Dimension reduction of image and audio space

The reduction of data necessary for storage or transmission is a desirable goal in the digital video and audio domain. Compression schemes strive to reduce the amount of storage space or bandwidth necessary to keep or move the data. Data reduction can be accomplished so that visually or audibly unnecessary data is removed or recoded thus aiding the compression phase of the data processing. The characterization and identification of data that can be successfully removed or reduced is the purpose of this work. New philosophy, theory and methods for data processing are presented towards the goal of data reduction. The philosophy and theory developed in this work establish a foundation for high speed data reduction suitable for multi-media applications. The developed methods encompass motion detection and edge detection as features of the systems. The philosophy of energy flow analysis in video processing enables the consideration of noise in digital video data. Research into noise versus motion leads to an efficient and successful method of identifying motion in a sequence. The research of the underlying statistical properties of vector quantization provides an insight into the performance characteristics of vector quantization and leads to successful improvements in application. The underlying statistical properties of the vector quantization process are analyzed and three theorems are developed and proved. The theorems establish the statistical distributions and probability densities of various metrics of the vector quantization process. From these properties, an intelligent and efficient algorithm design is developed and tested. The performance improvements in both time and quality are established through algorithm analysis and empirical testing. The empirical results are presented

Adaptive spatial image steganography and steganalysis using perceptual modelling and machine learning

Image steganography is a method for communicating secret messages under the cover images. A sender will embed the secret messages into the cover images according to an algorithm, and then the resulting image will be sent to the receiver. The receiver can extract the secret messages with the predefined algorithm. To counter this kind of technique, image steganalysis is proposed to detect the presence of secret messages. After many years of development, current image steganography uses the adaptive algorithm for embedding the secrets, which automatically finds the complex area in the cover source to avoid being noticed. Meanwhile, image steganalysis has also been advanced to universal steganalysis, which does not require the knowledge of the steganographic algorithm. With the development of the computational hardware, i.e., Graphical Processing Units (GPUs), some computational expensive techniques are now available, i.e., Convolutional Neural Networks (CNNs), which bring a large improvement in the detection tasks in image steganalysis. To defend against the attacks, new techniques are also being developed to improve the security of image steganography, these include designing more scientific cost functions, the key in adaptive steganography, and generating stego images from the knowledge of the CNNs. Several contributions are made for both image steganography and steganalysis in this thesis. Firstly, inspired by the Ranking Priority Profile (RPP), a new cost function for adaptive image steganography is proposed, which uses the two-dimensional Singular Spectrum Analysis (2D-SSA) and Weighted Median Filter (WMF) in the design. The RPP mainly includes three rules, i.e., the Complexity-First rule, the Clustering rule and the Spreading rule, to design a cost function. The 2D-SSA is employed in selecting the key components and clustering the embedding positions, which follows the Complexity-First rule and the Clustering rule. Also, the Spreading rule is followed to smooth the resulting image produced by 2D-SSA with WMF. The proposed algorithm has improved performance over four benchmarking approaches against non-shared selection channel attacks. It also provides comparable performance in selection-channel-aware scenarios, where the best results are observed when the relative payload is 0.3 bpp or larger. The approach is much faster than other model-based methods. Secondly, for image steganalysis, to tackle more complex datasets that are close to the real scenarios and to push image steganalysis further to real-life applications, an Enhanced Residual Network with self-attention ability, i.e., ERANet, is proposed. By employing a more mathematically sophisticated way to extract more effective features in the images and the global self-Attention technique, the ERANet can further capture the stego signal in the deeper layers, hence it is suitable for the more complex situations in the new datasets. The proposed Enhanced Low-Level Feature Representation Module can be easily mounted on other CNNs in selecting the most representative features. Although it comes with a slightly extra computational cost, comprehensive experiments on the BOSSbase and ALASKA#2 datasets have demonstrated the effectiveness of the proposed methodology. Lastly, for image steganography, with the knowledge from the CNNs, a novel postcost-optimization algorithm is proposed. Without modifying the original stego image and the original cost function of the steganography, and no need for training a Generative Adversarial Network (GAN), the proposed method mainly uses the gradient maps from a well-trained CNN to represent the cost, where the original cost map of the steganography is adopted to indicate the embedding positions. This method will smooth the gradient maps before adjusting the cost, which solves the boundary problem of the CNNs having multiple subnets. Extensive experiments have been carried out to validate the effectiveness of the proposed method, which provides state-of-the-art performance. In addition, compared to existing work, the proposed method is effcient in computing time as well. In short, this thesis has made three major contributions to image steganography and steganalysis by using perceptual modelling and machine learning. A novel cost function and a post-cost-optimization function have been proposed for adaptive spatial image steganography, which helps protect the secret messages. For image steganalysis, a new CNN architecture has also been proposed, which utilizes multiple techniques for providing state of-the-art performance. Future directions are also discussed for indicating potential research.Image steganography is a method for communicating secret messages under the cover images. A sender will embed the secret messages into the cover images according to an algorithm, and then the resulting image will be sent to the receiver. The receiver can extract the secret messages with the predefined algorithm. To counter this kind of technique, image steganalysis is proposed to detect the presence of secret messages. After many years of development, current image steganography uses the adaptive algorithm for embedding the secrets, which automatically finds the complex area in the cover source to avoid being noticed. Meanwhile, image steganalysis has also been advanced to universal steganalysis, which does not require the knowledge of the steganographic algorithm. With the development of the computational hardware, i.e., Graphical Processing Units (GPUs), some computational expensive techniques are now available, i.e., Convolutional Neural Networks (CNNs), which bring a large improvement in the detection tasks in image steganalysis. To defend against the attacks, new techniques are also being developed to improve the security of image steganography, these include designing more scientific cost functions, the key in adaptive steganography, and generating stego images from the knowledge of the CNNs. Several contributions are made for both image steganography and steganalysis in this thesis. Firstly, inspired by the Ranking Priority Profile (RPP), a new cost function for adaptive image steganography is proposed, which uses the two-dimensional Singular Spectrum Analysis (2D-SSA) and Weighted Median Filter (WMF) in the design. The RPP mainly includes three rules, i.e., the Complexity-First rule, the Clustering rule and the Spreading rule, to design a cost function. The 2D-SSA is employed in selecting the key components and clustering the embedding positions, which follows the Complexity-First rule and the Clustering rule. Also, the Spreading rule is followed to smooth the resulting image produced by 2D-SSA with WMF. The proposed algorithm has improved performance over four benchmarking approaches against non-shared selection channel attacks. It also provides comparable performance in selection-channel-aware scenarios, where the best results are observed when the relative payload is 0.3 bpp or larger. The approach is much faster than other model-based methods. Secondly, for image steganalysis, to tackle more complex datasets that are close to the real scenarios and to push image steganalysis further to real-life applications, an Enhanced Residual Network with self-attention ability, i.e., ERANet, is proposed. By employing a more mathematically sophisticated way to extract more effective features in the images and the global self-Attention technique, the ERANet can further capture the stego signal in the deeper layers, hence it is suitable for the more complex situations in the new datasets. The proposed Enhanced Low-Level Feature Representation Module can be easily mounted on other CNNs in selecting the most representative features. Although it comes with a slightly extra computational cost, comprehensive experiments on the BOSSbase and ALASKA#2 datasets have demonstrated the effectiveness of the proposed methodology. Lastly, for image steganography, with the knowledge from the CNNs, a novel postcost-optimization algorithm is proposed. Without modifying the original stego image and the original cost function of the steganography, and no need for training a Generative Adversarial Network (GAN), the proposed method mainly uses the gradient maps from a well-trained CNN to represent the cost, where the original cost map of the steganography is adopted to indicate the embedding positions. This method will smooth the gradient maps before adjusting the cost, which solves the boundary problem of the CNNs having multiple subnets. Extensive experiments have been carried out to validate the effectiveness of the proposed method, which provides state-of-the-art performance. In addition, compared to existing work, the proposed method is effcient in computing time as well. In short, this thesis has made three major contributions to image steganography and steganalysis by using perceptual modelling and machine learning. A novel cost function and a post-cost-optimization function have been proposed for adaptive spatial image steganography, which helps protect the secret messages. For image steganalysis, a new CNN architecture has also been proposed, which utilizes multiple techniques for providing state of-the-art performance. Future directions are also discussed for indicating potential research

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Contributions to the Content-Based Image Retrieval Using Pictorial Queris

L'accés massiu a les càmeres digitals, els ordinadors personals i a Internet, ha propiciat la creació de grans volums de dades en format digital. En aquest context, cada vegada adquireixen major rellevància totes aquelles eines dissenyades per organitzar la informació i facilitar la seva cerca.Les imatges són un cas particular de dades que requereixen tècniques específiques de descripció i indexació. L'àrea de la visió per computador encarregada de l'estudi d'aquestes tècniques rep el nom de Recuperació d'Imatges per Contingut, en anglès Content-Based Image Retrieval (CBIR). Els sistemes de CBIR no utilitzen descripcions basades en text sinó que es basen en característiques extretes de les pròpies imatges. En contrast a les més de 6000 llengües parlades en el món, les descripcions basades en característiques visuals representen una via d'expressió universal.La intensa recerca en el camp dels sistemes de CBIR s'ha aplicat en àrees de coneixement molt diverses. Així doncs s'han desenvolupat aplicacions de CBIR relacionades amb la medicina, la protecció de la propietat intel·lectual, el periodisme, el disseny gràfic, la cerca d'informació en Internet, la preservació dels patrimoni cultural, etc. Un dels punts importants d'una aplicació de CBIR resideix en el disseny de les funcions de l'usuari. L'usuari és l'encarregat de formular les consultes a partir de les quals es fa la cerca de les imatges. Nosaltres hem centrat l'atenció en aquells sistemes en què la consulta es formula a partir d'una representació pictòrica. Hem plantejat una taxonomia dels sistemes de consulta en composada per quatre paradigmes diferents: Consulta-segons-Selecció, Consulta-segons-Composició-Icònica, Consulta-segons-Esboç i Consulta-segons-Il·lustració. Cada paradigma incorpora un nivell diferent en el potencial expressiu de l'usuari. Des de la simple selecció d'una imatge, fins a la creació d'una il·lustració en color, l'usuari és qui pren el control de les dades d'entrada del sistema. Al llarg dels capítols d'aquesta tesi hem analitzat la influència que cada paradigma de consulta exerceix en els processos interns d'un sistema de CBIR. D'aquesta manera també hem proposat un conjunt de contribucions que hem exemplificat des d'un punt de vista pràctic mitjançant una aplicació final

Contributions to the content-based image retrieval using pictorial queries

Descripció del recurs: el 02 de novembre de 2010L'accés massiu a les càmeres digitals, els ordinadors personals i a Internet, ha propiciat la creació de grans volums de dades en format digital. En aquest context, cada vegada adquireixen major rellevància totes aquelles eines dissenyades per organitzar la informació i facilitar la seva cerca. Les imatges són un cas particular de dades que requereixen tècniques específiques de descripció i indexació. L'àrea de la visió per computador encarregada de l'estudi d'aquestes tècniques rep el nom de Recuperació d'Imatges per Contingut, en anglès Content-Based Image Retrieval (CBIR). Els sistemes de CBIR no utilitzen descripcions basades en text sinó que es basen en característiques extretes de les pròpies imatges. En contrast a les més de 6000 llengües parlades en el món, les descripcions basades en característiques visuals representen una via d'expressió universal. La intensa recerca en el camp dels sistemes de CBIR s'ha aplicat en àrees de coneixement molt diverses. Així doncs s'han desenvolupat aplicacions de CBIR relacionades amb la medicina, la protecció de la propietat intel·lectual, el periodisme, el disseny gràfic, la cerca d'informació en Internet, la preservació dels patrimoni cultural, etc. Un dels punts importants d'una aplicació de CBIR resideix en el disseny de les funcions de l'usuari. L'usuari és l'encarregat de formular les consultes a partir de les quals es fa la cerca de les imatges. Nosaltres hem centrat l'atenció en aquells sistemes en què la consulta es formula a partir d'una representació pictòrica. Hem plantejat una taxonomia dels sistemes de consulta en composada per quatre paradigmes diferents: Consulta-segons-Selecció, Consulta-segons-Composició-Icònica, Consulta-segons-Esboç i Consulta-segons-Il·lustració. Cada paradigma incorpora un nivell diferent en el potencial expressiu de l'usuari. Des de la simple selecció d'una imatge, fins a la creació d'una il·lustració en color, l'usuari és qui pren el control de les dades d'entrada del sistema. Al llarg dels capítols d'aquesta tesi hem analitzat la influència que cada paradigma de consulta exerceix en els processos interns d'un sistema de CBIR. D'aquesta manera també hem proposat un conjunt de contribucions que hem exemplificat des d'un punt de vista pràctic mitjançant una aplicació final

Automatic detection and classification of leukaemia cells

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Today, there is a substantial number of software and research groups that focus on the development of image processing software to extract useful information from medical images, in order to assist and improve patient diagnosis. The work presented in this thesis is centred on processing of images of blood and bone marrow smears of patients suffering from leukaemia, a common type of cancer. In general, cancer is due to aberrant gene expression, which is caused by either mutations or epigenetic changes in DNA. Poor diet and unhealthy lifestyle may trigger or contribute to these changes, although the underlying mechanism is often unknown. Importantly, many cancer types including leukaemia are curable and patient survival and treatment can be improved, subject to prompt diagnosis. In particular, this study focuses on Acute Myeloid Leukaemia (AML), which can be of eight distinct types (M0 to M7), with the main objective to develop a methodology to automatically detect and classify leukaemia cells into one of the above types. The data was collected from the Department of Haematology, Universiti Sains Malaysia, in Malaysia. Three main methods, namely Cellular Automata, Heuristic Search and classification using Neural Networks are facilitated. In the case of Cellular Automata, an improved method based on the 8-neighbourhood and rules were developed to remove noise from images and estimate the radius of the potential blast cells contained in them. The proposed methodology selects the starting points, corresponding to potential blast cells, for the subsequent seeded heuristic search. The Seeded Heuristic employs a new fitness function for blast cell detection. Furthermore, the WEKA software is utilised for classification of blast cells and hence images, into AML subtypes. As a result accuracy of 97.22% was achieved in the classification of blasts into M3 and other AML subtypes. Finally, these algorithms are integrated into an automated system for image processing. In brief, the research presented in this thesis involves the use of advanced computational techniques for processing and classification of medical images, that is, images of blood samples from patients suffering from leukaemia.The Institute of Higher Education of Malaysia and the Universiti Sains Islam Malaysia (USIM)

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition