22. Convolution Operator
The Convolution Operator is a crucial mathematical technique in image processing and AI, particularly within Convolutional Neural Networks (CNNs). It modifies images through filters, enabling feature extraction such as edge detection, which is essential for various applications like facial recognition and medical imaging. Understanding the convolution process, including components like kernels and feature maps, is vital for advancing in AI technologies.
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What we have learnt
- The Convolution Operator modifies images using mathematical filters to extract useful features.
- Different types of filters such as edge detection, sharpening, and blurring can enhance image processing.
- Convolution is widely applied in real-world scenarios such as face recognition, self-driving cars, and medical imaging.
Key Concepts
- -- Convolution Operator
- A mathematical operation that modifies an image or extracts features by applying a filter over it.
- -- Kernel/Filter
- A small matrix used to process an image, highlighting specific features.
- -- Feature Map
- The result of the convolution operation that shows the detected features of an image.
- -- Stride
- The number of pixels the filter moves each time during the convolution operation.
- -- Padding
- Adding extra pixels around an image to maintain its size after applying a filter.
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