35,912 research outputs found
Gait recognition based on shape and motion analysis of silhouette contours
This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject’s silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject’s shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject’s back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject’s leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods
M\"obius Invariants of Shapes and Images
Identifying when different images are of the same object despite changes
caused by imaging technologies, or processes such as growth, has many
applications in fields such as computer vision and biological image analysis.
One approach to this problem is to identify the group of possible
transformations of the object and to find invariants to the action of that
group, meaning that the object has the same values of the invariants despite
the action of the group. In this paper we study the invariants of planar shapes
and images under the M\"obius group , which arises
in the conformal camera model of vision and may also correspond to neurological
aspects of vision, such as grouping of lines and circles. We survey properties
of invariants that are important in applications, and the known M\"obius
invariants, and then develop an algorithm by which shapes can be recognised
that is M\"obius- and reparametrization-invariant, numerically stable, and
robust to noise. We demonstrate the efficacy of this new invariant approach on
sets of curves, and then develop a M\"obius-invariant signature of grey-scale
images
Automated Particle Identification through Regression Analysis of Size, Shape and Colour
Rapid point of care diagnostic tests and tests to provide therapeutic information are now available for a
range of specific conditions from the measurement of blood glucose levels for diabetes to card agglutination tests for
parasitic infections. Due to a lack of specificity these test are often then backed up by more conventional lab based
diagnostic methods for example a card agglutination test may be carried out for a suspected parasitic infection in the
field and if positive a blood sample can then be sent to a lab for confirmation. The eventual diagnosis is often achieved
by microscopic examination of the sample. In this paper we propose a computerized vision system for aiding in the
diagnostic process; this system used a novel particle recognition algorithm to improve specificity and speed during
the diagnostic process. We will show the detection and classification of different types of cells in a diluted blood
sample using regression analysis of their size, shape and colour. The first step is to define the objects to be tracked by
a Gaussian Mixture Model for background subtraction and binary opening and closing for noise suppression. After
subtracting the objects of interest from the background the next challenge is to predict if a given object belongs to a
certain category or not. This is a classification problem, and the output of the algorithm is a Boolean value (true/false).
As such the computer program should be able to ”predict” with reasonable level of confidence if a given particle
belongs to the kind we are looking for or not. We show the use of a binary logistic regression analysis with three
continuous predictors: size, shape and color histogram. The results suggest this variables could be very useful in a
logistic regression equation as they proved to have a relatively high predictive value on their own
A graph-based mathematical morphology reader
This survey paper aims at providing a "literary" anthology of mathematical
morphology on graphs. It describes in the English language many ideas stemming
from a large number of different papers, hence providing a unified view of an
active and diverse field of research
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