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AI SHACK
Tutorials for OpenCV, computer vision, deep learning, image processing, neural networks and artificial intelligence. FUNDAMENTALS OF FEATURES AND CORNERS: FEATURES: WHAT ARE Fundamentals of Features and Corners. Several computer vision tasks require finding matching points across several frames or views. With that info, you could really do a lot of stuff. An example. When doing stereo imaging, you want to know a few corresponding points between the two views. Once you do, you can triangulate almost all points on NOISE MODELS: GAUSSIAN AND GAMMA NOISE SIFT: THEORY AND PRACTICE: LOG APPROXIMATIONS THE CANNY EDGE DETECTOR: INTRODUCTION THE EDGE CONVOLUTIONS: IMAGE CONVOLUTION EXAMPLES Here's a first and simplest. This convolution kernel has an averaging effect. So you end up with a slight blur. The image convolution kernel is: Note that the sum of all elements of this matrix is 1.0. This is important. If the sum is not exactly one, the resultant image will be brighter or darker. Here's a blur that I got on an image: OPENCV MEMORY MANAGEMENT CONNECTED COMPONENT LABELLING: PIXEL NEIGHBOURHOODS ANDSEE MORE ONAISHACK.IN
SUBPIXEL CORNERS IN OPENCV OpenCV comes with a function to help you find subpixel corners. It uses the dot product technique to refine corners detected by other techniques, like the Shi-Tomasi corner detector.The function works iteratively, refining the corners till a termination criteria isreached.
K-MEANS CLUSTERING
K-Means clustering. K-Means is a clustering algorithm. That means you can "group" points based on their neighbourhood. When a lot of points a near by, you mark them as one cluster. With K-means, you can find good center points for these clusters. This "clustering" is not limited to two dimensions.AI SHACK
Tutorials for OpenCV, computer vision, deep learning, image processing, neural networks and artificial intelligence. FUNDAMENTALS OF FEATURES AND CORNERS: FEATURES: WHAT ARE Fundamentals of Features and Corners. Several computer vision tasks require finding matching points across several frames or views. With that info, you could really do a lot of stuff. An example. When doing stereo imaging, you want to know a few corresponding points between the two views. Once you do, you can triangulate almost all points on NOISE MODELS: GAUSSIAN AND GAMMA NOISE SIFT: THEORY AND PRACTICE: LOG APPROXIMATIONS THE CANNY EDGE DETECTOR: INTRODUCTION THE EDGE CONVOLUTIONS: IMAGE CONVOLUTION EXAMPLES Here's a first and simplest. This convolution kernel has an averaging effect. So you end up with a slight blur. The image convolution kernel is: Note that the sum of all elements of this matrix is 1.0. This is important. If the sum is not exactly one, the resultant image will be brighter or darker. Here's a blur that I got on an image: OPENCV MEMORY MANAGEMENT CONNECTED COMPONENT LABELLING: PIXEL NEIGHBOURHOODS ANDSEE MORE ONAISHACK.IN
SUBPIXEL CORNERS IN OPENCV OpenCV comes with a function to help you find subpixel corners. It uses the dot product technique to refine corners detected by other techniques, like the Shi-Tomasi corner detector.The function works iteratively, refining the corners till a termination criteria isreached.
K-MEANS CLUSTERING
K-Means clustering. K-Means is a clustering algorithm. That means you can "group" points based on their neighbourhood. When a lot of points a near by, you mark them as one cluster. With K-means, you can find good center points for these clusters. This "clustering" is not limited to two dimensions. NOISE MODELS: GAUSSIAN AND GAMMA NOISE Below: The image with gaussian noise. The histogram for each of these images is: The upper image is the histogram for the original image. Because it has only 2 colours, there are just two spikes. The lower image is the histogram for noisy image. When noise is added, noticehow
THE CANNY EDGE DETECTOR: INTRODUCTION THE EDGE The canny edge detector is a multistage edge detection algorithm. The steps are: The two key parameters of the algorithm are - an upper threshold and a lower threshold. The upper threshold is used to mark edges that are definitely edges. The lower threshold is to CONVOLUTIONS: IMAGE CONVOLUTION EXAMPLES A convolution is very useful for signal processing in general. There is a lot of complex mathematical theory available for convolutions. For digital image processing, you don't have to understand all ofthat.
THE SOBEL AND LAPLACIAN EDGE DETECTORS The Sobel Edge Detector. The Sobel edge detector is a gradient based method. It works with first order derivatives. It calculates the first derivatives of the image separately for the X and Y axes. The derivatives are only approximations (because the images are not continuous). To approximate them, the following kernels are used forconvolution
HIGHGUI: CREATING INTERFACES Introduction. In this tutorial, you'll learn how to add trackbars to windows. And also how to detect mouse click events within a window. An application of these flexibilities would be being able to dynamically control things within your program like changing the amount of erode without recompiling the code. HISTOGRAMS WITH FUNCTIONS OF OPENCV To create a new histogram, use the cvCreateHist function: CvHistogram* cvCreateHist(int dims, int* sizes, int type, float** ranges = NULL, int uniform = 1 ); Very simple to understand. Here's what the parameters mean: dims: The number of dimensions. *sizes: This is an array with the same number of integers as the number of dimensions. SUDOKU GRABBER IN OPENCV: EXTRACTING THE GRID The iterator helps traverse the array list. Each element of the list contains 2 things: rho and theta (the normal form of a line).. During the merging process, certain lines will fuse together. SUBPIXEL CORNERS IN OPENCV OpenCV comes with a function to help you find subpixel corners. It uses the dot product technique to refine corners detected by other techniques, like the Shi-Tomasi corner detector.The function works iteratively, refining the corners till a termination criteria isreached.
SUDOKU GRABBER IN OPENCV: GRID DETECTION Flood filling each blob (in progress) We iterate through the image. The >=128 condition is to ensure that only the white parts are flooded. Whenever we encounter such a FUNDAMENTALS OF FEATURES AND CORNERS: THE SHI-TOMASI The Shi-Tomasi corner detector is based entirely on the Harris corner detector. However, one slight variation in a "selection criteria" made this detector much better than the original. It works quite well where even the Harris corner detector fails. So here's the minor change that Shi and Tomasi did to the original Harris corner detector. Toggle navigation AI SHACK*
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SCANNING QR CODES
Recognize QR Codes in images from scratch. We'll do all the bit math to figure out the location markers and then read data from theblack/white array.
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SUDOKU GRABBER IN OPENCV I decided to write a quick and fun project. The idea is simple - capture an image, identify the sudoku grid + digits and then solve thepuzzle!
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TRACKING COLORED OBJECTS IN OPENCV Given an object that is distinctly colored, you'll learn how to detect the object in the scene and track it as it moves across the frame -live!
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SIFT: THEORY AND PRACTICE Learn how the famous SIFT keypoint detector works in the background. This paper led a mini revolution in the world of computer vision!Read more
-------------------------RECENT TUTORIALS
GENERATING MULTIVARIATE GAUSSIAN RANDOM NUMBERS Given a covariance matrix and a mean vector, how do we generate random vectors from the corresponding Gaussian model?Read more
EXPECTATION MAXIMIZATION WITH GAUSSIAN MIXTURE MODELS Learn how to model multivariate data with a Gaussian Mixture Model. For training this model, we use a technique called ExpectationMaximization.
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FINDING DOMINANT COLORS IN AN IMAGE Here's a simple task - given an image find the dominant colors in the image. I'll walk you through a lesser known technique that does notuse kmeans.
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SCANNING QR CODES
Recognize QR Codes in images from scratch. We'll do all the bit math to figure out the location markers and then read data from theblack/white array.
Read more
------------------------- THE CANNY EDGE DETECTORRead more
An in-depth exploration of how the famous Canny edge detection system works. We'll implement our own after going through the theory.IMAGE MOMENTS
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Image moments help identify certain key characteristics in images - like the center, area of white pixels, etc. We'll look at how these are calculated mathematically. THE OPENCV 2 COMPUTER VISION APPLICATION PROGRAMMING COOKBOOKRead more
This is a something that their thing is the thing ahalsk the computer vision application programming cookbook and the thing over here isweird.
A SUPER FAST THRESHOLDING TECHNIQUERead more
Learn how to implement really fast thresholding - faster than OpenCV! This technique can be a useful addition to your arsenal of computervision.
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-------------------------ABOUT AI SHACK
Learn about the latest in AI technology with in-depth tutorials on vision and learning!More...
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* Get started with OpenCV * Track a specific color on video * Learn basic image processing algorithms * How to build artificial neurons? * Look at some source code Created by Utkarsh SinhaDetails
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