Ten Lectures On Statistical And Structural Pattern Recognition (computational Imaging And Vision)
by M.I. Schlesinger /
2011 / English / PDF
17 MB Download
Preface to the English edition This monograph Ten Lectur,es on
Statistical and Structural Pattern Recognition uncovers the close
relationship between various well known pattern recognition
problems that have so far been considered independent. These
relationships became apparent when formal procedures addressing not
only known prob lems but also their generalisations were
discovered. The generalised problem formulations were analysed
mathematically and unified algorithms were found. The book unifies
of two main streams ill pattern recognition-the statisti cal a11d
structural ones. In addition to this bridging on the uppermost
level, the book mentions several other unexpected relations within
statistical and structural methods. The monograph is intended for
experts, for students, as well as for those who want to enter the
field of pattern recognition. The theory is built up from scratch
with almost no assumptions about any prior knowledge of the reader.
Even when rigorous mathematical language is used we make an effort
to keep the text easy to comprehend. This approach makes the book
suitable for students at the beginning of their scientific career.
Basic building blocks are explained in a style of an accessible
intellectual exercise, thus promoting good practice in reading
mathematical text. The paradoxes, beauty, and pitfalls of
scientific research are shown on examples from pattern recognition.
Each lecture is amended by a discussion with an inquisitive student
that elucidates and deepens the explanation, providing additional
pointers to computational procedures and deep rooted errors.
Preface to the English edition This monograph Ten Lectur,es on
Statistical and Structural Pattern Recognition uncovers the close
relationship between various well known pattern recognition
problems that have so far been considered independent. These
relationships became apparent when formal procedures addressing not
only known prob lems but also their generalisations were
discovered. The generalised problem formulations were analysed
mathematically and unified algorithms were found. The book unifies
of two main streams ill pattern recognition-the statisti cal a11d
structural ones. In addition to this bridging on the uppermost
level, the book mentions several other unexpected relations within
statistical and structural methods. The monograph is intended for
experts, for students, as well as for those who want to enter the
field of pattern recognition. The theory is built up from scratch
with almost no assumptions about any prior knowledge of the reader.
Even when rigorous mathematical language is used we make an effort
to keep the text easy to comprehend. This approach makes the book
suitable for students at the beginning of their scientific career.
Basic building blocks are explained in a style of an accessible
intellectual exercise, thus promoting good practice in reading
mathematical text. The paradoxes, beauty, and pitfalls of
scientific research are shown on examples from pattern recognition.
Each lecture is amended by a discussion with an inquisitive student
that elucidates and deepens the explanation, providing additional
pointers to computational procedures and deep rooted errors.
