Building a robot and enabling it to sense its environment is a wonderful way to
take your Arduino knowledge to the next level. In writing this book, I have brought
together my love for invention and my experience with electronics, robotics and
microcontrollers. I hope you have as much pleasure building and enhancing your
robot as I did developing the techniques contained in this book.
This book is an outgrowth of a course developed at Stanford University over
the past five years. It is suitable as a self-contained textbook for second-level
undergraduates or for first-level graduate students in almost every field that
employs quantitative methods. As prerequisites, it is assumed that the student
may have had a first course in differential equations and a first course in linear
algebra or matrix analysis. These two subjects, however, are reviewed in
Chapters 2 and 3, insofar as they are required for later developments.
Machine learning is a broad and fascinating field. Even
today, machine learning technology runs a substantial part of your
life, often without you knowing it. Any plausible approach to artifi-
cial intelligence must involve learning, at some level, if for no other
reason than it’s hard to call a system intelligent if it cannot learn.
Machine learning is also fascinating in its own right for the philo-
sophical questions it raises about what it means to learn and succeed
at tasks.
Artificial Intelligence (AI) has undoubtedly been one of the most important buz-
zwords over the past years. The goal in AI is to design algorithms that transform com-
puters into “intelligent” agents. By intelligence here we do not necessarily mean an
extraordinary level of smartness shown by superhuman; it rather often involves very
basic problems that humans solve very frequently in their day-to-day life. This can
be as simple as recognizing faces in an image, driving a car, playing a board game, or
reading (and understanding) an article in a newspaper. The intelligent behaviour ex-
hibited by humans when “reading” is one of the main goals for a subfield of AI called
Natural Language Processing (NLP). Natural language 1 is one of the most complex
tools used by humans for a wide range of reasons, for instance to communicate with
others, to express thoughts, feelings and ideas, to ask questions, or to give instruc-
tions. Therefore, it is crucial for computers to possess the ability to use the same tool
in order to effectively interact with humans.
This edition of Digital Image Processing is a major revision of the book. As in
the 1977 and 1987 editions by Gonzalez and Wintz, and the 1992, 2002, and 2008
editions by Gonzalez and Woods, this sixth-generation edition was prepared
with students and instructors in mind. The principal objectives of the book
continue to be to provide an introduction to basic concepts and methodologies
applicable to digital image processing, and to develop a foundation that can
be used as the basis for further study and research in this field. To achieve
these objectives, we focused again on material that we believe is fundamental
and whose scope of application is not limited to the solution of specialized
problems. The mathematical complexity of the book remains at a level well
within the grasp of college seniors and first-year graduate students who have
introductory preparation in mathematical analysis, vectors, matrices, probability,
statistics, linear systems, and computer programming. The book website provides
tutorials to support readers needing a review of this background material
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This programming manual provides information for application and system-level softwaredevelopers. It gives a full description of the STM32F3 and STM32F4 Series Cortex?-M4processor programming model, instruction set and core peripherals.
