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所在平台: Udemy |
课程主页: https://www.udemy.com/course/image-thresholding-and-masking/
课程评论：没有评论
课程名称：图像阈值和遮罩基础 课程概述： 本课程将教授您如何使用 OpenCV 和 Python 这一流行编程语言来处理图像。您将建立坚实的图像处理基础，理解图像如何被处理为 RGB 像素强度数组。课程将介绍阈值处理的基础知识，并演示如何利用 OpenCV 和 Python 进行阈值处理，以创建素描效果，提取前景和背景对象。我们将使用 Jupyter Notebook 进行编程，提供循序渐进的说明和丰富的示例。 掌握图像阈值作为一项强大实用的技能。Python、OpenCV 和 Jupyter 都是免费、易学且文档齐全的工具。图像阈值在日常应用中无处不在，例如边缘检测和广告图像质量改进。它也是机器学习计算机视觉应用的前提。图像处理领域的就业机会众多，学习 OpenCV 和 Python 将为您提供强大的竞争优势。 图像阈值任务正变得越来越流行。亚马逊、沃尔玛、谷歌等电子商务网站是图像阈值应用的知名范例。卷积神经网络 (CNN) 使用这些技术来寻找前景和背景对象。图像处理任务在信息检索和计算机视觉应用中至关重要。大型广告公司和好莱坞工作室已在使用图像阈值来改进图像质量。 本课程将指导您如何使用 OpenCV、Python 和 Jupyter 框架来平滑图像。您将跟随讲师一步一步构建项目，学习以下内容： * 图像阈值简介 * 如何将阈值处理应用于图像 * 使用 OpenCV 和 Python 分步构建 Jupyter Notebook * 学习双边阈值、高斯模糊、中值模糊和平均模糊等效果。 您将从课程中获得： * 从专业讲师处学习图像阈值基础知识，并亲手完成图像阈值任务。 * 超过 10 节课程，教授如何使用 OpenCV 和 Python 进行图像阈值处理。 * 适合初学者程序员，以及通过实际演示学习更快的用户。 * 采用视觉训练方法，提高学员的记忆力和学习速度。 * 将最复杂的应用分解为简单的步骤。 * 提供挑战题以巩固概念，并提供解决方案进行验证。 课程大纲：无
Course DescriptionLearn to process images by learning fundamentals of image thresholding using opencv and popular programming language Python.Build a strong foundation in Image Processing with this tutorial for beginners.Understanding of how images are processed as array of RGB pixel intensitiesLearn basics of thresholding Leverage OpenCV and Python to perform thresholding to create effects like sketch and extract foreground and background objectsUser Jupyter Notebook for programmingUse step by step instructions along with plenty of examplesA Powerful Skill at Your Fingertips Learning the fundamentals of image thresholding puts a powerful and very useful tool at your fingertips. Python, opencv and Jupyter are free, easy to learn, has excellent documentation.Image thresholding is ubiquitous in everyday applications such as edge detection, advertisement image quality improvements. Its also pre-requisite for computer vision applications using machine learning.Jobs in image processing area are plentiful, and being able to learn opencv and python will give you a strong edge.Image thresholding tasks are becoming very popular. Amazon, Walmart, Google eCommerce websites are few famous example of image thresholding in action. Convolutional neural network (CNN) uses these techniques to find foreground and background objects. Image processing tasks are vital in information retrieval and computer vision applications. Big advertising companies and Hollywood studios already using image thresholding in improving image quality. Content and Overview This course teaches you on how to smooth images using opencv, python and Jupyter framework. You will work along with me step by step to build following answersIntroduction to image thresholdingLearn how to apply thresholding to image Build an jupyter notebook step by step using opencv and python and learn effects like bilateral thresholding, gaussian blur, median blur and average blur.What am I going to get from this course?Learn fundamentals of image thresholding and build image thresholding tasks from professional trainer from your own desk.Over 10 lectures teaching you how to perform image thresholding using opencv and pythonSuitable for beginner programmers and ideal for users who learn faster when shown.Visual training method, offering users increased retention and accelerated learning.Breaks even the most complex applications down into simplistic steps.Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.
