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Traditional Pattern Recognition (PR) and Computer Vision (CV)
technologies have mainly focused on full automation, even though
full automation often proves elusive or unnatural in many
applications, where the technology is expected to assist rather
than replace the human agents. However, not all the problems can be
automatically solved being the human interaction the only way to
tackle those applications. Recently, multimodal human interaction
has become an important field of increasing interest in the
research community. Advanced man-machine interfaces with high
cognitive capabilities are a hot research topic that aims at
solving challenging problems in image and video applications.
Actually, the idea of computer interactive systems was already
proposed on the early stages of computer science. Nowadays, the
ubiquity of image sensors together with the ever-increasing
computing performance has open new and challenging opportunities
for research in multimodal human interaction. This book aims to
show how existing PR and CV technologies can naturally evolve using
this new paradigm. The chapters of this book show different
successful case studies of multimodal interactive technologies for
both image and video applications. They cover a wide spectrum of
applications, ranging from interactive handwriting transcriptions
to human-robot interactions in real environments.
Traditional Pattern Recognition (PR) and Computer Vision (CV)
technologies have mainly focused on full automation, even though
full automation often proves elusive or unnatural in many
applications, where the technology is expected to assist rather
than replace the human agents. However, not all the problems can be
automatically solved being the human interaction the only way to
tackle those applications. Recently, multimodal human interaction
has become an important field of increasing interest in the
research community. Advanced man-machine interfaces with high
cognitive capabilities are a hot research topic that aims at
solving challenging problems in image and video applications.
Actually, the idea of computer interactive systems was already
proposed on the early stages of computer science. Nowadays, the
ubiquity of image sensors together with the ever-increasing
computing performance has open new and challenging opportunities
for research in multimodal human interaction. This book aims to
show how existing PR and CV technologies can naturally evolve using
this new paradigm. The chapters of this book show different
successful case studies of multimodal interactive technologies for
both image and video applications. They cover a wide spectrum of
applications, ranging from interactive handwriting transcriptions
to human-robot interactions in real environments.
This work presents a full generic approach to the detection and
recognition of traffic signs. The approach is based on the latest
computer vision methods for object detection, and on powerful
methods for multiclass classification. The challenge was to
robustly detect a set of different sign classes in real time, and
to classify each detected sign into a large, extensible set of
classes. To address this challenge, several state-of-the-art
methods were developed that can be used for different recognition
problems. Following an introduction to the problems of traffic sign
detection and categorization, the text focuses on the problem of
detection, and presents recent developments in this field. The text
then surveys a specific methodology for the problem of traffic sign
categorization - Error-Correcting Output Codes - and presents
several algorithms, performing experimental validation on a mobile
mapping application. The work ends with a discussion on future
research and continuing challenges.
This accessible and classroom-tested textbook/reference presents an
introduction to the fundamentals of the emerging and
interdisciplinary field of data science. The coverage spans key
concepts adopted from statistics and machine learning, useful
techniques for graph analysis and parallel programming, and the
practical application of data science for such tasks as building
recommender systems or performing sentiment analysis. Topics and
features: provides numerous practical case studies using real-world
data throughout the book; supports understanding through hands-on
experience of solving data science problems using Python; describes
techniques and tools for statistical analysis, machine learning,
graph analysis, and parallel programming; reviews a range of
applications of data science, including recommender systems and
sentiment analysis of text data; provides supplementary code
resources and data at an associated website.
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