PhDPattern detection is a well-studied area of computer vision, but still current methods are
unstable in images of poor quality. This thesis describes improvements over contemporary
methods in the fast detection of unseen patterns in a large corpus of videos that vary
tremendously in colour and texture definition, captured “in the wild” by mobile devices
and surveillance cameras.
We focus on three key areas of this broad subject;
First, we identify consistency weaknesses in existing techniques of processing an image
and it’s horizontally reflected (mirror) image. This is important in police investigations
where subjects change their appearance to try to avoid recognition, and we propose that
invariance to horizontal reflection should be more widely considered in image description
and recognition tasks too. We observe online Deep Learning system behaviours in
this respect, and provide a comprehensive assessment of 10 popular low level feature
detectors.
Second, we develop simple and fast algorithms that combine to provide memory- and
processing-efficient feature matching. These involve static scene elimination in the presence
of noise and on-screen time indicators, a blur-sensitive feature detection that finds
a greater number of corresponding features in images of varying sharpness, and a combinatorial
texture and colour feature matching algorithm that matches features when
either attribute may be poorly defined. A comprehensive evaluation is given, showing
some improvements over existing feature correspondence methods.
Finally, we study random decision forests for pattern detection. A new method of
indexing patterns in video sequences is devised and evaluated. We automatically label
positive and negative image training data, reducing a task of unsupervised learning to
one of supervised learning, and devise a node split function that is invariant to mirror
reflection and rotation through 90 degree angles. A high dimensional vote accumulator
encodes the hypothesis support, yielding implicit back-projection for pattern detection.European Union’s Seventh Framework Programme, specific
topic “framework and tools for (semi-) automated exploitation of massive amounts of digital data
for forensic purposes”, under grant agreement number 607480 (LASIE IP project)
