This book uses the Python language to teach pro
-
gramming concepts and problem
-solving skills, without assuming any previous program- ming experience. With easy-to-understand examples, pseudocode, flowcharts, and other
tools, the student learns how to design the logic of programs and then implement those
programs using Python. This book is ideal for an introductory programming course or a
programming logic and design course using Python as the language.
As with all the boolts in the
Starting Out With
series, the hallmark of this text is its clear,
friendly, and easy
-to-understand writing. In addition, it is rich in example programs that
are concise and practical. The programs in this book include short examples that highlight
specific programming topics, as well as more involved examples that focus on problem
solving. Each chapter provides one or more case studies that provide step
-by-step analysis
of a specific problem and shows the student how to solve it.
The object detector described below has been initially proposed by
P.F. Felzenszwalb in [Felzenszwalb2010]. It is based on a
Dalal-Triggs detector that uses a single filter on histogram of
oriented gradients (HOG) features to represent an object category.
This detector uses a sliding window approach, where a filter is
applied at all positions and scales of an image. The first
innovation is enriching the Dalal-Triggs model using a
star-structured part-based model defined by a “root” filter
(analogous to the Dalal-Triggs filter) plus a set of parts filters
and associated deformation models. The score of one of star models
at a particular position and scale within an image is the score of
the root filter at the given location plus the sum over parts of the
maximum, over placements of that part, of the part filter score on
its location minus a deformation cost easuring the deviation of the
part from its ideal location relative to the root. Both root and
part filter scores are defined by the dot product between a filter
(a set of weights) and a subwindow of a feature pyramid computed
from the input image. Another improvement is a representation of the
class of models by a mixture of star models. The score of a mixture
model at a particular position and scale is the maximum over
components, of the score of that component model at the given
location.
Regardless of the branch of science or engineering, theoreticians have always
been enamored with the notion of expressing their results in the form of
closed-form expressions. Quite often, the elegance of the closed-form solution
is overshadowed by the complexity of its form and the difficulty in evaluating
it numerically. In such instances, one becomes motivated to search instead for
a solution that is simple in form and simple to evaluate.
Regardless of the branch of science or engineering, theoreticians have always been
enamored with the notion of expressing their results in the form of closed-form
expressions. Quite often the elegance of the closed-form solution is overshadowed
by the complexity of its form and the difficulty in evaluating it numerically. In
such instances, one becomes motivated to search instead for a solution that is
simple in form and likewise simple to evaluate.
Mobile wireless communications are in constant evolution due to the continu-
ously increasing requirements and expectations of both users and operators.
Mass multimedia* services have been for a long time expected to generate a large
amount of data traffic in future wireless networks [1]. Mass multimedia services
are, by definition, purposed for many people. In general, it can be distinguished
between the distribution of any popular content over a wide area and the distribu-
tion of location-dependent information in highly populated areas. Representative
examples include the delivery of live video streaming content (like sports compe-
titions, concerts, or news) and file download (multimedia clips, digital newspa-
pers, or software updates).
