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首页OpenCV与深度学习:计算机视觉实战指南
"《利用OpenCV学习计算机视觉:深度学习卷积神经网络与循环神经网络》是一本实用指南,由Sunila Gollapudi撰写,VLaxmikanth作序,由Allitebooks出版。本书专为那些希望在Python环境下利用OpenCV库掌握计算机视觉技术的读者设计。作者深入浅出地探讨了计算机视觉的核心概念,包括图像处理、目标检测、物体识别、跟踪以及运动分析等关键技术。 书中特别强调了深度学习在计算机视觉中的应用,特别是卷积神经网络(Convolutional Neural Networks, CNN)和循环神经网络(Recurrent Neural Networks, RNN)的结合。这些现代神经网络架构在图像分类、物体定位、视频理解等方面表现出色,为实际场景中的自动化和智能化提供了强大工具。 通过实例演示和理论讲解,读者不仅能够理解计算机视觉的基本原理,还能学会如何构建和优化自己的深度学习模型,应用于诸如人脸识别、自动驾驶、无人机监控等众多领域。此外,书中的版权信息明确,所有内容受到法律保护,确保了知识产权的尊重。 本书适合有一定编程基础,对计算机视觉感兴趣,或希望进一步提升在这一领域的技能的读者。无论是初学者还是经验丰富的开发者,都能从中受益匪浅。无论是希望通过OpenCV探索人工智能潜力的科研人员,还是希望将计算机视觉技术应用于产品开发的企业工程师,都能找到所需的知识和实践案例。"
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Chapter 4, “Image Manipulation and Segmentation,”
covers image manipulations and segmentation-
related functions that are core to image processing
in computer vision. For each of the use cases,
the syntax and implementations of the built-in
functions in OpenCV in Python are covered, and
sample implementations are provided. Techniques
such as edge detection, rotations, resizing, shape
detection, and so on, are covered in depth.
Chapter 5, “Object Detection and Recognition,”
provides a deep dive into object detection and
then moves on to object recognition followed by
face-feature recognition, landmark identication,
and nally handwriting recognition. e necessary
OpenCV libraries are explained, and sample
implementations are provided.
Chapter 6, “Motion Analysis and Tracking,” covers
motion analysis and tracking of objects in videos.
Information about dierent types of objects in
motion is given, with details on how to remove
background and foreground information and how to
do real-time tracking. e topics in this chapter are
an extension to the object detection and recognition
techniques in Chapter 5.
InTroduCTIonInTroduCTIon
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© Sunila Gollapudi 2019
S. Gollapudi, Learn Computer Vision Using OpenCV,
https://doi.org/10.1007/978-1-4842-4261-2_1
CHAPTER 1
Artificial Intelligence
andComputer Vision
The field of artificial intelligence, and its application in day-to day life,
has seen remarkable evolution in the past three to five years. Artificial
intelligence (AI) is an enabler that potentially facilitates machines doing
everything that humans can do. This includes perceiving, reasoning,
rationalizing, and problem-solving while working within a context or
interacting with the environment with more efficiency and accuracy.
Here, the word context means the domain or the business where the
problem is dealt with, for example online shopping, social media,
insurance, manufacturing, and others. Interacting with the environment
could mean that computers or machines work along with the humans or
take input from external stimuli and adjust their behaviors accordingly.
Computer vision, which enables computers and machines to see and
understand the world around them, specifically has become a game-
changer for how and where machines can be used and AI can be adopted.
This chapter covers the larger AI dream that is all about touching both
the personal and professional lives of humans and how computer vision
among other areas is a key enabler. Also, you’ll learn about a few real-
world applications, challenges, and technology tools such as OpenCV that
help in complex implementations.
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The following topics are covered in detail in this chapter:
• Artificial intelligence and its landscape, which includes
a basic definition and the usage context of robotics,
intelligent automation, natural language processing,
expert systems, speech recognition, computer vision,
and machine learning
• Computer vision, including its challenges and
applications in today’s world
• Computer vision architecture and tools, including what
images are and how to understand and manipulate key
attributes of images
• A sneak-peak into the core building blocks of computer
vision and aspects such as image manipulation and
segmentation, object detection, motion analysis and
tracking, and others
• A brief introduction to optical character recognition,
intelligent character recognition, and optimal mark
recognition
Note A good understanding of programming and prior knowledge
of Python will be helpful to understand the working examples in
this book; however, primers will be given for all the hands-on code
exercises.
CHAPTER 1 ARTIFICIAL INTELLIGENCE ANDCOMPUTER VISION
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Introduction toArtificial Intelligence
The definition of artificial intelligence has evolved since its first reference
in 1956 at a Dartmouth conference, from emulating how the human brain
works to solving focused, complex problems to doing all that a human can
do such as seeing, hearing, communicating, acting, learning, perceiving,
thinking, deciding, demonstrating emotion and compassion, interacting
with environment, and more. The 2012 AI breakthroughs with vision,
language recognition, and self-driving vehicles changed the way that AI
is looked at today. This section gives a simple and informal definition of
artificial intelligence.
Essentially, AI is the field of computer science that involves enabling
computers to behave like humans or perform tasks that usually
require human intelligence.
The purpose of AI systems is evolving. In this section, we will cover
different types of AI systems categorized based on their core purpose.
You will also observe how these different types of AI systems signify a step
toward building smarter systems.
Figure 1-1 lists different types of AI.
CHAPTER 1 ARTIFICIAL INTELLIGENCE ANDCOMPUTER VISION
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• Reactive AI was the rst kind of AI that was talked
about. ese types of machines do not have memory
and do not use information from past experiences.
In these machines, the current context is directly
perceived as it is and acted upon. is makes the
machine behave the same way every time it encounters
a situation. e benet of this is a reliable and
consistent outcome. An example is Deep Blue (a chess-
playing computer developed by IBM that won against
Kasparov in the game of chess).
• Limited memory AI machines look into the past and
use it as a preprogrammed representation of the world
and then apply it to the current data set. For example,
in self-driving cars, decisions on when a car should
Figure 1-1. Types of AI
CHAPTER 1 ARTIFICIAL INTELLIGENCE ANDCOMPUTER VISION
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