Machine Learning Techniques for Vision Application

In traditional programming, we will manually write the code based on a well-defined algorithm that takes the input data and provides the desired output data. Machine learning (ML), on the other hand, learns the algorithm from the data without being explicitly programmed. The data can be of any format, but in the domain of computer vision, it primarily refers to images. ML algorithms for vision systems analyze the image data and learn to identify patterns relevant to the task at hand. Over time, the algorithm gains experience and improve its ability to perform the task. The term task here can refer to a wide range of computer vision applications like image classification, object tracking, image segmentation, OCR, etc. This ultimately leads to intelligent systems that are able to learn and adapt by enabling machines to make data-driven predictions and automate complicated decisions. The groundwork for using machine learning with image data in your Raspberry Pi-based vision system will be laid out in this part. As with all the traditional image processing system that we have discussed so far, ML-based image processing systems will also require certain preprocessing steps that can enable the system to learn faster. We will start by discussing these techniques briefly and then begin to explore some of the traditional ML models for image data. We will then delve into deep learning techniques that are better suited to handle image data than the traditional ML techniques.