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Development of Video Frame Enhancement Technique Using Pixel Intensity AnalysisFeb 13 2019This paper developed a brightness enhancement technique for video frame pixel intensity improvement. Frames extracted from the six sample video data used in this work were stored in the form of images in a buffer. Noise was added to the extracted image ... More
Assessment of tear film using videokeratoscopy based on fractal dimensionFeb 13 2019Purpose: To develop and test a new method for characterizing tear film surface quality with high speed videokeratoscopy utilizing a fractal dimension approach. Methods: The regularity of the reflected pattern in high speed videokeratoscopy (E300, Medmont) ... More
SHEAR-net: An End-to-End Deep Learning Approach for Single Push Ultrasound Shear Wave Elasticity ImagingFeb 13 2019Ultrasound Shear Wave Elastography (USWE) with conventional B-mode imaging demonstrates better performance in lesion segmentation and classification problems. In this article, we propose SHEAR-net, an end-to-end deep neural network, to reconstruct USWE ... More
Experimental Investigation of Directive Antenna in Near Field Indirect Microwave HolographyFeb 13 2019An efficient implementation of low cost, specifically designed directive antennae as transmitter and receiver for the development of compact indirect microwave holographic setup is presented. Microwave holograms are recorded by 2D scanning over a plane ... More
Forensic Similarity for Digital ImagesFeb 13 2019In this paper we introduce a new digital image forensics approach called forensic similarity, which determines whether two image patches contain the same forensic trace or different forensic traces. One benefit of this approach is that prior knowledge, ... More
Improvements of computational ghost imaging by using Special-Hadamard patternsFeb 12 2019We introduced a new kind of patterns named Special-Hadamard patterns, which could be used as structured illuminations of computational ghost imaging. Special-Hadamard patterns can get a better image quality than Hadamard patterns in a noisy environment. ... More
Learning to Authenticate with Deep Multibiometric Hashing and Neural Network DecodingFeb 11 2019In this paper, we propose a novel three-stage multimodal deep hashing neural decoder (MDHND) architecture, which integrates a deep hashing framework with a neural network decoder (NND) to create an effective multibiometric authentication system. The MDHND ... More
A complete data processing workflow for CryoET and subtomogram averagingFeb 11 2019Electron cryotomography (CryoET) is currently the only method capable of visualizing cells in 3D at nanometer resolutions. While modern instruments produce massive amounts of tomography data containing extremely rich structural information, the data processing ... More
Colorectal Cancer Outcome Prediction from H&E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype ProfilesFeb 10 2019Digital pathology (DP) is a new research area which falls under the broad umbrella of health informatics. Owing to its potential for major public health impact, in recent years DP has been attracting much research attention. Nevertheless, a wide breadth ... More
An Algorithm Unrolling Approach to Deep Blind Image DeblurringFeb 09 2019Blind image deblurring remains a topic of enduring interest. Learning based approaches, especially those that employ neural networks have emerged to complement traditional model based methods and in many cases achieve vastly enhanced performance. That ... More
Lumen boundary detection using neutrosophic c-means in IVOCT imagesFeb 09 2019In this paper, a novel method for lumen boundary identification is proposed using Neutrosophic c_means. This method clusters pixels of the intravascular optical coherence tomography image into several clusters using indeterminacy and Neutrosophic theory, ... More
Super-realtime facial landmark detection and shape fitting by deep regression of shape model parametersFeb 09 2019We present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms. Motivated by established model-based fitting methods such as active shapes, we use a PCA ... More
Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural ImagesFeb 08 2019The human ability to recognize objects is impaired when the object is not shown in full. "Minimal images" are the smallest regions of an image that remain recognizable for humans. Ullman et al. 2016 show that a slight modification of the location and ... More
Using Automatic Differentiation as a General Framework for Ptychographic ReconstructionFeb 08 2019Coherent diffraction imaging methods enable imaging beyond lens-imposed resolution limits. In these methods, the object can be recovered by minimizing an error metric that quantifies the difference between diffraction patterns as observed, and those calculated ... More
HYDRA: Hybrid Deep Magnetic Resonance FingerprintingFeb 07 2019Magnetic resonance fingerprinting (MRF) methods typically rely on dictionary matching to map the temporal MRF signals to quantitative tissue parameters. Such approaches suffer from inherent discretization errors, as well as high computational complexity ... More
Matrix Cofactorization for Joint Representation Learning and Supervised Classification -- Application to Hyperspectral Image AnalysisFeb 07 2019Supervised classification and representation learning are two widely used methods to analyze multivariate images. Although complementary, these two classes of methods have been scarcely considered jointly. In this paper, a method coupling these two approaches ... More
Iris Image Processing in Compressive Sensing ScenarioFeb 07 2019This paper observes the application of the Compressive Sensing in reconstruction of the under-sampled iris images. Iris recognition represents form of biometric identification whose usage in real applications is growing. Compressive Sensing represents ... More
DoPAMINE: Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise DespecklingFeb 07 2019We propose DoPAMINE, a new neural network based multiplicative noise despeckling algorithm. Our algorithm is inspired by Neural AIDE (N-AIDE), which is a recently proposed neural adaptive image denoiser. While the original N-AIDE was designed for the ... More
Dual-Reference Design for Holographic Coherent Diffraction ImagingFeb 07 2019A new reference design is introduced for Holographic Coherent Diffraction Imaging. This consists of two reference portions - being "block" and "pinhole" shaped regions - adjacent to the imaging specimen. Expected error analysis on data following a Poisson ... More
SAPSAM - Sparsely Annotated Pathological Sign Activation Maps - A novel approach to train Convolutional Neural Networks on lung CT scans using binary labels onlyFeb 06 2019Chronic Pulmonary Aspergillosis (CPA) is a complex lung disease caused by infection with Aspergillus. Computed tomography (CT) images are frequently requested in patients with suspected and established disease, but the radiological signs on CT are difficult ... More
Content-based image retrieval system with most relevant features among wavelet and color featuresFeb 06 2019Content-based image retrieval (CBIR) has become one of the most important research directions in the domain of digital data management. In this paper, a new feature extraction schema including the norm of low frequency components in wavelet transformation ... More
Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural NetworksFeb 05 2019High-fidelity semantic segmentation of magnetic resonance volumes is critical for estimating tissue morphometry and relaxation parameters in both clinical and research applications. While manual segmentation is accepted as the gold-standard, recent advances ... More
Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup SegmentationFeb 04 2019Glaucoma is a serious ocular disorder for which the screening and diagnosis are carried out by the examination of the optic nerve head (ONH). The color fundus image (CFI) is the most common modality used for ocular screening. In CFI, the central r
Segmentation of Cortical Spreading Depression Wavefronts Through Local Similarity MetricFeb 01 2019In this paper, we present a novel region-based segmentation method for cortical spreading depressions in 2-photon microscopy images. Fluorescent microscopy has become an important tool in neuroscience, but segmentation approaches are challenged by the ... More
Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRIFeb 01 2019Feb 04 2019Slow acquisition has been one of the historical problems in dynamic magnetic resonance imaging (dMRI), but the rise of compressed sensing (CS) has brought numerous algorithms that successfully achieve high acceleration rates. While CS proposes random ... More
Projection-Based 2.5D U-net Architecture for Fast Volumetric SegmentationFeb 01 2019Convolutional neural networks are state-of-the-art for various segmentation tasks. While for 2D images these networks are also computationally efficient, 3D convolutions have huge storage requirements and require long training time. To overcome this issue, ... More
Image reconstruction enhancement via masked regularizationJan 31 2019Image reconstruction based on an edge-sparsity assumption has become popular in recent years. Many methods of this type are capable of reconstructing nearly perfect edge-sparse images using limited data. In this paper, we present a method to improve the ... More
Trading beams for bandwidth: Imaging with randomized beamformingJan 31 2019We study the problem of actively imaging a range-limited far-field scene using an antenna array. We describe how the range limit imposes structure in the measurements across multiple wavelengths. This structure allows us to introduce a novel trade-off: ... More
Tutorials on X-ray Phase Contrast Imaging: Some Fundamentals and Some Conjectures on Future DevelopmentsJan 31 2019These tutorials introduce some basics of imaging with coherent X-rays, focusing on phase contrast. We consider the transport-of-intensity equation, as one particular method for X-ray phase contrast imaging among many, before passing on to the inverse ... More
A Convolutional Neural Network for the Automatic Diagnosis of Collagen VI related Muscular DystrophiesJan 30 2019The development of machine learning systems for the diagnosis of rare diseases is challenging mainly due the lack of data to study them. Despite this challenge, this paper proposes a system for the Computer Aided Diagnosis (CAD) of low-prevalence, congenital ... More
CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule NetworksJan 28 2019Capsule Networks envision an innovative point of view about the representation of the objects in the brain and preserve the hierarchical spatial relationships between them. This type of networks exhibits a huge potential for several Machine Learning tasks ... More
A deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imagingJan 27 2019We propose a novel automatic method for accurate segmentation of the prostate in T2-weighted magnetic resonance imaging (MRI). Our method is based on convolutional neural networks (CNNs). Because of the large variability in the shape, size, and appearance ... More
Development of Systematic Image Preprocessing of LAPAN-A3/IPB Multispectral ImagesJan 26 2019As of any other satellite images, LAPAN-A3/IPB multispectral images suffered from both geometric and radiometric distortions which need to be corrected. LAPAN as satellite owner has developed image preprocessing algorithm to process raw image into systematically ... More
Fully Automated Volumetric Classification in CT Scans for Diagnosis and Analysis of Normal Pressure HydrocephalusJan 25 2019Normal Pressure Hydrocephalus (NPH) is one of the few reversible forms of dementia. Due to their low cost and versatility, Computed Tomography (CT) scans have long been used as an aid to help diagnose intracerebral anomalies such as NPH. However, because ... More
CT synthesis from MR images for orthopedic applications in the lower arm using a conditional generative adversarial networkJan 24 2019Purpose: To assess the feasibility of deep learning-based high resolution synthetic CT generation from MRI scans of the lower arm for orthopedic applications. Methods: A conditional Generative Adversarial Network was trained to synthesize CT images from ... More
Ptychography for pulse-to-pulse wavefront sensing at free-electron lasersJan 24 2019The growing interest in the wavefront of ultra-bright and ultra-short pulses produced by free-electron lasers (FELs) brought to the development of several complementary approaches to characterize them. Ptychography has been proposed as a suitable method ... More
Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imagingJan 23 2019Magnetic resonance imaging (MRI) is a remarkably powerful diagnostic technique: it generates wide-ranging information for the non-invasive study of tissue anatomy and physiology. Complementary data is normally obtained in separate measurements, either ... More
PadChest: A large chest x-ray image dataset with multi-label annotated reportsJan 22 2019Feb 07 2019We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted ... More
A high-speed, wavelength invariant, single-pixel wavefront sensor with a digital micromirror deviceJan 22 2019The wavefront measurement of a light beam is a complex task, which often requires a series of spatially resolved intensity measurements. For instance, a detector array may be used to measure the local phase gradient in the transverse plane of the unknown ... More
Prior Information Guided Regularized Deep Learning for Cell Nucleus DetectionJan 21 2019Cell nuclei detection is a challenging research topic because of limitations in cellular image quality and diversity of nuclear morphology, i.e. varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduring ... More
MIMIC-CXR: A large publicly available database of labeled chest radiographsJan 21 2019Jan 23 2019Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient's thorax, but requiring specialized training for proper interpretation. With the advent of high performance general purpose computer vision algorithms, ... More
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert ComparisonJan 21 2019Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to ... More
Edge-masked CT image reconstruction from limited dataJan 16 2019This paper presents an iterative inversion algorithm for computed tomography image reconstruction that performs well in terms of accuracy and speed using limited data. The computational method combines an image domain technique and statistical reconstruction ... More
Actions Speak Louder Than (Pass)words: Passive Authentication of Smartphone Users via Deep Temporal FeaturesJan 16 2019Prevailing user authentication schemes on smartphones rely on explicit user interaction, where a user types in a passcode or presents a biometric cue such as face, fingerprint, or iris. In addition to being cumbersome and obtrusive to the users, such ... More
Detection of delayed target response in SARJan 15 2019Delayed target response in synthetic aperture radar (SAR) imaging can be obscured by the range-delay ambiguity and speckle. To analyze the range-delay ambiguity, one extends the standard SAR formulation and allows both the target reflectivity and the ... More
Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual PresentationJan 15 2019Rapid Serial Visual Presentation (RSVP) is a paradigm that supports the application of cortically coupled computer vision to rapid image search. In RSVP, images are presented to participants in a rapid serial sequence which can evoke Event-related Potentials ... More
Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and RobustnessJan 14 2019In this paper we propose a novel augmentation technique that improves not only the performance of deep neural networks on clean test data, but also significantly increases their robustness to random transformations, both affine and projective. Inspired ... More
Light-Field for RFJan 13 2019Most computer vision systems and computational photography systems are visible light based which is a small fraction of the electromagnetic (EM) spectrum. In recent years radio frequency (RF) hardware has become more widely available, for example, many ... More
ChronoMID - Cross-Modal Neural Networks for 3-D Temporal Medical Imaging DataJan 12 2019ChronoMID builds on the success of cross-modal convolutional neural networks (X-CNNs), making the novel application of the technique to medical imaging data. Specifically, this paper presents and compares alternative approaches - timestamps and difference ... More
Dense Super-Resolution Imaging of Molecular Orientation via Joint Sparse Basis Deconvolution and Spatial PoolingJan 12 2019In single-molecule super-resolution microscopy, engineered point-spread functions (PSFs) are designed to efficiently encode new molecular properties, such as 3D orientation, into complex spatial features captured by a camera. To fully benefit from their ... More
Multi-Level Batch Normalization In Deep Networks For Invasive Ductal Carcinoma Cell Discrimination In Histopathology ImagesJan 11 2019Breast cancer is the most diagnosed cancer and the most predominant cause of death in women worldwide. Imaging techniques such as the breast cancer pathology helps in the diagnosis and monitoring of the disease. However identification of malignant cells ... More
Learning image from projection: a full-automatic reconstruction (FAR) net for sparse-views computed tomographyJan 11 2019The sparse-views x-ray computed tomography (CT) is essential for medical diagnosis and industrial nondestructive testing. However, in particular, the reconstructed image usually suffers from complex artifacts and noise, when the sampling is insufficient. ... More
High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population CohortJan 10 2019The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imaging data automatically, a pipeline should ... More
Tensor-based subspace learning for tracking salt-dome boundariesJan 09 2019The exploration of petroleum reservoirs has a close relationship with the identification of salt domes. To efficiently interpret salt-dome structures, in this paper, we propose a method that tracks salt-dome boundaries through seismic volumes using a ... More
Selective metamorphosis for growth modelling with applications to landmarksJan 08 2019We present a framework for shape matching in computational anatomy allowing users control of the degree to which the matching is diffeomorphic. This control is given as a function defined over the image and parameterises the template deformation. By modelling ... More
Long Short-Term Memory Spatial Transformer NetworkJan 08 2019Spatial transformer network has been used in a layered form in conjunction with a convolutional network to enable the model to transform data spatially. In this paper, we propose a combined spatial transformer network (STN) and a Long Short-Term Memory ... More
Learning-based Optimization of the Under-sampling Pattern in MRIJan 07 2019Acquisition of Magnetic Resonance Imaging (MRI) scans can be accelerated by under-sampling in k-space (i.e., the Fourier domain). In this paper, we consider the problem of optimizing the sub-sampling pattern in a data-driven fashion. Since the reconstruction ... More
Joint non-rigid image registration and reconstruction for quantitative atomic resolution scanning transmission electron microscopyJan 07 2019Jan 14 2019Aberration corrected scanning transmission electron microscopes (STEM) enable to determine local strain fields, composition and bonding states at atomic resolution. The precision to locate atomic columns is often obstructed by scan artifacts limiting ... More
QFlow: A Reinforcement Learning Approach to High QoE Video Streaming over Wireless NetworksJan 04 2019Jan 24 2019Wireless Internet access has brought legions of heterogeneous applications all sharing the same resources. However, current wireless edge networks that cater to worst or average case performance lack the agility to best serve these diverse sessions. Simultaneously, ... More
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimizationJan 02 2019This paper investigates lung nodule classification by using deep neural networks (DNNs). Hyperparameter optimization in DNNs is a computationally expensive problem, where evaluating a hyperparameter configuration may take several hours or even days. Bayesian ... More
Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimers diseaseDec 28 2018Diffusion MRI is the modality of choice to study alterations of white matter. In the past years, various works have used diffusion MRI for automatic classification of Alzheimers disease. However, the performances obtained with different approaches are ... More
Noise-robust detection and tracking of salt domes in postmigrated volumes using texture, tensors, and subspace learningDec 28 2018The identification of salt dome boundaries in migrated seismic data volumes is important for locating petroleum reservoirs. The presence of noise in the data makes computer-aided salt dome interpretation even more challenging. In this paper, we develop ... More
2.5D Deep Learning for CT Image Reconstruction using a Multi-GPU implementationDec 20 2018While Model Based Iterative Reconstruction (MBIR) of CT scans has been shown to have better image quality than Filtered Back Projection (FBP), its use has been limited by its high computational cost. More recently, deep convolutional neural networks (CNN) ... More
Model Based Iterative Reconstruction With Spatially Adaptive Sinogram Weights for Wide-Cone Cardiac CTDec 20 2018With the recent introduction of CT scanners with large cone angles, wide coverage detectors now provide a desirable scanning platform for cardiac CT that allows whole heart imaging in a single rotation. On these scanners, while half-scan data is strictly ... More
Lattice Identification and Separation: Theory and AlgorithmDec 19 2018Motivated by lattice mixture identification and grain boundary detection, we present a framework for lattice pattern representation and comparison, and propose an efficient algorithm for lattice separation. We define new scale and shape descriptors, which ... More
Iterative annotation to ease neural network training: Specialized machine learning in medical image analysisDec 18 2018Neural networks promise to bring robust, quantitative analysis to medical fields, but adoption is limited by the technicalities of training these networks. To address this translation gap between medical researchers and neural networks in the field of ... More
Retinal vessel segmentation based on Fully Convolutional Neural NetworksDec 18 2018Dec 19 2018The retinal vascular condition is a reliable biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation may be crucial to diagnose and monitor them. In this paper, we propose a novel method that combines the multiscale ... More
TOP-GAN: Label-Free Cancer Cell Classification Using Deep Learning with a Small Training SetDec 17 2018We propose a new deep learning approach for medical imaging that copes with the problem of a small training set, the main bottleneck of deep learning, and apply it for classification of healthy and cancer cells acquired by quantitative phase imaging. ... More
Fast and Accurate Depth Estimation from Sparse Light FieldsDec 17 2018We present a fast and accurate method for dense depth reconstruction from sparsely sampled light fields obtained using a synchronized camera array. In our method, the source images are over-segmented into non-overlapping compact superpixels that are used ... More
High-Resolution Limited-Angle Phase Tomography of Dense Layered Objects Using Deep Neural NetworksDec 15 2018Dec 19 2018We present a Machine Learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to $\pm $10$^\circ$. Whereas previous approaches to phase tomography generally require two steps, first to retrieve ... More
Single molecule localization by $\ell_2-\ell_0$ constrained optimizationDec 14 2018Single Molecule Localization Microscopy (SMLM) enables the acquisition of high-resolution images by alternating between activation of a sparse subset of fluorescent molecules present in a sample and localization. In this work, the localization problem ... More
Long-range depth imaging using a single-photon detector array and non-local data fusionDec 11 2018The ability to measure and record high-resolution depth images at long stand-off distances is important for a wide range of applications, including connected and automotive vehicles, defense and security, and agriculture and mining. In LIDAR (light detection ... More
A practical light transport system model for chemiluminescence distribution reconstructionDec 08 2018Plenoptic cameras and other integral photography instruments capture richer angular information from a scene than traditional 2D cameras. This extra information is used to estimate depth, perform superresolution or reconstruct 3D information from the ... More
SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstructionDec 08 2018Deep learning holds great promise in the reconstruction of undersampled Magnetic Resonance Imaging (MRI) data, providing new opportunities to escalate the performance of rapid MRI. In existing deep learning-based reconstruction methods, supervised training ... More
Topology, homogeneity and scale factors for object detection: application of eCognition software for urban mapping using multispectral satellite imageDec 06 2018The research scope of this paper is to apply spatial object based image analysis (OBIA) method for processing panchromatic multispectral image covering study area of Brussels for urban mapping. The aim is to map different land cover types and more specifically, ... More
Automatic Segmentation of Choroid Layer in EDI OCT Images Using Graph Theory in Neutrosophic SpaceDec 05 2018The choroid is vascular tissue located underneath the retina and supplies oxygen to the outer retina; any damage to this tissue can be a precursor to retinal disease. Choroid is almost invisible in Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT) ... More
Multichannel reconstruction from nonuniform samples with application to image recoveryDec 05 2018The multichannel trigonometric reconstruction from uniform samples was proposed recently. It not only makes use of multichannel information about the signal but is also capable to generate various kinds of interpolation formulas according to the types ... More
An Analysis by Synthesis Approach for Automatic Vertebral Shape Identification in Clinical QCTDec 03 2018Quantitative computed tomography (QCT) is a widely used tool for osteoporosis diagnosis and monitoring. The assessment of cortical markers like cortical bone mineral density (BMD) and thickness is a demanding task, mainly because of the limited spatial ... More
Adaptive Anisotropic Total Variation - A Nonlinear Spectral AnalysisNov 27 2018A fundamental concept in solving inverse problems is the use of regularizers, which yield more physical and less-oscillatory solutions. Total variation (TV) has been widely used as an edge-preserving regularizer. However, objects are often over-regularized ... More
Leveraging Filter Correlations for Deep Model CompressionNov 26 2018We present a filter correlation based model compression approach for deep convolutional neural networks. Our approach iteratively identifies pairs of filters with largest pairwise correlations and discards one of the filters from each such pair. However, ... More
Validation of Biometric Identification of Dairy Cows based on Udder NIR ImagesNov 25 2018Identifying dairy cows with infections such as mastitis or cows on medications is an extremely important task and legally required by the FDA$'$s Pasteurized Milk Ordinance. The milk produced by these dairy cows cannot be allowed to mix with the milk ... More
Hyperconnected Relator Spaces. CW Complexes and Continuous Function Paths that are HyperconnectedNov 24 2018This article introduces proximal cell complexes in a hyperconnected space. Hyperconnectedness encodes how collections of path-connected sub-complexes in a Alexandroff-Hopf-Whitehead CW space are near to or far from each other. Several main results are ... More
Fault Detection Using Color Blending and Color TransformationsNov 22 2018In the field of seismic interpretation, univariate databased maps are commonly used by interpreters, especially for fault detection. In these maps, contrast between target regions and the background is one of the main factors that affect the accuracy ... More
Feature-based groupwise registration of historical aerial images to present-day ortho-photo mapsNov 22 2018In this paper, we address the registration of historical WWII images to present-day ortho-photo maps for the purpose of geolocalization. Due to the challenging nature of this problem, we propose to register the images jointly as a group rather than in ... More
Edge-adaptive l2 regularization image reconstruction from non-uniform Fourier dataNov 20 2018Total variation regularization based on the l1 norm is ubiquitous in image reconstruction. However, the resulting reconstructions are not always as sparse in the edge domain as desired. Iteratively reweighted methods provide some improvement in accuracy, ... More
Effects of Lombard Reflex on the Performance of Deep-Learning-Based Audio-Visual Speech Enhancement SystemsNov 15 2018Humans tend to change their way of speaking when they are immersed in a noisy environment, a reflex known as Lombard effect. Current speech enhancement systems based on deep learning do not usually take into account this change in the speaking style, ... More
On Training Targets and Objective Functions for Deep-Learning-Based Audio-Visual Speech EnhancementNov 15 2018Audio-visual speech enhancement (AV-SE) is the task of improving speech quality and intelligibility in a noisy environment using audio and visual information from a talker. Recently, deep learning techniques have been adopted to solve the AV-SE task in ... More
Advanced Denoising for X-ray PtychographyNov 05 2018The success of ptychographic imaging experiments strongly depends on achieving high signal-to-noise ratio. This is particularly important in nanoscale imaging experiments when diffraction signals are very weak and the experiments are accompanied by significant ... More
Deep BV: A Fully Automated System for Brain Ventricle Localization and Segmentation in 3D Ultrasound Images of Embryonic MiceNov 05 2018Volumetric analysis of brain ventricle (BV) structure is a key tool in the study of central nervous system development in embryonic mice. High-frequency ultrasound (HFU) is the only non-invasive, real-time modality available for rapid volumetric imaging ... More
Spectral Method for Multiplexed Phase Retrieval and Application in Optical Imaging in Complex MediaOct 30 2018We introduce a generalized version of phase retrieval called multiplexed phase retrieval. We want to recover the phase of amplitude-only measurements from linear combinations of them. This corresponds to the case in which multiple incoherent sources are ... More
Principled Design and Implementation of Steerable DetectorsOct 23 2018We provide a complete pipeline for the detection of patterns of interest in an image. In our approach, the patterns are assumed to be adequately modeled by a known template, and are located at unknown position and orientation. We propose a continuous-domain ... More
A Hardware Realization of Superresolution Combining Random Coding and BlurringOct 20 2018Resolution enhancements are often desired in imaging applications where high-resolution sensor arrays are difficult to obtain. Many computational imaging methods have been proposed to encode high-resolution scene information on low-resolution sensors ... More
Characterising epithelial tissues using persistent entropyOct 13 2018In this paper, we apply persistent entropy, a novel topological statistic, for characterization of images of epithelial tissues. We have found out that persistent entropy is able to summarize topological and geometric information encoded by \alpha-complexes ... More
Heterogeneous multireference alignment for images with application to 2-D classification in single particle reconstructionOct 12 2018Motivated by the task of $2$-D classification in single particle reconstruction by cryo-electron microscopy (cryo-EM), we consider the problem of heterogeneous multireference alignment of images. In this problem, the goal is to estimate a (typically small) ... More
Robot Vision: Calibration of Wide-Angle Lens Cameras Using Collinearity Condition and K-Nearest Neighbour RegressionSep 29 2018Visual perception is regularly used by humans and robots for navigation. By either implicitly or explicitly mapping the environment, ego-motion can be determined and a path of actions can be planned. The process of mapping and navigation are delicately ... More
Context-adaptive Entropy Model for End-to-end Optimized Image CompressionSep 27 2018Nov 19 2018We propose a context-adaptive entropy model for use in end-to-end optimized image compression. Our model exploits two types of contexts, bit-consuming contexts and bit-free contexts, distinguished based upon whether additional bit allocation is required. ... More
Attention-based Audio-Visual Fusion for Robust Automatic Speech RecognitionSep 05 2018Nov 19 2018Automatic speech recognition can potentially benefit from the lip motion patterns, complementing acoustic speech to improve the overall recognition performance, particularly in noise. In this paper we propose an audio-visual fusion strategy that goes ... More
Tensor Alignment Based Domain Adaptation for Hyperspectral Image ClassificationAug 29 2018Sep 04 2018This paper presents a tensor alignment (TA) based domain adaptation method for hyperspectral image (HSI) classification. To be specific, HSIs in both domains are first segmented into superpixels and tensors of both domains are constructed to include neighboring ... More
Cross-Domain Collaborative Learning via Cluster Canonical Correlation Analysis and Random Walker for Hyperspectral Image ClassificationAug 29 2018Oct 30 2018This paper introduces a novel heterogenous domain adaptation (HDA) method for hyperspectral image classification with a limited amount of labeled samples in both domains. The method is achieved in the way of cross-domain collaborative learning (CDCL), ... More
Geometry of the Phase Retrieval ProblemAug 23 2018One of the most powerful approaches to imaging at the nanometer or subnanometer length scale is coherent diffraction imaging using X-ray sources. For amorphous (non-crystalline) samples, the raw data can be interpreted as the modulus of the continuous ... More