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One of the main techniques is *feature extraction*. Algorithms identify key features in an image, such as edges, corners, and textures. These features act as fingerprints for the objects. Then, *machine learning* is used to train algorithms to recognize objects based on the extracted features. Convolutional neural networks (CNNs) are particularly well-suited for object recognition due to their ability to learn complex patterns in images. Object recognition is essential for *self-driving cars*. It helps vehicles recognize other cars, pedestrians, traffic signs, and other elements of the road. In *medical imaging*, object recognition is used to detect and diagnose diseases by identifying abnormalities in medical images. In *security and surveillance*, object recognition is used to identify suspicious activities or potential threats in real time.
