Image processing plays a vital role in computer vision, enabling us to manipulate and analyze images to extract valuable information. It encompasses a range of techniques that allow us to enhance images, detect edges, and segment objects. Let's dive into some key concepts:

1. Image Filtering: Image filtering involves applying a filter or mask to an image to highlight or suppress certain features. Common types include blurring filters for removing noise and sharpening filters for enhancing edges.

2. Edge Detection: Edge detection identifies the boundaries between objects in an image. Popular edge detection algorithms include the Sobel operator, Canny edge detector, and Laplacian of Gaussian.

3. Image Segmentation: Image segmentation involves dividing an image into different regions to simplify its analysis. Techniques like thresholding, region growing, and clustering-based segmentation help in separating objects from the background.

These techniques form the foundation of image processing in computer vision. By applying them, we can preprocess images before further analysis or feature extraction.

Remember, image processing allows us to shape the data and extract important features that are crucial for subsequent computer vision tasks.