Computer Vision Algorithms and Applications
Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive
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Introduction 1.1 1.2 1.3 1.4 1.5 1.6
What is computer vision? A brief history . . . . . . Book overview . . . . . Sample syllabus . . . . . A note on notation . . . Additional reading . . .
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Figure 1.1 The human visual system has no problem interpreting the subtle variations in translucency and shading in this photograph and correctly segmenting the object from its background.
R. Szeliski, Computer Vision: Algorithms and Applications, Texts in Computer Science, DOI 10.1007/978-1-84882-935-0_1, © Springer-Verlag London Limited 2011
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1 Introduction
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Figure 1.2 Some examples of computer vision algorithms and applications. (a) Structure from motion algorithms can reconstruct a sparse 3D point model of a large complex scene from hundreds of partially overlapping phoc 2006 ACM. (b) Stereo matching algorithms can build a detailed 3D tographs (Snavely, Seitz, and Szeliski 2006) model of a building fac¸ade from hundreds of differently exposed photographs taken from the Internet (Goesele, c 2007 IEEE. (c) Person tracking algorithms can track a person walking in front Snavely, Curless et al. 2007) c 2000 Springer. (d) Face detection algorithms, of a cluttered background (Sidenbladh, Black, and Fleet 2000) coupled with color-based clothing and hair detection algorithms, can locate and recognize the individuals in this c 2006 Springer. image (Sivic, Zitnick, and Szeliski 2006)
1.1 What is computer vision?
1.1 What is computer vision? As humans, we perceive the three-dimensional structure of the world around us with apparent ease. Think of how vivid the three-dimensional percept is when you look at a vase of flowers sitting on the table next to you. You can tell the shape and translucency of each petal through the subtle patterns of light and shading that play across its surface and effortlessly segment each flower from the background of the scene (Figure 1.1). Looking at a framed group portrait, you can easily count (and name) all of the people in the picture and even guess at their emotions from their facial appearance. Perceptual psychologists have spent decades trying to understand how the visual system works and, even though they can devise optical illusions1 to tease apart some of its principles (Figure 1.3), a complete solution to this puzzle remains elusive (Marr 1982; Palmer 1999; Livingstone 2008). Researchers in computer vision have been developing, in parallel, mathematical techniques for recovering the three-dimensional shape and appearance of objects in imagery. We now have reliable techniques for accurately computing a partial 3D model of an environment from thousands of partially overlapping photograp
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