2,543 research outputs found
A Reproducible Study on Remote Heart Rate Measurement
This paper studies the problem of reproducible research in remote
photoplethysmography (rPPG). Most of the work published in this domain is
assessed on privately-owned databases, making it difficult to evaluate proposed
algorithms in a standard and principled manner. As a consequence, we present a
new, publicly available database containing a relatively large number of
subjects recorded under two different lighting conditions. Also, three
state-of-the-art rPPG algorithms from the literature were selected, implemented
and released as open source free software. After a thorough, unbiased
experimental evaluation in various settings, it is shown that none of the
selected algorithms is precise enough to be used in a real-world scenario
From Gabor Magnitude to Gabor Phase Features: Tackling the Problem of Face Recognition under Severe Illumination Changes
Among the numerous biometric systems presented in the literature, face recognition systems have received a great deal of attention in recent years. The main driving force in the development of these systems can be found in the enormous potential face recognition technology has in various application domains ranging from access control, human-machin
Neuromorphic perception for greenhouse technology using event-based sensors
Event-Based Cameras (EBCs), unlike conventional cameras, feature independent pixels that asynchronously generate outputs upon detecting changes in their field of view. Short calculations are performed on each event to mimic the brain. The output is a sparse sequence of events with high temporal precision. Conventional computer vision algorithms do not leverage these properties. Thus a new paradigm has been devised. While event cameras are very efficient in representing sparse sequences of events with high temporal precision, many approaches are challenged in applications where a large amount of spatially-temporally rich information must be processed in real-time. In reality, most tasks in everyday life take place in complex and uncontrollable environments, which require sophisticated models and intelligent reasoning. Typical hard problems in real-world scenes are detecting various non-uniform objects or navigation in an unknown and complex environment. In addition, colour perception is an essential fundamental property in distinguishing objects in natural scenes. Colour is a new aspect of event-based sensors, which work fundamentally differently from standard cameras, measuring per-pixel brightness changes per colour filter asynchronously rather than measuring “absolute” brightness at a constant rate. This thesis explores neuromorphic event-based processing methods for high-noise and cluttered environments with imbalanced classes. A fully event-driven processing pipeline was developed for agricultural applications to perform fruits detection and classification to unlock the outstanding properties of event cameras. The nature of features in such data was explored, and methods to represent and detect features were demonstrated. A framework for detecting and classifying features was developed and evaluated on the N-MNIST and Dynamic Vision Sensor (DVS) gesture datasets. The same network was evaluated on laboratory recorded and real-world data with various internal variations for fruits detection such as overlap, variation in size and appearance. In addition, a method to handle highly imbalanced data was developed. We examined the characteristics of spatio-temporal patterns for each colour filter to help expand our understanding of this novel data and explored their applications in classification tasks where colours were more relevant features than shapes and appearances. The results presented in this thesis demonstrate the potential and efficacy of event- based systems by demonstrating the applicability of colour event data and the viability of event-driven classification
Texture analysis and Its applications in biomedical imaging: a survey
Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity
and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications.
This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021;
date of current version January 24, 2022. This work was supported in
part by the Portuguese Foundation for Science and Technology (FCT)
under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by
FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio
Adaptive visual sampling
PhDVarious visual tasks may be analysed in the context of sampling from the visual field. In visual
psychophysics, human visual sampling strategies have often been shown at a high-level to
be driven by various information and resource related factors such as the limited capacity of
the human cognitive system, the quality of information gathered, its relevance in context and
the associated efficiency of recovering it. At a lower-level, we interpret many computer vision
tasks to be rooted in similar notions of contextually-relevant, dynamic sampling strategies
which are geared towards the filtering of pixel samples to perform reliable object association. In
the context of object tracking, the reliability of such endeavours is fundamentally rooted in the
continuing relevance of object models used for such filtering, a requirement complicated by realworld
conditions such as dynamic lighting that inconveniently and frequently cause their rapid
obsolescence. In the context of recognition, performance can be hindered by the lack of learned
context-dependent strategies that satisfactorily filter out samples that are irrelevant or blunt the
potency of models used for discrimination. In this thesis we interpret the problems of visual
tracking and recognition in terms of dynamic spatial and featural sampling strategies and, in this
vein, present three frameworks that build on previous methods to provide a more flexible and
effective approach.
Firstly, we propose an adaptive spatial sampling strategy framework to maintain statistical object
models for real-time robust tracking under changing lighting conditions. We employ colour
features in experiments to demonstrate its effectiveness. The framework consists of five parts:
(a) Gaussian mixture models for semi-parametric modelling of the colour distributions of multicolour
objects; (b) a constructive algorithm that uses cross-validation for automatically determining
the number of components for a Gaussian mixture given a sample set of object colours; (c) a
sampling strategy for performing fast tracking using colour models; (d) a Bayesian formulation
enabling models of object and the environment to be employed together in filtering samples by
discrimination; and (e) a selectively-adaptive mechanism to enable colour models to cope with
changing conditions and permit more robust tracking.
Secondly, we extend the concept to an adaptive spatial and featural sampling strategy to deal
with very difficult conditions such as small target objects in cluttered environments undergoing
severe lighting fluctuations and extreme occlusions. This builds on previous work on dynamic
feature selection during tracking by reducing redundancy in features selected at each stage as
well as more naturally balancing short-term and long-term evidence, the latter to facilitate model
rigidity under sharp, temporary changes such as occlusion whilst permitting model flexibility
under slower, long-term changes such as varying lighting conditions. This framework consists of
two parts: (a) Attribute-based Feature Ranking (AFR) which combines two attribute measures;
discriminability and independence to other features; and (b) Multiple Selectively-adaptive Feature
Models (MSFM) which involves maintaining a dynamic feature reference of target object
appearance. We call this framework Adaptive Multi-feature Association (AMA). Finally, we present an adaptive spatial and featural sampling strategy that extends established
Local Binary Pattern (LBP) methods and overcomes many severe limitations of the traditional
approach such as limited spatial support, restricted sample sets and ad hoc joint and disjoint statistical
distributions that may fail to capture important structure. Our framework enables more
compact, descriptive LBP type models to be constructed which may be employed in conjunction
with many existing LBP techniques to improve their performance without modification. The
framework consists of two parts: (a) a new LBP-type model known as Multiscale Selected Local
Binary Features (MSLBF); and (b) a novel binary feature selection algorithm called Binary Histogram
Intersection Minimisation (BHIM) which is shown to be more powerful than established
methods used for binary feature selection such as Conditional Mutual Information Maximisation
(CMIM) and AdaBoost
Dynamic Hyperspectral and Polarized Endoscopic Imaging
The health of rich, developed nations has seen drastic improvement
in the last two centuries. For it to continue improving at a similar
rate new or improved diagnostic and treatment technologies are required,
especially for those diseases such as cancer which are forecast
to constitute the majority of disease burden in the future. Optical
techniques such as microscopy have long played their part in the diagnostic
process. However there are several new biophotonic modalities
that aim to exploit various interactions between light and tissue to provide
enhanced diagnostic information. Many of these show promise in
a laboratory setting but few have progressed to a clinical setting. We
have designed and constructed a
flexible, multi-modal, multi-spectral
laparoscopic imaging system that could be used to demonstrate several
different techniques in a clinical setting.
The core of this system is a dynamic hyperspectral illumination system
based around a supercontinuum laser and Digital Micromirror
Device that can provide specified excitation light in the visible and
near infra-red ranges. This is a powerful tool for spectroscopic techniques
as it is not limited to interrogating a fixed range of wavelengths
and can switch between excitation bands instantaneously. The excitation
spectra can be customised to match particular
fluorophores or
absorption features, introducing new possibilities for spectral imaging.
A standard 10 mm diameter rigid endoscope was incorporated into
the system to reduce cost and demonstrate compatibility with existing
equipment. The polarization properties of two commercial endoscopes
were characterised and found to be unsuited to current polarization
imaging techniques as birefringent materials used in their construction introduce complex, spatially dependent transformations of the polarization
state. Preliminary exemplar data from phantoms and ex vivo
tissue was collected and the feasibility and accuracy of different analysis
techniques demonstrated including multiple class classification algorithms.
Finally, a novel visualisation method was implemented in
order to display the complex hyperspectral data sets in a meaningful
and intuitive way to the user
Investigations into colour constancy by bridging human and computer colour vision
PhD ThesisThe mechanism of colour constancy within the human visual system has long been of great interest to researchers within the psychophysical and image processing communities. With the maturation of colour imaging techniques for both scientific and artistic applications the importance of colour capture accuracy has consistently increased. Colour offers a great deal more information for the viewer than grayscale imagery, ranging from object detection to food ripeness and health estimation amongst many others.
However these tasks rely upon the colour constancy process in order to discount scene illumination to allow these tasks to be carried out. Psychophysical studies have attempted to uncover the inner workings of this mechanism, which would allow it to be reproduced algorithmically. This would allow the development of devices which can eventually capture and perceive colour in the same manner as a human viewer.
These two communities have approached this challenge from opposite ends, and as such very different and largely unconnected approaches. This thesis investigates the development of studies and algorithms which bridge the two communities. Utilising findings from psychophysical studies as inspiration to firstly improve an existing image enhancement algorithm. Results are then compared to state of the art methods. Then, using further knowledge, and inspiration, of the human visual system to develop a novel colour constancy approach. This approach attempts to mimic and replicate the mechanism of colour constancy by investigating the use of a physiological colour space and specific scene contents to estimate illumination. Performance of the colour constancy mechanism within the visual system is then also investigated. The performance of the mechanism across different scenes and commonly and uncommonly encountered illuminations is tested.
The importance of being able to bridge these two communities, with a successful colour constancy method, is then further illustrated with a case study investigating the human visual perception of the agricultural produce of tomatoes.EPSRC DTA:
Institute of Neuroscience, Newcastle University
Objective localisation of oral mucosal lesions using optical coherence tomography.
PhDIdentification of the most representative location for biopsy is critical in establishing
the definitive diagnosis of oral mucosal lesions. Currently, this process involves
visual evaluation of the colour characteristics of tissue aided by topical application of
contrast enhancing agents. Although, this approach is widely practiced, it remains
limited by its lack of objectivity in identifying and delineating suspicious areas for
biopsy. To overcome this drawback there is a need to introduce a technique that
would provide macroscopic guidance based on microscopic imaging and analysis.
Optical Coherence Tomography is an emerging high resolution biomedical imaging
modality that can potentially be used as an in vivo tool for selection of the most
appropriate site for biopsy. This thesis investigates the use of OCT for qualitative
and quantitative mapping of oral mucosal lesions. Feasibility studies were performed
on patient biopsy samples prior to histopathological processing using a commercial
OCT microscope. Qualitative imaging results examining a variety of normal, benign,
inflammatory and premalignant lesions of the oral mucosa will be presented.
Furthermore, the identification and utilisation of a common quantifiable parameter in
OCT and histology of images of normal and dysplastic oral epithelium will be
explored thus ensuring objective and reproducible mapping of the progression of oral
carcinogenesis. Finally, the selection of the most representative biopsy site of oral
epithelial dysplasia would be investigated using a novel approach, scattering
attenuation microscopy. It is hoped this approach may help convey more clinical
meaning than the conventional visualisation of OCT images
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