While this course covers a number of algorithms and techniques possible through the OpenCV library, there are numerous other algorithms and capabilities. Patrick introduces a few of these other techniques that may be useful for further exploration after this course.
- [Narrator] From a computer vision standpoint,…we have only scratched the surface…with the topics covered so far.…Let's take a moment to briefly look…at some other algorithms in the field.…One of those applications we've already seen briefly…under the hood is machine learning.…Specifically, we have been looking…at supervised machine learning.…This is a form of machine-based learning…where you train a classifier using already-tagged…or identified data.…For example, you start a pool of images…that is an apple and then a pool of images…which are not an apple.…The classifier then builds little tests,…extracting features from the image.…
And for each of those tests, it is evaluated…of how well it indicates it being…one image object or another.…When it comes to supervised machine learning,…an important concept is the confusion matrix.…The idea is that you can evaluate the effectiveness…of your classifier or machine learning data…by testing it against a set of images…that were not used in the training process.…Here you can see that the true positives…
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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