OpenCV: The most popular image processing library, supporting operations such as object detection, filtering, morphological operations, and more. PIL (Pillow): Used for image manipulation, providing basic image processing functions.

Images are represented as matrices of pixels. Colored images are often represented with three separate matrices in the RGB format. Matrix manipulation and the use of the NumPy library.

Filters are used to emphasize or hide specific features in an image. Example filters: Gaussian, Sobel, Median.

Adjusting contrast and brightness by analyzing pixel intensities in the image. Histogram equalization.

Used to change the shape and structure of objects in an image. Operations like erosion, dilation, opening, and closing.

Detection of objects using algorithms like YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), Faster R-CNN. Integration with libraries like OpenCV and TensorFlow.

Using CNN (Convolutional Neural Network) architectures for image classification, object detection, and segmentation. Deep learning frameworks like TensorFlow and PyTorch.

Medical imaging, face recognition, applications in the automotive industry for autonomous vehicles, and security systems.

Developing personal projects to gain practical skills.

Examples include face recognition, digit recognition, or color detection applications. Image processing with Python covers a broad range of topics, and there are many applications in various fields. Starting from the basics, practicing through projects, and advancing to more complex topics will help enhance your skills in this area.