Integrating image processing with machine learning consists of following steps:

1. Decide your project title/purpose/objectives.

2. Collect data, i.e images relevant to your project.

3. Preprocess all the collected images. Preprocessing steps may be different for different projects. It basically includes filtering, noise removal, grayscale conversion, binary image formation, morphological operation, thresholding and so on.

4. Divide the preprocessed images into two sets: training and tesing sets.

5. Extract features that you think will most probably define your data. (For example, if you are working on a face detection project, one possible feature could be the distance between the two eyes. You can think of a number of such features) (very important step)

6.Think of a suitable machine learning algorithm. Since you said you are familiar with machine learning, I assume you must have come across some basic ML algorithms.

1. Build a model, i.e, write a program that trains your training data with the ML algorithm. Now you will get a model.

8.Test your model with the testing data and find out how accurate your model is