Deep Learning, Machine Learning -- Digital Signal and Image Processing: From Theory to Application

Autor:	Hsieh, Weiche, Bi, Ziqian, Liu, Junyu, Peng, Benji, Zhang, Sen, Pan, Xuanhe, Xu, Jiawei, Wang, Jinlang, Chen, Keyu, Yin, Caitlyn Heqi, Feng, Pohsun, Wen, Yizhu, Wang, Tianyang, Li, Ming, Ren, Jintao, Niu, Qian, Chen, Silin, Liu, Ming
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Graphics Electrical Engineering and Systems Science - Image and Video Processing Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	Digital Signal Processing (DSP) and Digital Image Processing (DIP) with Machine Learning (ML) and Deep Learning (DL) are popular research areas in Computer Vision and related fields. We highlight transformative applications in image enhancement, filtering techniques, and pattern recognition. By integrating frameworks like the Discrete Fourier Transform (DFT), Z-Transform, and Fourier Transform methods, we enable robust data manipulation and feature extraction essential for AI-driven tasks. Using Python, we implement algorithms that optimize real-time data processing, forming a foundation for scalable, high-performance solutions in computer vision. This work illustrates the potential of ML and DL to advance DSP and DIP methodologies, contributing to artificial intelligence, automated feature extraction, and applications across diverse domains. Comment: 293 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.20304 Zobrazit plný text záznamu View this record from Arxiv