Fourier Descriptors with Translation and Rotation Invariance
Fourier descriptors providing translation and rotation invariance, suitable for object recognition with robust feature extraction capabilities
Fingerprint Recognition Algorithm Implementation Using MATLAB
With the continuous advancement of biometric technology, it has been discovered that each individual's fingerprint possesses uniqueness and permanence. Consequently, fingerprint recognition technology has evolved into a novel identity authentication method, demonstrating strong potential to replace traditional identification approaches due to its excellent security and reliability. This article systematically outlines the fundamental steps of fingerprint recognition: fingerprint image preprocessing, feature extraction, and fingerprint matching. The preprocessing phase covers normalization, image enhancement, binarization, and thinning techniques, ultimately producing a refined binary image with single-pixel width. Fingerprint matching is then performed by analyzing distinctive endpoint and crossover point features. The complete algorithmic pipeline is implemented through MATLAB programming, providing practical insights into image processing operations and pattern recognition methodologies.
Extraction of Normalized Central Moments - Excellent Invariance to Translation and Scaling
Extracting normalized central moments demonstrates superior invariance to translation and scaling transformations. This method appears simpler and more reliable compared to Hu moments for computer vision applications. Please correct me if any technical inaccuracies exist. Contact qq254730570 for further discussion.