Ultrasound Imaging

In this book, we present a dozen state of the art developments for ultrasound imaging, for example, hardware implementation, transducer, beamforming, signal processing, measurement of elasticity and diagnosis. The editors would like to thank all the chapter authors, who focused on the publication of this book

Automated analysis of 3D echocardiography

In this thesis we aim at automating the analysis of 3D echocardiography, mainly targeting the functional analysis of the left ventricle. Manual analysis of these data is cumbersome, time-consuming and is associated with inter-observer and inter-institutional variability. Methods for reconstruction of 3D echocardiographic images from fast rotating ultrasound transducers is presented and methods for analysis of 3D echocardiography in general, using tracking, detection and model-based segmentation techniques to ultimately fully automatically segment the left ventricle for functional analysis. We show that reliable quantification of left ventricular volume and mitral valve displacement can be achieved using the presented techniques.SenterNovem (IOP Beeldverwerking, grant IBVC02003), Dutch Technology Foundation STW (grant 06666)UBL - phd migration 201

Distributed Compressed Representation of Correlated Image Sets

Vision sensor networks and video cameras find widespread usage in several applications that rely on effective representation of scenes or analysis of 3D information. These systems usually acquire multiple images of the same 3D scene from different viewpoints or at different time instants. Therefore, these images are generally correlated through displacement of scene objects. Efficient compression techniques have to exploit this correlation in order to efficiently communicate the 3D scene information. Instead of joint encoding that requires communication between the cameras, in this thesis we concentrate on distributed representation, where the captured images are encoded independently, but decoded jointly to exploit the correlation between images. One of the most important and challenging tasks relies in estimation of the underlying correlation from the compressed correlated images for effective reconstruction or analysis in the joint decoder. This thesis focuses on developing efficient correlation estimation algorithms and joint representation of multiple correlated images captured by various sensing methodologies, e.g., planar, omnidirectional and compressive sensing (CS) sensors. The geometry of the 2D visual representation and the acquisition complexity vary for each sensor type. Therefore, we need to carefully consider the specific geometric nature of the captured images while developing distributed representation algorithms. In this thesis we propose robust algorithms in different scene analysis and reconstruction scenarios. We first concentrate on the distributed representation of omnidirectional images captured by catadioptric sensors. The omnidirectional images are captured from different viewpoints and encoded independently with a balanced rate distribution among the different cameras. They are mapped on the sphere which captures the plenoptic function in its radial form without Euclidean discrepancies. We propose a transform-based distributed coding algorithm, where the spherical images initially undergo a multi-resolution decomposition. The visual information is then split into two correlated partitions. The encoder transmits one partition after entropy coding, as well as the syndrome bits resulting from the Slepian-Wolf encoding of the other partition. The joint decoder estimates a disparity image to take benefit of the correlation between views and uses the syndrome bits to decode the missing information. Such a strategy proves to be beneficial with respect to the independent processing of images and shows only a small performance loss compared to the joint encoding of different views. The encoding complexity in the previous approach is non-negligible due to the visual information processing based on Slepian-Wolf coding and its associated rate parameter estimation. We therefore discard the Slepian-Wolf encoding and propose a distributed coding solution, where the correlated images are encoded independently using transform-based coding solutions (e.g., SPIHT). The central decoder now builds a correlation model from the compressed images, which is used to jointly decode a pair of images. Experimental results demonstrate that the proposed distributed coding solution improves the rate-distortion performance of the separate coding results for both planar and omnidirectional images. However, this improvement is significant only at medium to high bit rates. We therefore propose a rate allocation scheme that identifies and transmits the necessary visual information from each image to improve the correlation estimation accuracy at low bit rate. Experimental results show that for a given bit budget the proposed encoding scheme permits to compute an accurate correlation estimation comparing to the one obtained with SPIHT, JPEG 2000 or JPEG coding schemes. We show however that the improvement in the correlation estimation comes at the price of penalizing the image reconstruction quality; therefore there exists an interesting trade-off between the accurate correlation estimation and image reconstruction as encoding optimization objectives are different in both cases. Next, we further simplify the encoding complexity by replacing the classical imaging sensors with the simple CS sensors, that directly acquire the compressed images in the form of quantized linear measurements. We now concentrate on the particular problem, where one image is selected as the reference and it is used as a side information for the correlation estimation. We propose a geometry-based model to describe the correlation between the visual information in a pair of images. The joint decoder first captures the most prominent visual features in the reconstructed reference image using geometric functions. Since the images are correlated, these features are likely to be present in the other images too, possibly with geometric transformations. Hence, we propose to estimate the correlation model with a regularized optimization problem that locates these features in the compressed images. The regularization terms enforce smoothness of the transformation field, and consistency between the estimated images and the quantized measurements. Experimental results show that the proposed scheme is able to efficiently estimate the correlation between images for several multi-view and video datasets. The proposed scheme is finally shown to outperform DSC schemes based on unsupervised disparity (or motion) learning, as well as independent coding solutions based on JPEG 2000. We then extend the previous scenario to a symmetric decoding problem, where we are interested to estimate the correlation model directly from the quantized linear measurements without explicitly reconstructing the reference images. We first show that the motion field that represents the main source of correlation between images can be described as a linear operator. We further derive a linear relationship between the correlated measurements in the compressed domain. We then derive a regularized cost function to estimate the correlation model directly in the compressed domain using graph-based optimization algorithms. Experimental results show that the proposed scheme estimates an accurate correlation model among images in both multi-view and video imaging scenarios. We then propose a robust data fidelity term that improves the quality of the correlation estimation when the measurements are quantized. Finally, we show by experiments that the proposed compressed correlation estimation scheme is able to compete the solution of a scheme that estimates a correlation model from the reconstructed images without the complexity of image reconstruction. Finally, we study the benefit of using the correlation information while jointly reconstructing the images from the compressed linear measurements. We consider both the asymmetric and symmetric scenarios described previously. We propose joint reconstruction methodologies based on a constrained optimization problem which is solved using effective proximal splitting methods. The constraints included in our framework enforce the reconstructed images to satisfy both the correlation and the quantized measurements consistency objectives. Experimental results demonstrate that the proposed joint reconstruction scheme improves the quality of the decoded images, when compared to a scheme where the images are handled independently. In this thesis we build efficient distributed scene representation algorithms for the multiple correlated images captured in planar, omnidirectional and CS cameras. The coding rate in our symmetric distributed coding solution stays balanced between the encoders and stays close to the joint encoding solutions. Our novel algorithms lead to effective correlation estimation in different sensing and coding scenarios. In addition, we provide innovative solutions for robust correlation estimation from highly compressed images in simple sensing frameworks. Our CS-based joint reconstruction frameworks effectively exploit the inter-view correlation, that permits to achieve high compression gains compared to state-of-the-art independent and distributed coding solutions

Aeronautical engineering: A continuing bibliography with indexes (supplement 289)

This bibliography lists 792 reports, articles, and other documents introduced into the NASA scientific and technical information system in Mar. 1993. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

DEEP INFERENCE ON MULTI-SENSOR DATA

Computer vision-based intelligent autonomous systems engage various types of sensors to perceive the world they navigate in. Vision systems perceive their environments through inferences on entities (structures, humans) and their attributes (pose, shape, materials) that are sensed using RGB and Near-InfraRed (NIR) cameras, LAser Detection And Ranging (LADAR), radar and so on. This leads to challenging and interesting problems in efficient data-capture, feature extraction, and attribute estimation, not only for RGB but various other sensors. In some cases, we encounter very limited amounts of labeled training data. In certain other scenarios we have sufficient data, but annotations are unavailable for supervised learning. This dissertation explores two approaches to learning under conditions of minimal to no ground truth. The first approach applies projections on training data that make learning efficient by improving training dynamics. The first and second topics in this dissertation belong to this category. The second approach makes learning without ground-truth possible via knowledge transfer from a labeled source domain to an unlabeled target domain through projections to domain-invariant shared latent spaces. The third and fourth topics in this dissertation belong to this category. For the first topic we study the feasibility and efficacy of identifying shapes in LADAR data in several measurement modes. We present results on efficient parameter learning with less data (for both traditional machine learning as well as deep models) on LADAR images. We use a LADAR apparatus to obtain range information from a 3-D scene by emitting laser beams and collecting the reflected rays from target objects in the region of interest. The Agile Beam LADAR concept makes the measurement and interpretation process more efficient using a software-defined architecture that leverages computational imaging principles. Using these techniques, we show that object identification and scene understanding can be accurately performed in the LADARmeasurement domain thereby rendering the efforts of pixel-based scene reconstruction superfluous. Next, we explore the effectiveness of deep features extracted by Convolutional Neural Networks (CNNs) in the Discrete Cosine Transform (DCT) domain for various image classification tasks such as pedestrian and face detection, material identification and object recognition. We perform the DCT operation on the feature maps generated by convolutional layers in CNNs. We compare the performance of the same network with the same hyper-parameters with or without the DCT step. Our results indicate that a DCT operation incorporated into the network after the first convolution layer can have certain advantages such as convergence over fewer training epochs and sparser weight matrices that are more conducive to pruning and hashing techniques. Next, we present an adversarial deep domain adaptation (ADA)-based approach for training deep neural networks that fit 3Dmeshes on humans in monocular RGB input images. Estimating a 3D mesh from a 2D image is helpful in harvesting complete 3Dinformation about body pose and shape. However, learning such an estimation task in a supervised way is challenging owing to the fact that ground truth 3D mesh parameters for real humans do not exist. We propose a domain adaptation based single-shot (no re-projection, no iterative refinement), end-to-end training approach with joint optimization on real and synthetic images on a shared common task. Through joint inference on real and synthetic data, the network extracts domain invariant features that are further used to estimate the 3D mesh parameters in a single shot with no supervision on real samples. While we compute regression loss on synthetic samples with ground truth mesh parameters, knowledge is transferred from synthetic to real data through ADA without direct ground truth for supervision. Finally, we propose a partially supervised method for satellite image super-resolution by learning a unified representation of samples from different domains (captured by different sensors) in a shared latent space. The training samples are drawn from two datasets which we refer to as source and target domains. The source domain consists of fewer samples which are of higher resolution and contain very detailed and accurate annotations. In contrast, samples from the target domain are low-resolution and available ground truth is sparse. The pipeline consists of a feature extractor and a super-resolving module which are trained end-to-end. Using a deep feature extractor, we jointly learn (on two datasets) a common embedding space for all samples. Partial supervision is available for the samples in the source domain which have high-resolution ground truth. Adversarial supervision is used to successfully super-resolve low-resolution RGB satellite imagery from target domain without direct paired supervision from high resolution counterparts

Comprehensive retinal image analysis: image processing and feature extraction techniques oriented to the clinical task

Medical digital imaging has become a key element of modern health care procedures. It provides a visual documentation, a permanent record for the patients, and most importantly the ability to extract information about many diseases. Ophthalmology is a field that is heavily dependent on the analysis of digital images because they can aid in establishing an early diagnosis even before the first symptoms appear. This dissertation contributes to the digital analysis of such images and the problems that arise along the imaging pipeline, a field that is commonly referred to as retinal image analysis. We have dealt with and proposed solutions to problems that arise in retinal image acquisition and longitudinal monitoring of retinal disease evolution. Specifically, non-uniform illumination, poor image quality, automated focusing, and multichannel analysis. However, there are many unavoidable situations in which images of poor quality, like blurred retinal images because of aberrations in the eye, are acquired. To address this problem we have proposed two approaches for blind deconvolution of blurred retinal images. In the first approach, we consider the blur to be space-invariant and later in the second approach we extend the work and propose a more general space-variant scheme. For the development of the algorithms we have built preprocessing solutions that have enabled the extraction of retinal features of medical relevancy, like the segmentation of the optic disc and the detection and visualization of longitudinal structural changes in the retina. Encouraging experimental results carried out on real retinal images coming from the clinical setting demonstrate the applicability of our proposed solutions

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Feature Extraction Methods for Character Recognition

Not Include

Aeronautical engineering: A continuing bibliography with indexes (supplement 295)

This bibliography lists 581 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System in Sep. 1993. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics