It took me a lot of time to decide what is the best intro for this article. I should have published this article more than a year ago and I just want to say:
This article is a continuation to the first one. Please go back and check it out if you have not already, since it heavily depends on it.
We are going to cover the following in this article:
1. Implementation for Sobel filter through X axis (Vertically).
2. Implementation for Sobel filter through Y axis (Horizontally).
3.Implementation for Sobel filter through X and Y together.
The main usage of Sobel filter is for Edge Detection. It does that by calculating the gradient of the image at each pixel which results in finding the largest increase from light to dark pixels and the rate of change. Edge can be defined as a set of contiguous pixel positions where a sudden change of the intensity values occur in the pixel. …
Tens of good tutorials are available online for image processing using OpenCV library. To name just a few of the cool functions in this library, there are cvtColor and filter2D. The Former, converts your image from one color space to another, whereas the latter convolves an image with a specific kernel.
While these libraries definitely make the life of a Computer Vision Engineer easier, it is not always enough to get the job done. I’ll let you in on a secret: In order to get a position as a Computer Vision or Image Processing Engineer, you would need to go through multiple tough interviews that may destroy your life or cause you to feel failure but I want you to know that you are certainly NOT FAILING. You just need to practice. Oh! …
Since you are here, I suppose that you have gone through Part1 and Part2 of this series and, hopefully, found them beneficial. As I promised in part 2 of this series, here is a new article which will help you prepare for your next Computer Vision/Image Processing coding interview.
The main two sections of this article are:
1. Implementation for Gaussian blur with support to gray scale and colored images.
Being sick over the past two days prevented me from going to work. Therefore, I decided to play with OpenCV on iOS and I ended up with this project, which I want to share with you guys :)
The idea is to use Haar feature-based cascade classifiers, which is implemented in OpenCV and used for face detection. Nothing new, I have just put the pieces together and reached the results below.
In this article, you can find all steps for this to work. I also provided a link to the source I used with each step.
The main steps for this…