88 research outputs found
Advanced correlation-based character recognition applied to the Archimedes Palimpsest
The Archimedes Palimpsest is a manuscript containing the partial text of seven treatises by Archimedes that were copied onto parchment and bound in the tenth-century AD. This work is aimed at providing tools that allow scholars of ancient Greek mathematics to retrieve as much information as possible from images of the remaining degraded text. Acorrelation pattern recognition (CPR) system has been developed to recognize distorted versions of Greek characters in problematic regions of the palimpsest imagery, which have been obscured by damage from mold and fire, overtext, and natural aging. Feature vectors for each class of characters are constructed using a series of spatial correlation algorithms and corresponding performance metrics. Principal components analysis (PCA) is employed prior to classification to remove features corresponding to filtering schemes that performed poorly for the spatial characteristics of the selected region-of-interest. A probability is then assigned to each class, forming a character probability distribution based on relative distances from the class feature vectors to the ROI feature vector in principal component (PC) space. However, the current CPR system does not produce a single classification decision, as is common in most target detection problems, but instead has been designed to provide intermediate results that allow the user to apply his or her own decisions (or evidence) to arrive at a conclusion. To achieve this result, a probabilistic network has been incorporated into the recognition system. A probabilistic network represents a method for modeling the uncertainty in a system, and for this application, it allows information from the existing iv partial transcription and contextual knowledge from the user to be an integral part of the decision-making process. The CPR system was designed to provide a framework for future research in the area of spatial pattern recognition by accommodating a broad range of applications and the development of new filtering methods. For example, during preliminary testing, the CPR system was used to confirm the publication date of a fifteenth-century Hebrew colophon, and demonstrated success in the detection of registration markers in three-dimensional MRI breast imaging. In addition, a new correlation algorithm that exploits the benefits of linear discriminant analysis (LDA) and the inherent shift invariance of spatial correlation has been derived, implemented, and tested. Results show that this composite filtering method provides a high level of class discrimination while maintaining tolerance to withinclass distortions. With the integration of this algorithm into the existing filter library, this work completes each stage of a cyclic workflow using the developed CPR system, and provides the necessary tools for continued experimentation
Robust Subspace Estimation via Low-Rank and Sparse Decomposition and Applications in Computer Vision
PhDRecent advances in robust subspace estimation have made dimensionality reduction and
noise and outlier suppression an area of interest for research, along with continuous
improvements in computer vision applications. Due to the nature of image and video
signals that need a high dimensional representation, often storage, processing, transmission,
and analysis of such signals is a difficult task. It is therefore desirable to obtain a
low-dimensional representation for such signals, and at the same time correct for corruptions,
errors, and outliers, so that the signals could be readily used for later processing.
Major recent advances in low-rank modelling in this context were initiated by the work of
Cand`es et al. [17] where the authors provided a solution for the long-standing problem of
decomposing a matrix into low-rank and sparse components in a Robust Principal Component
Analysis (RPCA) framework. However, for computer vision applications RPCA
is often too complex, and/or may not yield desirable results. The low-rank component
obtained by the RPCA has usually an unnecessarily high rank, while in certain tasks
lower dimensional representations are required. The RPCA has the ability to robustly
estimate noise and outliers and separate them from the low-rank component, by a sparse
part. But, it has no mechanism of providing an insight into the structure of the sparse
solution, nor a way to further decompose the sparse part into a random noise and a structured
sparse component that would be advantageous in many computer vision tasks. As
videos signals are usually captured by a camera that is moving, obtaining a low-rank
component by RPCA becomes impossible. In this thesis, novel Approximated RPCA
algorithms are presented, targeting different shortcomings of the RPCA. The Approximated
RPCA was analysed to identify the most time consuming RPCA solutions, and
replace them with simpler yet tractable alternative solutions. The proposed method is
able to obtain the exact desired rank for the low-rank component while estimating a
global transformation to describe camera-induced motion. Furthermore, it is able to
decompose the sparse part into a foreground sparse component, and a random noise
part that contains no useful information for computer vision processing. The foreground
sparse component is obtained by several novel structured sparsity-inducing norms, that
better encapsulate the needed pixel structure in visual signals. Moreover, algorithms for
reducing complexity of low-rank estimation have been proposed that achieve significant
complexity reduction without sacrificing the visual representation of video and image
information. The proposed algorithms are applied to several fundamental computer
vision tasks, namely, high efficiency video coding, batch image alignment, inpainting,
and recovery, video stabilisation, background modelling and foreground segmentation,
robust subspace clustering and motion estimation, face recognition, and ultra high definition
image and video super-resolution. The algorithms proposed in this thesis including
batch image alignment and recovery, background modelling and foreground segmentation,
robust subspace clustering and motion segmentation, and ultra high definition
image and video super-resolution achieve either state-of-the-art or comparable results to
existing methods
Biometric Systems
Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications
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