Small Target Recognition Based on Ridgelet Wavelet Decomposition and Integer Wavelet Decomposition

My undergraduate graduation project program represents an engaging and challenging endeavor focused on developing and implementing small target recognition technology using ridgelet wavelet decomposition and integer wavelet decomposition methods. Through this project, I mastered how to apply these wavelet decomposition techniques to extract and analyze small targets within images, successfully implementing functionality for identifying and locating these targets. The implementation involved creating algorithms that process image data through wavelet transforms, where ridgelet decomposition handles directional features while integer wavelet decomposition ensures lossless processing for precise target extraction. This project extends beyond a simple application to include in-depth algorithm research and optimization to ensure recognition accuracy and efficiency. The code architecture includes modules for image preprocessing, wavelet coefficient calculation, feature extraction, and target classification. Key functions implemented involve thresholding techniques for noise reduction and pattern matching algorithms for target identification. Throughout the development process, I gained proficiency in using various programming languages and tools to implement the program, continuously refining and optimizing its performance during debugging and testing phases. Overall, this undergraduate graduation project provided me with a valuable opportunity to deeply research and practice knowledge in image processing and pattern recognition fields, while demonstrating my programming capabilities and problem-solving skills through the implementation of sophisticated wavelet-based recognition algorithms.