pHash is an implementation of various perceptual hashing algorithms. A perceptual hash is a fingerprint of an audio, video, or image file that is mathematically based on the audio or visual content contained within. Unlike cryptographic hash functions that rely on the avalanche effect of small changes in input leading to drastic changes in the output, perceptual hashes are "close" to one another if the inputs are visually or auditorily similar. As a result, perceptual hashes must also be robust enough to take into account transformations that could have been performed on the input.
The first two functions are useful for creating a blank image of the specified parameters. A possible use is in functions that require a pointer to a result.
The clone function performs an exact copy of the IplImage* parameter
This library implements the KLT Tracking algorithm [2004] for Feature Tracking in Video useful in computer vision tasks like object recognition, image indexing, tracking and structure from motion. This implementation uses programmable Graphics Hardware to achieve considerable speedup in the running time of the GPU-based implementation.
This library implements the KLT Tracking algorithm [2004] for Feature Tracking in Video useful in computer vision tasks like object recognition, image indexing, tracking and structure from motion. This implementation uses programmable Graphics Hardware to achieve considerable speedup in the running time of the GPU-based implementation.
Cisco E-DI supports perl scripting through the CLI. This feature automates many of the server and
network administration tasks. This section explains how to enable and disable the perl scripting service,
and how to use Cisco E-DI Perl API for daily tasks.
When a perl script is implemented by an administrator or a user, the script automatically inherits the
user s security privileges and the user s operational context (either server or network).
Each invocation of a
base implementaion of the protein (starting from the amino acid sequence) feature extractor used in "L. Nanni and A. Lumini, An ensemble of Support Vector Machines for predicting virulent proteins, Expert Systems With Applications, vol.36, no.4, pp.7458-7462, May 2009. "
This C++ code example demonstrates how to localise an application to adapt to the selected phone language. The example application has two sets of localised strings (English and German) for the application name, labels, text, and image. The application also includes number, currency, date, and time localisation, in which the TLocale method is used for localisation even if the selected phone language is neither one of the two application languages. The updated example has been tested to support S60 5th Edition and touch UI.
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.
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
