图像和视频是人类记录、表达和传递外部世界的重要视觉载体,也是感知外部世界的视觉基础,图像处理是实现物联网、机器视觉和人工智能等相关应用的基本支撑技术。
本课程将从如下八个方面来讲授图像处理的一些基本概念,方法与技术:
1)图像表征(依据图像基信号是否基于数据驱动,介绍图像处理中经典的傅里叶变换,离散余弦变换和基于数据驱动的主元分析法,生动展现同一幅图像在不同变换下的形式)
2)运动估计(分别介绍像素级别的光流法和图像块级别的块匹配算法原理,以及它们应用差异之所在)
3)图像与视频压缩技术(分别介绍包括静态数字图像压缩标准JPEG和视频压缩MPEG原理)
4)图像半色调技术(介绍包括最简单的阈值方法,用于印刷业的聚合型抖动模板和分散型抖动模板,误差传播法等)
5)图像滤波技术(介绍图像中常见的噪声类型,传统图像滤波,如中值滤波和高斯滤波等,以及最近出现针对纹理的滤波方法)
6)图像插值与超分辨率技术(介绍包括传统图像插值方法,和基于图像自相似图像超分辨率技术等)
7)图像边缘检测与分割技术(介绍包括Canny算子,mean-shift图像色彩分割方法等)
8)视频目标跟踪技术(介绍目前较热门的Discriminative Correlation Filter(DCF)的目标跟踪技术原理)。
在授课过程中我们通过理论与实践相结合方式,以及课后大量文献阅读来加深对图像处理基本概念和理论的理解;通过实例来分析比较不同图像处理方法的优缺点;通过提出问题来引导学生独立深入思考。
课程的目标是通过学习,能让学员掌握图像处理与计算机视觉中一些基本概念,基本研究思路和方法等,从而帮助他们展开相关领域后续深入的研究工作,和开发相关应用系统等。
线性代数,信号与系统,高等数学,统计概率论等。
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完成课程教学内容学习和考核,成绩达到课程考核标准的学生(每门课程的考核标准不同,详见课程内的评分标准),具备申请认证证书资格,可在证书申请开放期间(以申请页面显示的时间为准),完成在线付费申请。
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