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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/features-and-boundaries
课程评论：没有评论
课程名称：特征与边界 课程概述：本课程聚焦于图像中特征与边界的检测。特征和边界检测是许多视觉任务的关键预处理步骤，包括物体检测、物体识别和计量学（即测量物体的物理尺寸及其他属性）。课程介绍了多种检测特征和边界的方法，并展示了如何利用从图像中提取的特征来解决重要的视觉任务。 课程内容从简单且重要的特征检测开始，例如边缘和角点。我们展示了如何利用基于图像一阶和二阶导数的算子，可靠地检测出这些特征。接下来，我们探索了“兴趣点”的概念——即图像中的独特且有用的局部外观。我们描述了如何使用SIFT检测器稳健地检测兴趣点。借助该检测器，我们提供了一种从重叠图像拼接的端到端解决方案，以获得广角全景图。最后，我们讨论了在图像中找到人脸的重要问题，并展示了人脸检测的多个应用。 课程大纲： 1. 入门：特征与边界 2. 边缘检测 3. 边界检测 4. SIFT检测器 5. 图像拼接 6. 人脸检测
Name:Getting Started: Features and Boundaries
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Name:Edge Detection
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Name:Boundary Detection
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Name:SIFT Detector
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Name:Image Stitching
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Name:Face Detection
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This course focuses on the detection of features and boundaries in images. Feature and boundary detection is a critical preprocessing step for a variety of vision tasks including object detection, object recognition and metrology – the measurement of the physical dimensions and other properties of objects. The course presents a variety of methods for detecting features and boundaries and shows how features extracted from an image can be used to solve important vision tasks. We begin with the detection of simple but important features such as edges and corners. We show that such features can be reliably detected using operators that are based on the first and second derivatives of images. Next, we explore the concept of an “interest point” – a unique and hence useful local appearance in an image. We describe how interest points can be robustly detected using the SIFT detector. Using this detector, we describe an end-to-end solution to the problem of stitching overlapping images of a scene to obtain a wide-angle panorama. Finally, we describe the important problem of finding faces in images and show several applications of face detection.
