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Image Processing

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Abonia Sojasingarayar edited this page Jan 30, 2019 · 1 revision

sequence of image processing events:

Input image →

  1. Greyscale image
  2. Gaussian Blur
  3. Canny edge detection
  4. Mask edges image
  5. Hough lines → Lane line output

Simple explanation about my understanding of input image manipulation:

It’s important to remember that an image is nothing more than a bunch of pixels arranged in a rectangle. This particular rectangle is 960 pixels by 540 pixels.

The value of each pixel is some combination of red, green, and blue, and is represented by a triplet of numbers, where each number corresponds to the value of one of the colors. The value of each of the colors can range from 0 to 255, where 0 is the complete absence of the color and 255 is 100% intensity.

For example, the color white is represented as (255, 255, 255) and the color black is represented as (0, 0, 0).

So, this input image can be described by 960 x 540 = 518,400 triplets of numbers ranging from (0, 0, 0) to (255, 255, 255).

Now that this image is just a collection of numbers, we can start manipulating these numbers in useful ways using math.

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