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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-for-signal-and-image-processing-master-class-2023/
课程评论：没有评论
《Python 信号与图像处理大师班》是一门旨在弥合信号与图像处理算法的理论与 Python 实现之间差距的课程。本课程提供所有讲义和 Python 代码。 **课程亮点：** * **信号处理的重要性：** 课程将探讨数字信号处理在工程和科学领域的广泛应用，包括机器学习、数据分析、计算机视觉、通信系统、金融等。信号处理涉及对信号的基本性质进行操作，以获得期望的输出，并关注信号的数字表示和处理。 * **图像处理的应用：** 课程将详细介绍图像处理在深度学习、计算机视觉、医学成像、机器人、计算机图形学、人脸检测、遥感、农业食品等众多领域的应用。 * **Python 实现：** 课程强调使用 Python 实现各类信号与图像处理算法，帮助学习者将理论知识付诸实践。 **课程大纲：** 本课程涵盖了从基础到高级的广泛主题，包括： * **Python 基础：** 快速入门 Python 编程。 * **信号处理基础：** 学习信号处理的基本概念，如卷积、信号去噪。 * **复数与傅里叶变换：** 掌握复数运算和傅里叶变换在信号分析中的应用。 * **滤波器设计：** 学习 FIR 和 IIR 滤波器的设计方法。 * **小波变换：** 探索一维信号的小波变换。 * **图像处理基础：** 介绍图像处理的基本原理，并使用 NumPy、Matplotlib 和 OpenCV 进行实际操作。 * **图像操作：** 学习图像的算术、逻辑、几何变换以及点/灰度级变换。 * **图像增强：** 深入了解直方图处理和空间域/频率域滤波技术。 * **形态学处理：** 学习图像的形态学操作。 * **图像小波变换：** 探索图像的小波变换应用。 * **工具使用：** 课程还将介绍 Google Colab 的使用。
This course will bridge the gap between the theory and implementation of Signal and Image Processing Algorithms and their implementation in Python. All the lecture slides and python codes are provided.Why Signal Processing?Since the availability of digital computers in the 1970s, digital signal processing has found its way in all sections of engineering and sciences.Signal processing is the manipulation of the basic nature of a signal to get the desired shaping of the signal at the output. It is concerned with the representation of signals by a sequence of numbers or symbols and the processing of these signals.Following areas of sciences and engineering are specially benefitted by rapid growth and advancement in signal processing techniques.1. Machine Learning.2. Data Analysis.3. Computer Vision.4. Image Processing5. Communication Systems.6. Power Electronics.7. Probability and Statistics.8. Time Series Analysis.9. Finance10. Decision TheoryWhy Image Processing?Image Processing has found its applications in numerous fields of Engineering and Sciences.Few of them are the following.1. Deep Learning2. Computer Vision3. Medical Imaging4. Radar Engineering5. Robotics6. Computer Graphics7. Face detection8. Remote Sensing9. Agriculture and food industryCourse OutlineSection 01: Introduction of the courseSection 02: Python crash courseSection 03: Fundamentals of Signal ProcessingSection 04: ConvolutionSection 05: Signal DenoisingSection 06: Complex NumbersSection 07: Fourier TransformSection 08: FIR Filter DesignSection 09: IIR Filter DesignSection 10: Introduction to Google ColabSection 11: Wavelet Transform of a SignalSection 12: Fundamentals of Image ProcessingSection 13: Fundamentals of Image Processing With NumPy and MatplotlibSection 14: Fundamentals of Image Processing with OpenCVSection 15: Arithmetic and Logic Operations with ImagesSection 16: Geometric Operations with ImagesSection 17: Point Level OR Gray level TransformationSection 18: Histogram ProcessingSection 19: Spatial Domain FilteringSection 20: Frequency Domain FilteringSection 21: Morphological ProcessingSection 22: Wavelet Transform of Images
