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This book presents practical optimization techniques used in image
processing and computer vision problems. Ill-posed problems are
introduced and used as examples to show how each type of problem is
related to typical image processing and computer vision problems.
Unconstrained optimization gives the best solution based on
numerical minimization of a single, scalar-valued objective
function or cost function. Unconstrained optimization problems have
been intensively studied, and many algorithms and tools have been
developed to solve them. Most practical optimization problems,
however, arise with a set of constraints. Typical examples of
constraints include: (i) pre-specified pixel intensity range, (ii)
smoothness or correlation with neighboring information, (iii)
existence on a certain contour of lines or curves, and (iv) given
statistical or spectral characteristics of the solution.
Regularized optimization is a special method used to solve a class
of constrained optimization problems. The term regularization
refers to the transformation of an objective function with
constraints into a different objective function, automatically
reflecting constraints in the unconstrained minimization process.
Because of its simplicity and efficiency, regularized optimization
has many application areas, such as image restoration, image
reconstruction, optical flow estimation, etc. Optimization plays a
major role in a wide variety of theories for image processing and
computer vision. Various optimization techniques are used at
different levels for these problems, and this volume summarizes and
explains these techniques as applied to image processing and
computer vision.
This book presents practical optimization techniques used in image
processing and computer vision problems. Ill-posed problems are
introduced and used as examples to show how each type of problem is
related to typical image processing and computer vision problems.
Unconstrained optimization gives the best solution based on
numerical minimization of a single, scalar-valued objective
function or cost function. Unconstrained optimization problems have
been intensively studied, and many algorithms and tools have been
developed to solve them. Most practical optimization problems,
however, arise with a set of constraints. Typical examples of
constraints include: (i) pre-specified pixel intensity range, (ii)
smoothness or correlation with neighboring information, (iii)
existence on a certain contour of lines or curves, and (iv) given
statistical or spectral characteristics of the solution.
Regularized optimization is a special method used to solve a class
of constrained optimization problems. The term regularization
refers to the transformation of an objective function with
constraints into a different objective function, automatically
reflecting constraints in the unconstrained minimization process.
Because of its simplicity and efficiency, regularized optimization
has many application areas, such as image restoration, image
reconstruction, optical flow estimation, etc. Optimization plays a
major role in a wide variety of theories for image processing and
computer vision. Various optimization techniques are used at
different levels for these problems, and this volume summarizes and
explains these techniques as applied to image processing and
computer vision.
This book provides ample coverage of theoretical and experimental
state-of-the-art work as well as new trends and directions in the
biometrics field. It offers students and software engineers a
thorough understanding of how some core low-level building blocks
of a multi-biometric system are implemented. While this book covers
a range of biometric traits, its main emphasis is placed on
multi-sensory and multi-modal face biometrics algorithms and
systems.
This book provides an ample coverage of theoretical and
experimental state-of-the-art work as well as new trends and
directions in the biometrics field. It offers students and software
engineers a thorough understanding of how some core low-level
building blocks of a multi-biometric system are implemented. While
this book covers a range of biometric traits including facial
geometry, 3D ear form, fingerprints, vein structure, voice, and
gait, its main emphasis is placed on multi-sensory and multi-modal
face biometrics algorithms and systems. "Multi-sensory" refers to
combining data from two or more biometric sensors, such as
synchronized reflectance-based and temperature-based face images.
"Multi-modal" biometrics means fusing two or more biometric
modalities, like face images and voice timber. This practical
reference contains four distinctive parts and a brief introduction
chapter. The first part addresses new and emerging face biometrics.
Emphasis is placed on biometric systems where single sensor and
single modality are employed in challenging imaging conditions. The
second part on multi-sensory face biometrics deals with the
personal identification task in challenging variable illuminations
and outdoor operating scenarios by employing visible and thermal
sensors. The third part of the book focuses on multi-modal face
biometrics by integrating voice, ear, and gait modalities with
facial data. The last part presents generic chapters on
multi-biometrics fusion methodologies and performance prediction
techniques.
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