Accurate object tracking in low-light environments is crucial, particularly in surveillance, ethology applications, and biometric recognition systems. However, achieving this is significantly challenging due to the poor quality of captured sequences. Factors such as noise, color imbalance, and low contrast contribute to these challenges.
View Article and Find Full Text PDFMonitoring ground displacements identifies potential geohazard risks early before they cause critical damage. Interferometric synthetic aperture radar (InSAR) is one of the techniques that can monitor these displacements with sub-millimeter accuracy. However, using the InSAR technique is challenging due to the need for high expertise, large data volumes, and other complexities.
View Article and Find Full Text PDFIEEE Trans Ultrason Ferroelectr Freq Control
January 2023
In the field of biomedical imaging, ultrasonography has become common practice, and used as an important auxiliary diagnostic tool with unique advantages, such as being non-ionizing and often portable. This article reviews the state-of-the-art in medical ultrasound (US) image processing and in particular its applications in the examination of the lungs. First, we briefly introduce the basis of lung US (LUS) examination.
View Article and Find Full Text PDFUnlabelled: Radar (SAR) satellites systematically acquire imagery that can be used for volcano monitoring, characterising magmatic systems and potentially forecasting eruptions on a global scale. However, exploiting the large dataset is limited by the need for manual inspection, meaning timely dissemination of information is challenging. Here we automatically process ~ 600,000 images of > 1000 volcanoes acquired by the Sentinel-1 satellite in a 5-year period (2015-2020) and use the dataset to demonstrate the applicability and limitations of machine learning for detecting deformation signals.
View Article and Find Full Text PDFIEEE Trans Ultrason Ferroelectr Freq Control
November 2020
In this article, we present a novel method for line artifacts quantification in lung ultrasound (LUS) images of COVID-19 patients. We formulate this as a nonconvex regularization problem involving a sparsity-enforcing, Cauchy-based penalty function, and the inverse Radon transform. We employ a simple local maxima detection technique in the Radon transform domain, associated with known clinical definitions of line artifacts.
View Article and Find Full Text PDFThis paper presents an analysis of the low-level features and key spatial points used by humans during locomotion over diverse types of terrain. Although, a number of methods for creating saliency maps and task-dependent approaches have been proposed to estimate the areas of an image that attract human attention, none of these can straightforwardly be applied to sequences captured during locomotion, which contain dynamic content derived from a moving viewpoint. We used a novel learning-based method for creating a visual priority map informed by human eye tracking data.
View Article and Find Full Text PDFIEEE Trans Med Imaging
October 2017
This paper presents a novel method for line restoration in speckle images. We address this as a sparse estimation problem using both convex and non-convex optimization techniques based on the Radon transform and sparsity regularization. This breaks into subproblems, which are solved using the alternating direction method of multipliers, thereby achieving line detection and deconvolution simultaneously.
View Article and Find Full Text PDFIEEE Trans Cybern
October 2015
This paper presents a novel algorithm for terrain type classification based on monocular video captured from the viewpoint of human locomotion. A texture-based algorithm is developed to classify the path ahead into multiple groups that can be used to support terrain classification. Gait is taken into account in two ways.
View Article and Find Full Text PDFIEEE Trans Image Process
June 2013
Restoring a scene distorted by atmospheric turbulence is a challenging problem in video surveillance. The effect, caused by random, spatially varying, perturbations, makes a model-based solution difficult and in most cases, impractical. In this paper, we propose a novel method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which can severely degrade a region of interest (ROI).
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