Conclusion

When to use traditional computer vision vs. machine learning?

The various techniques we studied in this course can be divided into two broad categories:

• Traditional computer vision

• Machine learning

Traditional computer vision

By traditional computer vision, we mean the set of programming techniques that accomplish a computer vision task step-by-step, using low-level functions supplied by packages such as OpenCV and NumPy. It doesn’t require a large number of example images; a handful of images is enough to build most of the code.

Traditional computer vision has the disadvantage of requiring manually optimized parameters. For a novice practitioner, this optimization process is often difficult. When a large sample of annotated images is available, e.g., greater than 300, a machine-learning algorithm can optimize the parameter values. Genetic algorithms are a good choice for this specific scenario. If we don’t have access to a large image dataset, we must turn to real-time optimization, allowing the automated inspection system to analyze the images in production. By saving the images and their inspection results, we can identify the corner ...