224 research outputs found
2D partially occluded object recognition using curve moment invariants
Master'sMASTER OF ENGINEERIN
Human-Centric Machine Vision
Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans
On-board three-dimensional object tracking: Software and hardware solutions
We describe a real time system for recognition and tracking 3D objects such as UAVs, airplanes, fighters with the optical sensor. Given a 2D image, the system has to perform background subtraction, recognize relative rotation, scale and translation of the object to sustain a prescribed topology of the fleet. In the thesis a comparative study of different algorithms and performance evaluation is carried out based on time and accuracy constraints. For background subtraction task we evaluate frame differencing, approximate median filter, mixture of Gaussians and propose classification based on neural network methods. For object detection we analyze the performance of invariant moments, scale invariant feature transform and affine scale invariant feature transform methods. Various tracking algorithms such as mean shift with variable and a fixed sized windows, scale invariant feature transform, Harris and fast full search based on fast fourier transform algorithms are evaluated. We develop an algorithm for the relative rotations and the scale change calculation based on Zernike moments. Based on the design criteria the selection is made for on-board implementation. The candidate techniques have been implemented on the Texas Instrument TMS320DM642 EVM board. It is shown in the thesis that 14 frames per second can be processed; that supports the real time implementation of the tracking system under reasonable accuracy limits
Continuous Human Activity Tracking over a Large Area with Multiple Kinect Sensors
In recent years, researchers had been inquisitive about the use of technology to enhance the healthcare and wellness of patients with dementia. Dementia symptoms are associated with the decline in thinking skills and memory severe enough to reduce a person’s ability to pay attention and perform daily activities. Progression of dementia can be assessed by monitoring the daily activities of the patients. This thesis encompasses continuous localization and behavioral analysis of patient’s motion pattern over a wide area indoor living space using multiple calibrated Kinect sensors connected over the network. The skeleton data from all the sensor is transferred to the host computer via TCP sockets into Unity software where it is integrated into a single world coordinate system using calibration technique. Multiple cameras are placed with some overlap in the field of view for the successful calibration of the cameras and continuous tracking of the patients. Localization and behavioral data are stored in a CSV file for further analysis
3D Object Recognition Based On Constrained 2D Views
The aim of the present work was to build a novel 3D object recognition system capable of classifying
man-made and natural objects based on single 2D views. The approach to this problem
has been one motivated by recent theories on biological vision and multiresolution analysis. The
project's objectives were the implementation of a system that is able to deal with simple 3D
scenes and constitutes an engineering solution to the problem of 3D object recognition, allowing
the proposed recognition system to operate in a practically acceptable time frame.
The developed system takes further the work on automatic classification of marine phytoplank-
(ons, carried out at the Centre for Intelligent Systems, University of Plymouth. The thesis discusses
the main theoretical issues that prompted the fundamental system design options. The
principles and the implementation of the coarse data channels used in the system are described.
A new multiresolution representation of 2D views is presented, which provides the classifier
module of the system with coarse-coded descriptions of the scale-space distribution of potentially
interesting features. A multiresolution analysis-based mechanism is proposed, which directs
the system's attention towards potentially salient features. Unsupervised similarity-based
feature grouping is introduced, which is used in coarse data channels to yield feature signatures
that are not spatially coherent and provide the classifier module with salient descriptions of object
views. A simple texture descriptor is described, which is based on properties of a special wavelet
transform.
The system has been tested on computer-generated and natural image data sets, in conditions
where the inter-object similarity was monitored and quantitatively assessed by human subjects,
or the analysed objects were very similar and their discrimination constituted a difficult task even
for human experts. The validity of the above described approaches has been proven. The studies
conducted with various statistical and artificial neural network-based classifiers have shown that
the system is able to perform well in all of the above mentioned situations. These investigations
also made possible to take further and generalise a number of important conclusions drawn during
previous work carried out in the field of 2D shape (plankton) recognition, regarding the behaviour
of multiple coarse data channels-based pattern recognition systems and various classifier
architectures.
The system possesses the ability of dealing with difficult field-collected images of objects and
the techniques employed by its component modules make possible its extension to the domain
of complex multiple-object 3D scene recognition. The system is expected to find immediate applicability
in the field of marine biota classification
Biometric iris image segmentation and feature extraction for iris recognition
PhD ThesisThe continued threat to security in our interconnected world today begs for urgent
solution. Iris biometric like many other biometric systems provides an alternative solution
to this lingering problem. Although, iris recognition have been extensively studied, it is
nevertheless, not a fully solved problem which is the factor inhibiting its implementation
in real world situations today. There exists three main problems facing the existing iris
recognition systems: 1) lack of robustness of the algorithm to handle non-ideal iris
images, 2) slow speed of the algorithm and 3) the applicability to the existing systems in
real world situation. In this thesis, six novel approaches were derived and implemented
to address these current limitation of existing iris recognition systems.
A novel fast and accurate segmentation approach based on the combination of graph-cut
optimization and active contour model is proposed to define the irregular boundaries of
the iris in a hierarchical 2-level approach. In the first hierarchy, the approximate boundary
of the pupil/iris is estimated using a method based on Hough’s transform for the pupil and
adapted starburst algorithm for the iris. Subsequently, in the second hierarchy, the final
irregular boundary of the pupil/iris is refined and segmented using graph-cut based active
contour (GCBAC) model proposed in this work. The segmentation is performed in two
levels, whereby the pupil is segmented first before the iris. In order to detect and eliminate
noise and reflection artefacts which might introduce errors to the algorithm, a preprocessing
technique based on adaptive weighted edge detection and high-pass filtering
is used to detect reflections on the high intensity areas of the image while exemplar based
image inpainting is used to eliminate the reflections. After the segmentation of the iris
boundaries, a post-processing operation based on combination of block classification
method and statistical prediction approach is used to detect any super-imposed occluding
eyelashes/eyeshadows. The normalization of the iris image is achieved though the rubber
sheet model.
In the second stage, an approach based on construction of complex wavelet filters and
rotation of the filters to the direction of the principal texture direction is used for the
extraction of important iris information while a modified particle swam optimization
(PSO) is used to select the most prominent iris features for iris encoding. Classification
of the iriscode is performed using adaptive support vector machines (ASVM).
Experimental results demonstrate that the proposed approach achieves accuracy of
98.99% and is computationally about 2 times faster than the best existing approach.Ebonyi State
University and Education Task Fund, Nigeri
Multi-modal video analysis for early fire detection
In dit proefschrift worden verschillende aspecten van een intelligent videogebaseerd branddetectiesysteem onderzocht. In een eerste luik ligt de nadruk op de multimodale verwerking van visuele, infrarood en time-of-flight videobeelden, die de louter visuele detectie verbetert. Om de verwerkingskost zo minimaal mogelijk te houden, met het oog op real-time detectie, is er voor elk van het type sensoren een set ’low-cost’ brandkarakteristieken geselecteerd die vuur en vlammen uniek beschrijven. Door het samenvoegen van de verschillende typen informatie kunnen het aantal gemiste detecties en valse alarmen worden gereduceerd, wat resulteert in een significante verbetering van videogebaseerde branddetectie. Om de multimodale detectieresultaten te kunnen combineren, dienen de multimodale beelden wel geregistreerd (~gealigneerd) te zijn. Het tweede luik van dit proefschrift focust zich hoofdzakelijk op dit samenvoegen van multimodale data en behandelt een nieuwe silhouet gebaseerde registratiemethode. In het derde en tevens laatste luik van dit proefschrift worden methodes voorgesteld om videogebaseerde brandanalyse, en in een latere fase ook brandmodellering, uit te voeren. Elk van de voorgestelde technieken voor multimodale detectie en multi-view lokalisatie zijn uitvoerig getest in de praktijk. Zo werden onder andere succesvolle testen uitgevoerd voor de vroegtijdige detectie van wagenbranden in ondergrondse parkeergarages
Uniscale and multiscale gait recognition in realistic scenario
The performance of a gait recognition method is affected by numerous challenging
factors that degrade its reliability as a behavioural biometrics for subject identification in
realistic scenario. Thus for effective visual surveillance, this thesis presents five gait recog-
nition methods that address various challenging factors to reliably identify a subject in
realistic scenario with low computational complexity. It presents a gait recognition method
that analyses spatio-temporal motion of a subject with statistical and physical parameters
using Procrustes shape analysis and elliptic Fourier descriptors (EFD). It introduces a part-
based EFD analysis to achieve invariance to carrying conditions, and the use of physical
parameters enables it to achieve invariance to across-day gait variation. Although spatio-
temporal deformation of a subject’s shape in gait sequences provides better discriminative
power than its kinematics, inclusion of dynamical motion characteristics improves the iden-
tification rate. Therefore, the thesis presents a gait recognition method which combines
spatio-temporal shape and dynamic motion characteristics of a subject to achieve robust-
ness against the maximum number of challenging factors compared to related state-of-the-
art methods. A region-based gait recognition method that analyses a subject’s shape in
image and feature spaces is presented to achieve invariance to clothing variation and carry-
ing conditions. To take into account of arbitrary moving directions of a subject in realistic
scenario, a gait recognition method must be robust against variation in view. Hence, the the-
sis presents a robust view-invariant multiscale gait recognition method. Finally, the thesis
proposes a gait recognition method based on low spatial and low temporal resolution video
sequences captured by a CCTV. The computational complexity of each method is analysed.
Experimental analyses on public datasets demonstrate the efficacy of the proposed methods
- …