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Finally, we arrive at *classification and interpretation*. This is where the computer uses the extracted features to identify and categorize objects or patterns in the image. This is often done using *machine learning algorithms*, which are trained on labeled data to learn how to recognize different objects or patterns. Once trained, the algorithm can be used to classify new images. *Classification* is essential for a wide range of applications. For example, in medical imaging, it can be used to identify tumors or other abnormalities. In autonomous vehicles, it is used to identify traffic signs, pedestrians, and other vehicles. This step is where the computer translates the data into insights, allowing it to "understand" what it is seeing. It's about turning pixels into knowledge!
