232,204 research outputs found
Blood vessel segmentation in the analysis of retinal and diaphragm images
The segmentation and characterization of structures in medical images represents an
important part of the diagnostic and research procedures in medicine. This thesis focuses
on the characterization methods in two application fields that make use of two imaging
modalities. The first topic is the characterization of the blood vessel structure in the
human retina and the second is the characterization of diaphragm movement during
breathing. The imaged blood vessel structures are considered important landmarks in
both applications.
The framework for the retinal image processing and analysis starts with the testing
of five publicly available blood vessel segmentation methods for retinal images. The
parameters of the methods are optimized on five databases with the ground truth for
blood vessels. An approach for predicting the method parameters is proposed based on
the optimization results. The parameter prediction approach is then applied to obtain
vessel segmentation on a new database and an automatic approach to the blood vessel
classification and computation of the arteriovenous ratio is proposed and evaluated on
the new database.
The framework for the diaphragm image processing and analysis is based on the measurement
of diaphragm motion. The motion is characterized by a set of features quantifying
the amplitude and frequency of the breathing pattern, as well as a portion of the nonharmonic
movements that occur. In addition, a set of static features like the diaphragm
slope and height are proposed. Two approaches for the motion measurement are proposed
and compared. A statistical evaluation of the proposed features is performed by
comparing measurements from people with and without spinal findings.
The results from the retinal image processing and analysis revealed the possibility of the
successful prediction of the parameters of the blood vessel segmentation methods. The
automatic approach for the automatic arteriovenous ratio estimation revealed a stronger
association with blood pressure than the manually estimated ratio. The results from the
diaphragm image processing and analysis confirmed differences in the position, shape and
breathing patterns between the healthy people and people suffering from spinal findings.
The blood vessel structure was shown to be a reliable marker for characterizing the
diaphragm motion.Katedra kybernetik
Zernike velocity moments for sequence-based description of moving features
The increasing interest in processing sequences of images motivates development of techniques for sequence-based object analysis and description. Accordingly, new velocity moments have been developed to allow a statistical description of both shape and associated motion through an image sequence. Through a generic framework motion information is determined using the established centralised moments, enabling statistical moments to be applied to motion based time series analysis. The translation invariant Cartesian velocity moments suffer from highly correlated descriptions due to their non-orthogonality. The new Zernike velocity moments overcome this by using orthogonal spatial descriptions through the proven orthogonal Zernike basis. Further, they are translation and scale invariant. To illustrate their benefits and application the Zernike velocity moments have been applied to gait recognition—an emergent biometric. Good recognition results have been achieved on multiple datasets using relatively few spatial and/or motion features and basic feature selection and classification techniques. The prime aim of this new technique is to allow the generation of statistical features which encode shape and motion information, with generic application capability. Applied performance analyses illustrate the properties of the Zernike velocity moments which exploit temporal correlation to improve a shape's description. It is demonstrated how the temporal correlation improves the performance of the descriptor under more generalised application scenarios, including reduced resolution imagery and occlusion
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
We present a novel unsupervised deep learning framework for anomalous event
detection in complex video scenes. While most existing works merely use
hand-crafted appearance and motion features, we propose Appearance and Motion
DeepNet (AMDN) which utilizes deep neural networks to automatically learn
feature representations. To exploit the complementary information of both
appearance and motion patterns, we introduce a novel double fusion framework,
combining both the benefits of traditional early fusion and late fusion
strategies. Specifically, stacked denoising autoencoders are proposed to
separately learn both appearance and motion features as well as a joint
representation (early fusion). Based on the learned representations, multiple
one-class SVM models are used to predict the anomaly scores of each input,
which are then integrated with a late fusion strategy for final anomaly
detection. We evaluate the proposed method on two publicly available video
surveillance datasets, showing competitive performance with respect to state of
the art approaches.Comment: Oral paper in BMVC 201
A Multiresolution Census Algorithm for Calculating Vortex Statistics in Turbulent Flows
The fundamental equations that model turbulent flow do not provide much
insight into the size and shape of observed turbulent structures. We
investigate the efficient and accurate representation of structures in
two-dimensional turbulence by applying statistical models directly to the
simulated vorticity field. Rather than extract the coherent portion of the
image from the background variation, as in the classical signal-plus-noise
model, we present a model for individual vortices using the non-decimated
discrete wavelet transform. A template image, supplied by the user, provides
the features to be extracted from the vorticity field. By transforming the
vortex template into the wavelet domain, specific characteristics present in
the template, such as size and symmetry, are broken down into components
associated with spatial frequencies. Multivariate multiple linear regression is
used to fit the vortex template to the vorticity field in the wavelet domain.
Since all levels of the template decomposition may be used to model each level
in the field decomposition, the resulting model need not be identical to the
template. Application to a vortex census algorithm that records quantities of
interest (such as size, peak amplitude, circulation, etc.) as the vorticity
field evolves is given. The multiresolution census algorithm extracts coherent
structures of all shapes and sizes in simulated vorticity fields and is able to
reproduce known physical scaling laws when processing a set of voriticity
fields that evolve over time
Linguistically-driven framework for computationally efficient and scalable sign recognition
We introduce a new general framework for sign recognition from monocular video using limited quantities of annotated data. The novelty of the hybrid framework we describe here is that we exploit state-of-the art learning methods while also incorporating features based on what we know about the linguistic composition of lexical signs. In particular, we analyze hand shape, orientation, location, and motion trajectories, and then use CRFs to combine this linguistically significant information for purposes of sign recognition. Our robust modeling and recognition of these sub-components of sign production allow an efficient parameterization of the sign recognition problem as compared with purely data-driven methods. This parameterization enables a scalable and extendable time-series learning approach that advances the state of the art in sign recognition, as shown by the results reported here for recognition of isolated, citation-form, lexical signs from American Sign Language (ASL)
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