale space - AI Shack - Tutorials for OpenCV, computer vision, deep learning, image processing, neural networks and artificial intelligence.
http://www.aishack.in/tutorials/sift-scale-invariant-feature-transform-scale-space
SIFT: Theory and Practice. SIFT: Theory and Practice. SIFT: Theory and Practice:. Getting rid of low contrast keypoints. Real world objects are meaningful only at a certain scale. You might see a sugar cube perfectly on a table. But if looking at the entire milky way, then it simply does not exist. This multi-scale nature of objects is quite common in nature. And a scale space attempts to replicate this concept on digital images. Do you want to look at a leaf or the entire tree? Scale spaces in SIFT.
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Science, the only way forward | edaro's Weblog
https://edaro.wordpress.com/2012/07/26/science-the-only-way-forward
Laquo; Of ghosts, spirits and stories. Science, the only way forward. July 26, 2012 by edaro. Human beings are, by nature, the most curious of living things on the Earth. There is an inherent ability in us to feel wonder, reverence and awe, and the imperative need for an explanation to all our questions. We want to know why. This is the motivation for all our actions, and the basis for Science and Religion. What then of the claim that science cannot explain everything? On December 20, 2013 at 12:00 pm.
aishack.in
AI Shack - Tutorials for OpenCV, computer vision, deep learning, image processing, neural networks and artificial intelligence.
http://www.aishack.in/tracks/opencv-basics
Track, you'll learn how to get started with OpenCV right away. You'll learn all essentials: installation, reading/writing images, manipulation and working with the camera. This is an excellent first step towards computer vision in general. 2 Installing and Getting OpenCV running. 6 HighGUI: Creating Interfaces. Think you can differentiate between the different computer vision datasets? Play Name that Dataset! Additional functions to view whats happening. Image Convolutions in OpenCV.
aishack.in
AI Shack - Tutorials for OpenCV, computer vision, deep learning, image processing, neural networks and artificial intelligence.
http://www.aishack.in/about
Welcome to AI Shack a place to learn artificial intelligence through clear tutorials. All the mathematical mumbo-jumbo, made so simple to understand (through graphics, animations, and videos) that you’d think Oh, that’s it? Its like a Talk to me like I’m a 3 year old book on properties of the Hausdorff space in Topology. My name is Utkarsh Sinha, and I’m the creator and writer here at AI Shack. I’m 25 and currently studying Computer Vision. As a Technical Director. And most importantly, the Goan shacks.
aishack.in
AI Shack - Tutorials for OpenCV, computer vision, deep learning, image processing, neural networks and artificial intelligence.
http://www.aishack.in/tutorials
Here is a listing of all the tutorials on this AI Shack. Tracks are a series of tutorials put together in a logical order. Image processing algorithms (level 1). Figure out how some of the basic algorithms in computer vision work. This track takes you through a handful of everyday use algorithms. Image processing algorithms (level 2). Learn how commonly used algorithms in computer vision work under the hood. You'll learn a wide variety of techniques to add to your arsenal. The Canny Edge Detector. Occasi...
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