From the course: OpenCV for Python Developers

Unlock the full course today

Join today to access over 24,400 courses taught by industry experts.

Object detection overview

Object detection overview

From the course: OpenCV for Python Developers

Start my 1-month free trial Buy for my team

Object detection overview

“

- [Instructor] In this chapter, we reviewed a few ways to approach segmenting out objects in an image and detecting properties of those objects. A few areas we looked at included both simple and adaptive thresholding, using edges to help break down apart closely fitting objects. We also briefly looked at how to composite multiple thresholds of different types together, and in the last chapter we saw how to use Gaussian blurs to reduce noise and dilation and erosion filters to reduce small speckles or gaps. These are just some of the image processing tools helpful in segmenting out objects. It's important to keep in mind the context, know what the application will be used for, and develop segmentation that will fit the use case. Do you know that your lighting will always stay roughly the same with different image inputs? If so, it may be more effective to use non-adaptive thresholding. Perhaps you can improve your…

Contents