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The framework we will be working with is called the Open Computer Vision Library or OpenCV for short. The first thing you’re going to need to do is to get a reference to your camera and assign it to an object of type CvCapture.
To keep the window from closing, we will use a while loop that listens for any key to be pressed which will then close our window. If everything worked perfectly, you should be able to hook up your webcam and see something like the image below. If you have any problems, questions, or suggestions, be sure to leave them in the comments below. If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. The purpose of edge detection in general is to significantly reduce the amount of data in an image, while preserving the structural properties to be used for further image processing.
With Canny’s mathematical formulation of these criteria, Canny’s Edge Detector is optimal for a certain class of edges (known as step edges).
The readers are advised to do more research on canny edge detection method for detailed theory. Another point that I want to make is that Math.Atan returns value between -90 to 90, so you actually don't need the second condition for all your if statements.
In summary, I believe the nonmax suppression in the original code suppresses in the wrong direction, which explains why the edge image in the example looks fuzzy. We have a particular edge detection algorithm that was developed for the OpenIllusionist project specifically to help with detecting fiducials in live video since it was faster and better suited to the task than algorithms such as the canny edge detector. The porting of this algorithm to a GIMP plugin was not simple, since GIMP plugins usually make use of tile-based processing thereby reducing the overall memory required to handle very large images. One problem that this particular algorithm attempts to address is that of edge ringing in images, especially those captured from low-quality cameras or affected by compression artefacts.
The code for the plugin along with a binary for Linux x64 can be found in my github plugins repository and further examples of the algorithm in action are shown below.
By obtaining only the edges many computer algorithms become possible, as edges contain the majority (or at least a lot) of information in a scene.
When our brains try to make sense of the scene we know there is grass, and it’s pretty uniform.
That is why it is such an important concept to computer vision. By reducing an image to edges, it becomes much easier for many algorithms to identify, learn, or process a scene. The difficulty here, being the removal everything that isn’t a useful edge, but leaving the appropriate edges.
One of the most basic filter in edge detection is the gaussian filter, there are others, but that is what I’ll use throughout this article. You can see that the image is blurred, the gaussian filter took every pixel, and made a component of the pixel value based off it’s neighbors. In order to make this useful in edge detection, we can take the derivative of the gaussian in terms of x and y [1].
When you take the derivative with respect to the x and y components of a gaussian a large peak and trough is created.
That’s it, one lines can get you a gaussian, then take the derivative with respect to x and y.
What’s interesting, is we can also apply this method to RGB images and get colored edges as well.
The both images should represent the difference in color between a pixel and it’s neighbor, the color image just has three channels RGB and the black and white image has only one channel. Essentially, we take our gaussian filter and angle it in different directions to create different magnitudes based on the angle of the edge associated with the gaussian. The various gaussians produce filters for 90, 45, -45, and 22.5 degrees, with respect to the x or y components. The code I used above was almost the exact same as the single filter, but instead I merged them differently. There isn’t that much of a difference between the oriented and non-oriented filters, but it should be possible to see a small improvement with the multiple orientations. Over the past two years I have done a ton of testing and experimentation in different color spaces. The reason I am fascinated with the Lab color is it’s excellent ability to generate edges in a scene.



In fact, these color channels are perfect for finding gradients, as yellow seems to rarely appear next to yellow naturally, and similarly for red and green (although I have yet to prove this outright). To put this into action takes minimal extra coding, all we need to do is convert the input image into the Lab color space.
Then if we apply the filters (without non-maxima suppression) we get the following, a clear visual of what is grass, clouds, and the dividing line between grass and sky. Finally, if we run non-maxima suppression we get a much better representation of the edges than the Canny edge detector at the beginning of the article. On average, this method will improve the precision of edge detection of a basic method by about 10%. There are infinitely many ways to do edge detection, and the methods described here are by no means the best, easiest to implement, or even easiest to explain.
I have made all of my implementation (minute a few key components for the course) available on my Github, including an OpenCV implementation in C++. I like messing around with different eye tracking, head tracking, and pretty much any other kind of motion tracking systems. Inside our while – loop, we will need to setup a few variables which will house the captured frames from our camera and convert them to gray scale. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Several algorithms exists, and this worksheet focuses on a particular one developed by John F.
Detection: The probability of detecting real edge points should be maximized while the probability of falsely detecting non-edge points should be minimized. Number of responses: One real edge should not result in more than one detected edge (one can argue that this is implicitly included in the first requirement). Finding gradients: The edges should be marked where the gradients of the image haslarge magnitudes. I will explain in as much details as I can so everyone can understand exactly what's happening.
If the angle for a particular pixel is 0, it means dy is 0 and dx can be +ve or -ve (from the definition of tangent).
Other conditions can be deduced using similar reasoning except one should be careful with +ve and -45 degrees. In fact, if you have an angle that's greater than 90, say 120 (assuming 0 is the east direction), atan will return -60 instead. However, as can be seen in the above diagram, the adaptive threshold edge detector uses many full and scaled down buffers to produce the final result and as such cannot easily be modified to work with tiles. With regards to the lack of anti-aliasing, this is due to the fact that the algorithm was designed for real-time machine vision and is therefore intended to be used in conjunction with other forms of analysis.
It’s a pretty straight forward idea, running edge detection on an image should output only edges, similar to a sketch.
This may sound counter intuitive, or way over your head, but if we view images of the dervatives of a gaussian it becomes more clear.
If you know derivatives and take a second to consider why this is the case, it should come to you pretty quickly. We also use non-maxima suppression, which is the process of setting pixels to zero if they are not part of the local maxima.
For example, if we angle our gaussian at a 45 degree angle and it on an image with an edge at 45 degree angle it should have a much higher magnitude, than if we used a gaussian at a zero degree with respect to x or y. This produces a variety of different magnitudes of edges, though each filter should detect nearly all of the same edges. Specifically, the Lab color space, which put simply, is a different way of describing image. The Lab color space has a strong correlation with how we, as humans, perceive luminance in color. There are optimizations you can make, but just doing that one extra step can significantly improve appropriate edge detection. This was determined by running F-measure tests against The Berkeley Segmentation Dataset and Benchmark. Now that I have a Microsoft Kinect, most of the applications I write for motion tracking are designed for the Kinect. It is a free, open source library that contains over 500 algorithms for capturing and tracking motion and can be used for many different applications.


For this example, we’ll only be using the camera index which is 2 in my case since my camera is plugged into my second USB port. We will also be applying a canny filter that will cause our video to appear like it was hand drawn, but in real time. If your computer is a little slow or you just want to mess around a bit, try changing the thresholds (the 3rd and 4th parameters) of your Canny filter. At some point, I’ll even show you how to render images over your videos so that you can create your own animated videos using overlays and do it all in real time. The direction of dx and dy is hidden in the definition of the Sobel operator where dx is the horizontal (+ve->east, -ve->west) direction and dy is the vertical (+ve->south, -ve->north) direction.
The +1 or -1 pixels is determined based on the fact that +dx is east and +dy is south (which again is determined inherently from the sobel operator). This turns out to be correct for our purpose since -60 and 120 is actually on the same line (through origin) and non-max suppression only cares about the pixels on either side of the line. If you find my explanation confusing or unnecessarily detailed, then maybe wikipedia can help you understand better!
The current implementation of my plugin simply uses full image regions and therefore the user must be aware that it may have issues if applied to very large images. For example, the fiducial detector in the OpenIllusionist project performs grouping and then ellipse fitting on the detected line segments.Since the implementation of the edge detection as a GIMP plugin was intended to aid us in the prototyping of machine vision algorithms it does not make sense to modify it to produce more aesthetically pleasing results. My goal is to explain, not only how edge detection works very clearly, but also to provide a new and easy way to improve edge detection significantly with minimal work!
This accomplishes a blending, making the center pixel be minimally based off its neighbors. Due to large changes in the magnitude of the gaussian coming from the peak, a peak and trough occurs. This method (more-or-less) comes from a paper by Freeman and Adelson titled The design and use of steerable filters [2], the idea being we can aim our gaussian filter in several direction.
In the Lab color space, luminance is its own separate channel, making it better to handle differences in color as opposed to RGB, where luminance is connected to color.
Some of the coolest applications that use OpenCV are human machine interfaces, realtime video processing, and even robotics. After you have an object for your camera, you’ll need to initialize a window for us to render the camera captures into. Changing these numbers can cause your images to become sharper and more clear or it can become more grainy and a little more fuzzy. Plus, if you ask nicely, I might even show you how to use OpenCvSharp for doing real time lane detection by mounting a camera inside your car and hitting the streets. Even though it is quite old, it has become one of the standard edge detection methods and it is still used in research.
This means that this a VERTICAL edge even though all pixels have angles 0 (in the HORIZONTAL direction).
Similarly, when we are trying to find edges, we are trying to remove everything except the edges. Rather than diving into thresholding in Canny edge, let’s be a bit more general and build a few filters!
So, I want to take a few minutes to introduce you to one of my favorite motion tracking frameworks and share with you some example code for putting it to use for yourself. Although OpenCV is written in C++ and Python, there are wrappers available for other languages.
Therefore, when we want to to non max suppression to a pixels with angle 0, we're looking at a vertical edge and we should compare its gradient with pixels to the left or right. The example I’m going to show you will transform an ordinary USB webcam into a pretty cool video capturing tool. But for now, let’s see what it takes to capture video with our webcam and free framework.



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