Region-Based Image-Fusion Framework for Compressive Imaging

A novel region-based image-fusion framework for compressive imaging (CI) and its implementation scheme are proposed. Unlike previous works on conventional image fusion, we consider both compression capability on sensor side and intelligent understanding of the image contents in the image fusion. Firstly, the compressed sensing theory and normalized cut theory are introduced. Then region-based image-fusion framework for compressive imaging is proposed and its corresponding fusion scheme is constructed. Experiment results demonstrate that the proposed scheme delivers superior performance over traditional compressive image-fusion schemes in terms of both object metrics and visual quality

BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator

A study of information-theoretic metaheuristics applied to functional neuroimaging datasets

This dissertation presents a new metaheuristic related to a two-dimensional ensemble empirical mode decomposition (2DEEMD). It is based on Green’s functions and is called Green’s Function in Tension - Bidimensional Empirical Mode Decomposition (GiT-BEMD). It is employed for decomposing and extracting hidden information of images. A natural image (face image) as well as images with artificial textures have been used to test and validate the proposed approach. Images are selected to demonstrate efficiency and performance of the GiT-BEMD algorithm in extracting textures on various spatial scales from the different images. In addition, a comparison of the performance of the new algorithm GiT-BEMD with a canonical BEEMD is discussed. Then, GiT-BEMD as well as canonical bidimensional EEMD (BEEMD) are applied to an fMRI study of a contour integration task. Thus, it explores the potential of employing GiT-BEMD to extract such textures, so-called bidimensional intrinsic mode functions (BIMFs), of functional biomedical images. Because of the enormous computational load and the artifacts accompanying the extracted textures when using a canonical BEEMD, GiT-BEMD is developed to cope with such challenges. It is seen that the computational cost is decreased dramatically, and the quality of the extracted textures is enhanced considerably. Consequently, GiT-BEMD achieves a higher quality of the estimated BIMFs as can be seen from a direct comparison of the results obtained with different variants of BEEMD and GiT-BEMD. Moreover, results generated by 2DBEEMD, especially in case of GiT-BEMD, distinctly show a superior precision in spatial localization of activity blobs when compared with a canonical general linear model (GLM) analysis employing statistical parametric mapping (SPM). Furthermore, to identify most informative textures, i.e. BIMFs, a support vector machine (SVM) as well as a random forest (RF) classifier is employed. Classification performance demonstrates the potential of the extracted BIMFs in supporting decision making of the classifier. With GiT-BEMD, the classification performance improved significantly which might also be a consequence of a clearer structure for these modes compared to the ones obtained with canonical BEEMD. Altogether, there is strong believe that the newly proposed metaheuristic GiT-BEMD offers a highly competitive alternative to existing BEMD algorithms and represents a promising technique for blindly decomposing images and extracting textures thereof which may be used for further analysis

Face recognition by means of advanced contributions in machine learning

Face recognition (FR) has been extensively studied, due to both scientific fundamental challenges and current and potential applications where human identification is needed. FR systems have the benefits of their non intrusiveness, low cost of equipments and no useragreement requirements when doing acquisition, among the most important ones. Nevertheless, despite the progress made in last years and the different solutions proposed, FR performance is not yet satisfactory when more demanding conditions are required (different viewpoints, blocked effects, illumination changes, strong lighting states, etc). Particularly, the effect of such non-controlled lighting conditions on face images leads to one of the strongest distortions in facial appearance. This dissertation addresses the problem of FR when dealing with less constrained illumination situations. In order to approach the problem, a new multi-session and multi-spectral face database has been acquired in visible, Near-infrared (NIR) and Thermal infrared (TIR) spectra, under different lighting conditions. A theoretical analysis using information theory to demonstrate the complementarities between different spectral bands have been firstly carried out. The optimal exploitation of the information provided by the set of multispectral images has been subsequently addressed by using multimodal matching score fusion techniques that efficiently synthesize complementary meaningful information among different spectra. Due to peculiarities in thermal images, a specific face segmentation algorithm has been required and developed. In the final proposed system, the Discrete Cosine Transform as dimensionality reduction tool and a fractional distance for matching were used, so that the cost in processing time and memory was significantly reduced. Prior to this classification task, a selection of the relevant frequency bands is proposed in order to optimize the overall system, based on identifying and maximizing independence relations by means of discriminability criteria. The system has been extensively evaluated on the multispectral face database specifically performed for our purpose. On this regard, a new visualization procedure has been suggested in order to combine different bands for establishing valid comparisons and giving statistical information about the significance of the results. This experimental framework has more easily enabled the improvement of robustness against training and testing illumination mismatch. Additionally, focusing problem in thermal spectrum has been also addressed, firstly, for the more general case of the thermal images (or thermograms), and then for the case of facialthermograms from both theoretical and practical point of view. In order to analyze the quality of such facial thermograms degraded by blurring, an appropriate algorithm has been successfully developed. Experimental results strongly support the proposed multispectral facial image fusion, achieving very high performance in several conditions. These results represent a new advance in providing a robust matching across changes in illumination, further inspiring highly accurate FR approaches in practical scenarios.El reconeixement facial (FR) ha estat àmpliament estudiat, degut tant als reptes fonamentals científics que suposa com a les aplicacions actuals i futures on requereix la identificació de les persones. Els sistemes de reconeixement facial tenen els avantatges de ser no intrusius,presentar un baix cost dels equips d’adquisició i no la no necessitat d’autorització per part de l’individu a l’hora de realitzar l'adquisició, entre les més importants. De totes maneres i malgrat els avenços aconseguits en els darrers anys i les diferents solucions proposades, el rendiment del FR encara no resulta satisfactori quan es requereixen condicions més exigents (diferents punts de vista, efectes de bloqueig, canvis en la il·luminació, condicions de llum extremes, etc.). Concretament, l'efecte d'aquestes variacions no controlades en les condicions d'il·luminació sobre les imatges facials condueix a una de les distorsions més accentuades sobre l'aparença facial. Aquesta tesi aborda el problema del FR en condicions d'il·luminació menys restringides. Per tal d'abordar el problema, hem adquirit una nova base de dades de cara multisessió i multiespectral en l'espectre infraroig visible, infraroig proper (NIR) i tèrmic (TIR), sota diferents condicions d'il·luminació. En primer lloc s'ha dut a terme una anàlisi teòrica utilitzant la teoria de la informació per demostrar la complementarietat entre les diferents bandes espectrals objecte d’estudi. L'òptim aprofitament de la informació proporcionada pel conjunt d'imatges multiespectrals s'ha abordat posteriorment mitjançant l'ús de tècniques de fusió de puntuació multimodals, capaces de sintetitzar de manera eficient el conjunt d’informació significativa complementària entre els diferents espectres. A causa de les característiques particulars de les imatges tèrmiques, s’ha requerit del desenvolupament d’un algorisme específic per la segmentació de les mateixes. En el sistema proposat final, s’ha utilitzat com a eina de reducció de la dimensionalitat de les imatges, la Transformada del Cosinus Discreta i una distància fraccional per realitzar les tasques de classificació de manera que el cost en temps de processament i de memòria es va reduir de forma significa. Prèviament a aquesta tasca de classificació, es proposa una selecció de les bandes de freqüències més rellevants, basat en la identificació i la maximització de les relacions d'independència per mitjà de criteris discriminabilitat, per tal d'optimitzar el conjunt del sistema. El sistema ha estat àmpliament avaluat sobre la base de dades de cara multiespectral, desenvolupada pel nostre propòsit. En aquest sentit s'ha suggerit l’ús d’un nou procediment de visualització per combinar diferents bandes per poder establir comparacions vàlides i donar informació estadística sobre el significat dels resultats. Aquest marc experimental ha permès més fàcilment la millora de la robustesa quan les condicions d’il·luminació eren diferents entre els processos d’entrament i test. De forma complementària, s’ha tractat la problemàtica de l’enfocament de les imatges en l'espectre tèrmic, en primer lloc, pel cas general de les imatges tèrmiques (o termogrames) i posteriorment pel cas concret dels termogrames facials, des dels punt de vista tant teòric com pràctic. En aquest sentit i per tal d'analitzar la qualitat d’aquests termogrames facials degradats per efectes de desenfocament, s'ha desenvolupat un últim algorisme. Els resultats experimentals recolzen fermament que la fusió d'imatges facials multiespectrals proposada assoleix un rendiment molt alt en diverses condicions d’il·luminació. Aquests resultats representen un nou avenç en l’aportació de solucions robustes quan es contemplen canvis en la il·luminació, i esperen poder inspirar a futures implementacions de sistemes de reconeixement facial precisos en escenaris no controlats.Postprint (published version

Kernel Feature Extraction Methods for Remote Sensing Data Analysis

Technological advances in the last decades have improved our capabilities of collecting and storing high data volumes. However, this makes that in some fields, such as remote sensing several problems are generated in the data processing due to the peculiar characteristics of their data. High data volume, high dimensionality, heterogeneity and their nonlinearity, make that the analysis and extraction of relevant information from these images could be a bottleneck for many real applications. The research applying image processing and machine learning techniques along with feature extraction, allows the reduction of the data dimensionality while keeps the maximum information. Therefore, developments and applications of feature extraction methodologies using these techniques have increased exponentially in remote sensing. This improves the data visualization and the knowledge discovery. Several feature extraction methods have been addressed in the literature depending on the data availability, which can be classified in supervised, semisupervised and unsupervised. In particular, feature extraction can use in combination with kernel methods (nonlinear). The process for obtaining a space that keeps greater information content is facilitated by this combination. One of the most important properties of the combination is that can be directly used for general tasks including classification, regression, clustering, ranking, compression, or data visualization. In this Thesis, we address the problems of different nonlinear feature extraction approaches based on kernel methods for remote sensing data analysis. Several improvements to the current feature extraction methods are proposed to transform the data in order to make high dimensional data tasks easier, such as classification or biophysical parameter estimation. This Thesis focus on three main objectives to reach these improvements in the current feature extraction methods: The first objective is to include invariances into supervised kernel feature extraction methods. Throughout these invariances it is possible to generate virtual samples that help to mitigate the problem of the reduced number of samples in supervised methods. The proposed algorithm is a simple method that essentially generates new (synthetic) training samples from available labeled samples. These samples along with original samples should be used in feature extraction methods obtaining more independent features between them that without virtual samples. The introduction of prior knowledge by means of the virtual samples could obtain classification and biophysical parameter estimation methods more robust than without them. The second objective is to use the generative kernels, i.e. probabilistic kernels, that directly learn by means of clustering techniques from original data by finding local-to-global similarities along the manifold. The proposed kernel is useful for general feature extraction purposes. Furthermore, the kernel attempts to improve the current methods because the kernel not only contains labeled data information but also uses the unlabeled information of the manifold. Moreover, the proposed kernel is parameter free in contrast with the parameterized functions such as, the radial basis function (RBF). Using probabilistic kernels is sought to obtain new unsupervised and semisupervised methods in order to reduce the number and cost of labeled data in remote sensing. Third objective is to develop new kernel feature extraction methods for improving the features obtained by the current methods. Optimizing the functional could obtain improvements in new algorithm. For instance, the Optimized Kernel Entropy Component Analysis (OKECA) method. The method is based on the Independent Component Analysis (ICA) framework resulting more efficient than the standard Kernel Entropy Component Analysis (KECA) method in terms of dimensionality reduction. In this Thesis, the methods are focused on remote sensing data analysis. Nevertheless, feature extraction methods are used to analyze data of several research fields whereas data are multidimensional. For these reasons, the results are illustrated into experimental sequence. First, the projections are analyzed by means of Toy examples. The algorithms are tested through standard databases with supervised information to proceed to the last step, the analysis of remote sensing images by the proposed methods

High Performance Video Stream Analytics System for Object Detection and Classification

Due to the recent advances in cameras, cell phones and camcorders, particularly the resolution at which they can record an image/video, large amounts of data are generated daily. This video data is often so large that manually inspecting it for object detection and classification can be time consuming and error prone, thereby it requires automated analysis to extract useful information and meta-data. The automated analysis from video streams also comes with numerous challenges such as blur content and variation in illumination conditions and poses. We investigate an automated video analytics system in this thesis which takes into account the characteristics from both shallow and deep learning domains. We propose fusion of features from spatial frequency domain to perform highly accurate blur and illumination invariant object classification using deep learning networks. We also propose the tuning of hyper-parameters associated with the deep learning network through a mathematical model. The mathematical model used to support hyper-parameter tuning improved the performance of the proposed system during training. The outcomes of various hyper-parameters on system's performance are compared. The parameters that contribute towards the most optimal performance are selected for the video object classification. The proposed video analytics system has been demonstrated to process a large number of video streams and the underlying infrastructure is able to scale based on the number and size of the video stream(s) being processed. The extensive experimentation on publicly available image and video datasets reveal that the proposed system is significantly more accurate and scalable and can be used as a general purpose video analytics system.N/

Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding

The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child's outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field

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