Banknote Authentication and Medical Image Diagnosis Using Feature Descriptors and Deep Learning Methods

Banknote recognition and medical image analysis have been the foci of image processing and pattern recognition research. As counterfeiters have taken advantage of the innovation in print media technologies for reproducing fake monies, hence the need to design systems which can reassure and protect citizens of the authenticity of banknotes in circulation. Similarly, many physicians must interpret medical images. But image analysis by humans is susceptible to error due to wide variations across interpreters, lethargy, and human subjectivity. Computer-aided diagnosis is vital to improvements in medical analysis, as they facilitate the identification of findings that need treatment and assist the expert’s workflow. Thus, this thesis is organized around three such problems related to Banknote Authentication and Medical Image Diagnosis. In our first research problem, we proposed a new banknote recognition approach that classifies the principal components of extracted HOG features. We further experimented on computing HOG descriptors from cells created from image patch vertices of SURF points and designed a feature reduction approach based on a high correlation and low variance filter. In our second research problem, we developed a mobile app for banknote identification and counterfeit detection using the Unity 3D software and evaluated its performance based on a Cascaded Ensemble approach. The algorithm was then extended to a client-server architecture using SIFT and SURF features reduced by Bag of Words and high correlation-based HOG vectors. In our third research problem, experiments were conducted on a pre-trained mobile app for medical image diagnosis using three convolutional layers with an Ensemble Classifier comprising PCA and bagging of five base learners. Also, we implemented a Bidirectional Generative Adversarial Network to mitigate the effect of the Binary Cross Entropy loss based on a Deep Convolutional Generative Adversarial Network as the generator and encoder with Capsule Network as the discriminator while experimenting on images with random composition and translation inferences. Lastly, we proposed a variant of the Single Image Super-resolution for medical analysis by redesigning the Super Resolution Generative Adversarial Network to increase the Peak Signal to Noise Ratio during image reconstruction by incorporating a loss function based on the mean square error of pixel space and Super Resolution Convolutional Neural Network layers

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

Tematski zbornik radova međunarodnog značaja. Tom 3 / Međunarodni naučni skup "Dani Arčibalda Rajsa", Beograd, 1-2. mart 2013

The Thematic Conference Proceedings contains 138 papers written by eminent scholars in the field of law, security, criminalistics, police studies, forensics, medicine, as well as members of national security system participating in education of the police, army and other security services from Russia, Ukraine, Belarus, China, Poland, Slovakia, Czech Republic, Hungary, Slovenia, Bosnia and Herzegovina, Montenegro, Republic of Srpska and Serbia. Each paper has been reviewed by two competent international reviewers, and the Thematic Conference Proceedings in whole has been reviewed by five international reviewers. The papers published in the Thematic Conference Proceedings contain the overview of con-temporary trends in the development of police educational system, development of the police and contemporary security, criminalistics and forensics, as well as with the analysis of the rule of law activities in crime suppression, situation and trends in the above-mentioned fields, and suggestions on how to systematically deal with these issues. The Thematic Conference Proceedings represents a significant contribution to the existing fund of scientific and expert knowledge in the field of criminalistic, security, penal and legal theory and practice. Publication of this Conference Proceedings contributes to improving of mutual cooperation between educational, scientific and expert institutions at national, regional and international level

Feature Selection and Classifier Development for Radio Frequency Device Identification

The proliferation of simple and low-cost devices, such as IEEE 802.15.4 ZigBee and Z-Wave, in Critical Infrastructure (CI) increases security concerns. Radio Frequency Distinct Native Attribute (RF-DNA) Fingerprinting facilitates biometric-like identification of electronic devices emissions from variances in device hardware. Developing reliable classifier models using RF-DNA fingerprints is thus important for device discrimination to enable reliable Device Classification (a one-to-many looks most like assessment) and Device ID Verification (a one-to-one looks how much like assessment). AFITs prior RF-DNA work focused on Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) and Generalized Relevance Learning Vector Quantized Improved (GRLVQI) classifiers. This work 1) introduces a new GRLVQI-Distance (GRLVQI-D) classifier that extends prior GRLVQI work by supporting alternative distance measures, 2) formalizes a framework for selecting competing distance measures for GRLVQI-D, 3) introducing response surface methods for optimizing GRLVQI and GRLVQI-D algorithm settings, 4) develops an MDA-based Loadings Fusion (MLF) Dimensional Reduction Analysis (DRA) method for improved classifier-based feature selection, 5) introduces the F-test as a DRA method for RF-DNA fingerprints, 6) provides a phenomenological understanding of test statistics and p-values, with KS-test and F-test statistic values being superior to p-values for DRA, and 7) introduces quantitative dimensionality assessment methods for DRA subset selection

Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population

Kernel-based distribution features for statistical tests and Bayesian inference

The kernel mean embedding is known to provide a data representation which preserves full information of the data distribution. While typically computationally costly, its nonparametric nature has an advantage of requiring no explicit model specification of the data. At the other extreme are approaches which summarize data distributions into a finite-dimensional vector of hand-picked summary statistics. This explicit finite-dimensional representation offers a computationally cheaper alternative. Clearly, there is a trade-off between cost and sufficiency of the representation, and it is of interest to have a computationally efficient technique which can produce a data-driven representation, thus combining the advantages from both extremes. The main focus of this thesis is on the development of linear-time mean-embedding-based methods to automatically extract informative features of data distributions, for statistical tests and Bayesian inference. In the first part on statistical tests, several new linear-time techniques are developed. These include a new kernel-based distance measure for distributions, a new linear-time nonparametric dependence measure, and a linear-time discrepancy measure between a probabilistic model and a sample, based on a Stein operator. These new measures give rise to linear-time and consistent tests of homogeneity, independence, and goodness of fit, respectively. The key idea behind these new tests is to explicitly learn distribution-characterizing feature vectors, by maximizing a proxy for the probability of correctly rejecting the null hypothesis. We theoretically show that these new tests are consistent for any finite number of features. In the second part, we explore the use of random Fourier features to construct approximate kernel mean embeddings, for representing messages in expectation propagation (EP) algorithm. The goal is to learn a message operator which predicts EP outgoing messages from incoming messages. We derive a novel two-layer random feature representation of the input messages, allowing online learning of the operator during EP inference