While simple thresholding has its limits, adaptive thresholding techniques can increase the versatility of image thresholding operations. Instead of taking a simple global value as a threshold comparison, adaptive thresholding will look in the local neighborhood of the image to determine whether a relative threshold is met. In this way, it is possible to counteract issues such as uneven lighting.
- [Instructor] While simple thresholding…is a powerful algorithm, it has its limits,…such as when there's uneven lighting in an image.…This is where adaptive thresholding comes to the rescue.…This is a technique that can increase the versatility…of image thresholding operations.…Instead of taking a simple global value…as a threshold comparison, adaptive thresholding…will look in its local neighborhood of the image…to determine whether a relative threshold is met.…In this way, it is possible to counteract issues,…such as uneven lighting.…To try this out, we're going to open up…the Chapter Three Module Three file…and start off my importing our Sudoku image,…typing img = cv2.imread.…
We'll read in sudoku.png,…and then again, we're going to signify a 0,…saying that we want a black and white image.…You'll find this is very customary…that we only work with black and white images…when doing segmentation.…Next, we can go ahead and see what this image looks like…using cv2.imshow Original,img.…Next, let's do a basic thresholding to compare against.…
AuthorPatrick W. Crawford
- Installing and configuring OpenCV
- Data types and structures
- Image types
- Manipulating pixels
- Scaling and rotating images
- Using video inputs
- Creating custom interfaces
- Object detection
- Face and feature detection
- Template matching
Skill Level Intermediate
NumPy Data Science Essential Trainingwith Charles Kelly3h 54m Intermediate
Python: Programming Efficientlywith Michele Vallisneri2h 15m Intermediate
1. Install and Configure OpenCV
2. Basic Image Operations
3. Object Detection
4. Face and Feature Detection
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