Image Fusion: A Review

At the present time, image fusion is considered as one of the types of integrated technology information, it has played a significant role in several domains and production of high-quality images. The goal of image fusion is blending information from several images, also it is fusing and keeping all the significant visual information that exists in the original images. Image fusion is one of the methods of field image processing. Image fusion is the process of merging information from a set of images to consist one image that is more informative and suitable for human and machine perception. It increases and enhances the quality of images for visual interpretation in different applications. This paper offers the outline of image fusion methods, the modern tendencies of image fusion and image fusion applications. Image fusion can be performed in the spatial and frequency domains. In the spatial domain is applied directly on the original images by merging the pixel values of the two or more images for purpose forming a fused image, while in the frequency domain the original images will decompose into multilevel coefficient and synthesized by using inverse transform to compose the fused image. Also, this paper presents a various techniques for image fusion in spatial and frequency domains such as averaging, minimum/maximum, HIS, PCA and transform-based techniques, etc.. Different quality measures have been explained in this paper to perform a comparison of these methods

Medical Diagnosis with Multimodal Image Fusion Techniques

Image Fusion is an effective approach utilized to draw out all the significant information from the source images, which supports experts in evaluation and quick decision making. Multi modal medical image fusion produces a composite fused image utilizing various sources to improve quality and extract complementary information. It is extremely challenging to gather every piece of information needed using just one imaging method. Therefore, images obtained from different modalities are fused Additional clinical information can be gleaned through the fusion of several types of medical image pairings. This study's main aim is to present a thorough review of medical image fusion techniques which also covers steps in fusion process, levels of fusion, various imaging modalities with their pros and cons, and  the major scientific difficulties encountered in the area of medical image fusion. This paper also summarizes the quality assessments fusion metrics. The various approaches used by image fusion algorithms that are presently available in the literature are classified into four broad categories i) Spatial fusion methods ii) Multiscale Decomposition based methods iii) Neural Network based methods and iv) Fuzzy Logic based methods. the benefits and pitfalls of the existing literature are explored and Future insights are suggested. Moreover, this study is anticipated to create a solid platform for the development of better fusion techniques in medical applications

Automated Complexity-Sensitive Image Fusion

To construct a complete representation of a scene with environmental obstacles such as fog, smoke, darkness, or textural homogeneity, multisensor video streams captured in diferent modalities are considered. A computational method for automatically fusing multimodal image streams into a highly informative and unified stream is proposed. The method consists of the following steps: 1. Image registration is performed to align video frames in the visible band over time, adapting to the nonplanarity of the scene by automatically subdividing the image domain into regions approximating planar patches 2. Wavelet coefficients are computed for each of the input frames in each modality 3. Corresponding regions and points are compared using spatial and temporal information across various scales 4. Decision rules based on the results of multimodal image analysis are used to combine thewavelet coefficients from different modalities 5. The combined wavelet coefficients are inverted to produce an output frame containing useful information gathered from the available modalities Experiments show that the proposed system is capable of producing fused output containing the characteristics of color visible-spectrum imagery while adding information exclusive to infrared imagery, with attractive visual and informational properties

A Human-Centric Approach to Data Fusion in Post-Disaster Managment: The Development of a Fuzzy Set Theory Based Model

It is critical to provide an efficient and accurate information system in the post-disaster phase for individuals\u27 in order to access and obtain the necessary resources in a timely manner; but current map based post-disaster management systems provide all emergency resource lists without filtering them which usually leads to high levels of energy consumed in calculation. Also an effective post-disaster management system (PDMS) will result in distribution of all emergency resources such as, hospital, storage and transportation much more reasonably and be more beneficial to the individuals in the post disaster period. In this Dissertation, firstly, semi-supervised learning (SSL) based graph systems was constructed for PDMS. A Graph-based PDMS\u27 resource map was converted to a directed graph that presented by adjacent matrix and then the decision information will be conducted from the PDMS by two ways, one is clustering operation, and another is graph-based semi-supervised optimization process. In this study, PDMS was applied for emergency resource distribution in post-disaster (responses phase), a path optimization algorithm based ant colony optimization (ACO) was used for minimizing the cost in post-disaster, simulation results show the effectiveness of the proposed methodology. This analysis was done by comparing it with clustering based algorithms under improvement ACO of tour improvement algorithm (TIA) and Min-Max Ant System (MMAS) and the results also show that the SSL based graph will be more effective for calculating the optimization path in PDMS. This research improved the map by combining the disaster map with the initial GIS based map which located the target area considering the influence of disaster. First, all initial map and disaster map will be under Gaussian transformation while we acquired the histogram of all map pictures. And then all pictures will be under discrete wavelet transform (DWT), a Gaussian fusion algorithm was applied in the DWT pictures. Second, inverse DWT (iDWT) was applied to generate a new map for a post-disaster management system. Finally, simulation works were proposed and the results showed the effectiveness of the proposed method by comparing it to other fusion algorithms, such as mean-mean fusion and max-UD fusion through the evaluation indices including entropy, spatial frequency (SF) and image quality index (IQI). Fuzzy set model were proposed to improve the presentation capacity of nodes in this GIS based PDMS

Cardiovascular information for improving biometric recognition

Mención Internacional en el título de doctorThe improvements of the last two decades in data modeling and computing have lead to new biometric modalities. The Electrocardiogram (ECG) modality is part of them, and has been mainly researched by using public databases related to medical training. Despite of being useful for initial approaches, they are not representative of a real biometric environment. In addition, publishing and creating a new database is none trivial due to human resources and data protection laws. The main goal of this thesis is to successfully use ECG as a biometric signal while getting closer to the real case scenario. Every experiment considers low computational calculations and transformations to help in potential portability. The core experiments in this work come from a private database with different positions, time and heart rate scenarios. An initial segmentation evaluation is achieved with the help of fiducial point detection which determines the QRS selection as the input data for all the experiments. The approach of training a model per user (open-set) is tested with different machine learning algorithms, only getting an acceptable result with Gaussian Mixture Models (GMM). However, the concept of training all users in one model (closed-set) shows more potential with Linear Discriminant Analysis (LDA), whose results were improved in 40%. The results with LDA are also tested as a multi-modality technique, decreasing the Equal Error Rate (EER) of fingerprint verification in up to 70.64% with score fusion, and reaching 0% in Protection Attack Detection (PAD). The Multilayer Perceptron (MLP) algorithm enhances these results in verification while applying the first differentiation to the signal. The network optimization is achieved with EER as an observation metric, and improves the results of LDA in 22% for the worst case scenario, and decreases the EER to 0% in the best case. Complexity is added creating a Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) based network, BioECG. The tuning process is achieved without extra feature transformation and is evaluated through accuracy, aiming for good identification. The inclusion of a second day of enrollment in improves results from MLP, reaching the overall lowest results of 0.009%–1.352% in EER. Throughout the use of good quality signals, position changes did not noticeably impact the verification. In addition, collecting data in a different day or in a different hour did not clearly affect the performance. Moreover, modifying the verification process based on attempts, improves the overall results, up to reach a 0% EER when applying BioECG. Finally, to get closer to a real scenario, a smartband prototype is used to collect new databases. A private database with limited scenarios but controlled data, and another local database with a wider range of scenarios and days, and with a more relaxed use of the device. Applying the concepts of first differentiation and MLP, these signals required the Stationary Wavelet Transform (SWT) and new fiducial point detection to improve their results. The first database gave subtle chances of being used in identification with up to 78.2% accuracy, but the latter was completely discarded for this purpose. These realistic experiments show the impact of a low fidelity sensor, even considering the same modifications in previous successful experiments with better quality data, reaching up to 13.530% EER. In the second database, results reach a range of 0.068%–31.669% EER. This type of sensor is affected by heart rate changes, but also by position variations, given its sensitivity to movement.Las mejoras en modelado de datos y computación de las últimas dos décadas, han llevado a la creación de nuevas modalidades biométricas. La modalidad de electrocardiograma (ECG) es una de ellas, la cual se ha investigado usando bases de datos públicas que fueron creadas para entrenamiento de profesional médico. Aunque estos datos han sido útiles para los estados iniciales de la modalidad, no son representativos de un entorno biométrico real. Además, publicar y crear bases de datos nuevas son problemas no triviales debido a los recursos humanos y las leyes de protección de datos. El principal objetivo de esta tesis es usar exitosamente datos de ECG como señales biométricas a la vez que nos acercamos a un escenario realista. Cada experimento considera cálculos y transformadas de bajo coste computacional para ayudar en su potencial uso en aparatos móviles. Los principales experimentos de este trabajo se producen con una base de datos privada con diferentes escenarios en términos de postura, tiempo y frecuencia cardíaca. Con ella se evalúan las diferentes seleccións del complejo QRS mediante detección de puntos fiduciales, lo cual servirá como datos de entrada para el resto de experimentos. El enfoque de entrenar un modelo por usuario (open-set) se prueba con diferentes algoritmos de aprendizaje máquina (machine learning), obteniendo resultados aceptables únicamente mediante el uso de modelos de mezcla de Gaussianas (Gaussian Mixture Models, GMM). Sin embargo, el concepto de entrenar un modelo con todos los usuarios (closed-set) demuestra mayor potencial con Linear Discriminant Analysis (Análisis de Discriminante Lineal, LDA), cuyos resultados mejoran en un 40%. Los resultados de LDA también se utilizan como técnica multi-modal, disminuyendo la Equal Error Rate (Tasa de Igual Error, EER) de la verificación mediante huella en hasta un 70.64% con fusión de puntuación, y llegando a un sistema con un 0% de EER en Detección de Ataques de Presentación (Presentation Attack Detection, PAD). El algoritmo de Perceptrón Multicapa (Multilayer Perceptron, MLP) mejora los resultados previos en verificación aplicando la primera derivada a la señal. La optimización de la red se consigue en base a su EER, mejora la de LDA en hasta un 22% en el peor caso, y la lleva hasta un 0% en el mejor caso. Se añade complejidad creando una red neural convolucional (Convolutional Neural Network, CNN) con una red de memoria a largo-corto plazo (Long-Short Term Memory, LSTM), llamada BioECG. El proceso de ajuste de hiperparámetros se lleva acabo sin transformaciones y se evalúa observando la accuracy (precisión), para mejorar la identificación. Sin embargo, incluir un segundo día de registro (enrollment) con BioECG, estos resultados mejoran hasta un 74% para el peor caso, llegando a los resultados más bajos hasta el momento con 0.009%–1.352% en la EER. Durante el uso de señales de buena calidad, los cambios de postura no afectaron notablemente a la verificación. Además, adquirir los datos en días u horas diferentes tampoco afectó claramente a los resultados. Asimismo, modificar el proceso de verificación en base a intentos también produce mejoría en todos los resultados, hasta el punto de llegar a un 0% de EER cuando se aplica BioECG. Finalmente, para acercarnos al caso más realista, se usa un prototipo de pulsera para capturar nuevas bases de datos. Una base de datos privada con escenarios limitados pero datos más controlados, y otra base de datos local con más espectro de escenarios y días y un uso del dispositivo más relajado. Para estos datos se aplican los conceptos de primera diferenciación en MLP, cuyas señales requieren la Transformada de Wavelet Estacionaria (Stationary Wavelet Transform, SWT) y un detector de puntos fiduciales para mejorar los resultados. La primera base de datos da opciones a ser usada para identificación con un máximo de precisión del 78.2%, pero la segunda se descartó completamente para este propósito. Estos experimentos más realistas demuestran el impact de tener un sensor de baja fidelidad, incluso considerando las mismas modificaciones que previamente tuvieron buenos resultados en datos mejores, llegando a un 13.530% de EER. En la segunda base de datos, los resultados llegan a un rango de 0.068%–31.669% en EER. Este tipo de sensor se ve afectado por las variaciones de frecuencia cardíaca, pero también por el cambio de posición, dado que es más sensible al movimiento.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Cristina Conde Vilda.- Secretario: Mariano López García.- Vocal: Young-Bin Know

Unmasking the imposters: towards improving the generalisation of deep learning methods for face presentation attack detection.

Identity theft has had a detrimental impact on the reliability of face recognition, which has been extensively employed in security applications. The most prevalent are presentation attacks. By using a photo, video, or mask of an authorized user, attackers can bypass face recognition systems. Fake presentation attacks are detected by the camera sensors of face recognition systems using face presentation attack detection. Presentation attacks can be detected using convolutional neural networks, commonly used in computer vision applications. An in-depth analysis of current deep learning methods is used in this research to examine various aspects of detecting face presentation attacks. A number of new techniques are implemented and evaluated in this study, including pre-trained models, manual feature extraction, and data aggregation. The thesis explores the effectiveness of various machine learning and deep learning models in improving detection performance by using publicly available datasets with different dataset partitions than those specified in the official dataset protocol. Furthermore, the research investigates how deep models and data aggregation can be used to detect face presentation attacks, as well as a novel approach that combines manual features with deep features in order to improve detection accuracy. Moreover, task-specific features are also extracted using pre-trained deep models to enhance the performance of detection and generalisation further. This problem is motivated by the need to achieve generalization against new and rapidly evolving attack variants. It is possible to extract identifiable features from presentation attack variants in order to detect them. However, new methods are needed to deal with emerging attacks and improve the generalization capability. This thesis examines the necessary measures to detect face presentation attacks in a more robust and generalised manner

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches