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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/cameraandimaging
课程评论：没有评论
课程名称：摄影与成像 课程概述：本课程涵盖成像的基本原理——生成可供人类或机器消费或处理的图像。成像有着悠久的历史，跨越了几个世纪。但在过去三十年里，所取得的进展彻底改变了相机，并显著提高了计算机视觉系统的稳健性和准确性。本课程将介绍成像的基本知识，以及近年来在成像领域所取得的创新，这些创新对计算机视觉产生了深远的影响。 本课程从考察通过镜头相机形成图像的过程开始。我们探讨相机的光学特性，如放大倍率、F值、景深和视场。接下来，我们描述固态图像传感器（CCD和CMOS）如何记录图像，以及图像传感器的关键属性，如分辨率、噪声特性和动态范围。我们还讲述图像传感器如何用于感知颜色以及捕获高动态范围图像。在某些结构化环境中，可以通过阈值处理生成二值图像，从中计算物体的各种几何特性，并用于识别和定位物体。最后，我们介绍图像处理的基本原理——开发计算工具来处理捕获的图像，使其更清晰（去噪、去模糊等），并方便计算机视觉系统进行分析（线性与非线性图像滤波方法）。 课程大纲： 1. 开始：摄影与成像 2. 图像形成 3. 图像感知 4. 二值图像 5. 图像处理 I 6. 图像处理 II
Name:Getting Started: Camera and Imaging
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Name:Image Formation
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Name:Image Sensing
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Name:Binary Images
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Name:Image Processing I
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Name:Image Processing II
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This course covers the fundamentals of imaging – the creation of an image that is ready for consumption or processing by a human or a machine. Imaging has a long history, spanning several centuries. But the advances made in the last three decades have revolutionized the camera and dramatically improved the robustness and accuracy of computer vision systems. We describe the fundamentals of imaging, as well as recent innovations in imaging that have had a profound impact on computer vision. This course starts with examining how an image is formed using a lens camera. We explore the optical characteristics of a camera such as its magnification, F-number, depth of field and field of view. Next, we describe how solid-state image sensors (CCD and CMOS) record images, and the key properties of an image sensor such as its resolution, noise characteristics and dynamic range. We describe how image sensors can be used to sense color as well as capture images with high dynamic range. In certain structured environments, an image can be thresholded to produce a binary image from which various geometric properties of objects can be computed and used for recognizing and locating objects. Finally, we present the fundamentals of image processing – the development of computational tools to process a captured image to make it cleaner (denoising, deblurring, etc.) and easier for computer vision systems to analyze (linear and non-linear image filtering methods).
