Hu Moments for Video and Image Feature Extraction
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This text demonstrates how Hu moments can be utilized for feature extraction in videos or images, resulting in outstanding recognition performance. Hu moments represent a method for describing contour shapes, capable of extracting key features from images or videos such as edges, corners, and texture patterns. In implementation, Hu moments are calculated from central moments that are normalized and made scale-invariant. The seven Hu moment invariants are derived using nonlinear combinations of normalized central moments, providing rotation, scale, and translation invariance. Through analysis and comparison of these features, different objects or scenes can be accurately identified. Typically, the cv2.HuMoments() function in OpenCV computes these invariants from central moments. Therefore, employing Hu moments for feature extraction proves to be an effective approach that enables more accurate and reliable image or video recognition systems.
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