计算机视觉算法与应用概览

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"《Computer Vision Algorithms and Applications》是由Richard Szeliski编著的一本关于计算机视觉的专业书籍，适合在本科高年级阶段作为计算机科学和电子工程专业的教学教材。本书深入探讨了计算机视觉领域的算法与应用。" 在计算机视觉这个广泛的领域中，Richard Szeliski的这本书详细介绍了该领域的核心概念、技术和实践应用。它涵盖了从基础的图像处理技术到高级的视觉理解算法。以下是书中可能涉及的一些关键知识点： 1. 图像获取：介绍图像传感器的工作原理，包括数码相机和扫描仪等设备，以及图像的数字表示和色彩模型。 2. 图像处理：涵盖滤波器（如卷积）、边缘检测（如Canny算子、Sobel算子）和图像增强技术，用于改善图像质量和提取特征。 3. 特征检测与描述：如角点检测（Harris角点、Hessian矩阵）、尺度不变特征变换（SIFT）、快速方向梯度直方图（HOG）等，这些特征对于物体识别和匹配至关重要。 4. 目标检测与识别：讲述滑动窗口方法、模板匹配以及基于机器学习的分类器（如支持向量机、随机森林）在目标检测中的应用。 5. 图像分割：包括区域生长、阈值分割、水平集方法等，用于将图像分成有意义的部分或对象。 6. 几何建模与恢复：介绍单目和双目立体视觉，用于计算场景的深度信息和三维重建。 7. 运动分析：涵盖光流估计、摄像机运动估计（如Euler角、欧拉法）和结构从运动（Structure from Motion, SFM）技术。 8. 深度学习与卷积神经网络（CNNs）：讲解如何利用神经网络进行图像分类、物体检测和语义分割，如AlexNet、VGG、ResNet等经典模型。 9. 应用场景：包括自动驾驶、无人机导航、医学图像分析、人脸识别、视频监控等领域。 10. 实践与工具：讨论OpenCV库的使用，这是一个广泛使用的开源计算机视觉库，包含了大量的图像处理和计算机视觉算法。此书不仅提供了理论基础，还强调了实际应用，是学习和教授计算机视觉的理想教材。通过阅读和实践，读者能够掌握计算机视觉的基础知识，并有能力开发自己的视觉系统和应用。同时，书中可能还包括对版权和法律问题的讨论，提醒读者在使用相关技术和内容时遵守法律法规。

Contents

A.1.3 QR factorization ............................649

A.1.4 Cholesky factorization .........................650

A.2 Linear least squares ...............................651

A.2.1 Total least squares ...........................653

A.3 Non-linear least squares .............................654

A.4 Direct sparse matrix techniques .........................655

A.4.1 Variable reordering ...........................656

A.5 Iterative techniques ...............................656

A.5.1 Conjugate gradient ...........................657

A.5.2 Preconditioning .............................659

A.5.3 Multigrid ................................660

B Bayesian modeling and inference 661

B.1 Estimation theory ................................662

B.1.1 Likelihood for multivariate Gaussian noise ..............663

B.2 Maximum likelihood estimation and least squares ...............665

B.3 Robust statistics .................................666

B.4 Prior models and Bayesian inference ......................667

B.5 Markov random ﬁelds ..............................668

B.5.1 Gradient descent and simulated annealing ...............670

B.5.2 Dynamic programming .........................670

B.5.3 Belief propagation ...........................672

B.5.4 Graph cuts ...............................674

B.5.5 Linear programming ..........................676

B.6 Uncertainty estimation (error analysis) .....................678

C Supplementary material 679

C.1 Data sets .....................................680

C.2 Software .....................................682

C.3 Slides and lectures ...............................689

C.4 Bibliography ..................................690

References 691

Index 793

1.1 What is computer vision? 3

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 por-

trait, 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 illusions

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 tech-

niques 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 photographs (Figure 1.2a). Given a large enough

set of views of a particular object or fac¸ade, we can create accurate dense 3D surface mod-

els using stereo matching (Figure 1.2b). We can track a person moving against a complex

background (Figure 1.2c). We can even, with moderate success, attempt to ﬁnd and name

all of the people in a photograph using a combination of face, clothing, and hair detection

and recognition (Figure 1.2d). However, despite all of these advances, the dream of having a

computer interpret an image at the same level as a two-year old (for example, counting all of

the animals in a picture) remains elusive. Why is vision so difﬁcult? In part, it is because

vision is an inverse problem, in which we seek to recover some unknowns given insufﬁcient

information to fully specify the solution. We must therefore resort to physics-based and prob-

abilistic models to disambiguate between potential solutions. However, modeling the visual

world in all of its rich complexity is far more difﬁcult than, say, modeling the vocal tract that

produces spoken sounds.

The forward models that we use in computer vision are usually developed in physics (ra-

diometry, optics, and sensor design) and in computer graphics. Both of these ﬁelds model

how objects move and animate, how light reﬂects off their surfaces, is scattered by the at-

mosphere, refracted through camera lenses (or human eyes), and ﬁnally projected onto a ﬂat

(or curved) image plane. While computer graphics are not yet perfect (no fully computer-

animated movie with human characters has yet succeeded at crossing the uncanny valley

that separates real humans from android robots and computer-animated humans), in limited

domains, such as rendering a still scene composed of everyday objects or animating extinct

creatures such as dinosaurs, the illusion of reality is perfect.

In computer vision, we are trying to do the inverse, i.e., to describe the world that we see

in one or more images and to reconstruct its properties, such as shape, illumination, and color

distributions. It is amazing that humans and animals do this so effortlessly, while computer

vision algorithms are so error prone. People who have not worked in the ﬁeld often under-

estimate the difﬁculty of the problem. (Colleagues at work often ask me for software to ﬁnd

and name all the people in photos, so they can get on with the more “interesting” work.) This

http://www.michaelbach.de/ot/sze muelue

The term uncanny valley was originally coined by roboticist Masahiro Mori as applied to robotics (Mori 1970).

It is also commonly applied to computer-animated ﬁlms such as Final Fantasy and Polar Express (Geller 2008).

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