Imaging through turbid media is a challenging topic. A liquid is considered turbid when dispersed particles provoke strong light scattering, thus destroying the image formation by any standard optical system. Generally, colloidal solutions belong to the class of turbid media since dispersed particles have dimensions ranging between 0.2 μm and 2 μm. However, in microfluidics, another relevant issue has to be considered in the case of flowing liquid made of a multitude of occluding objects, e.g. red blood cells (RBCs) flowing in veins. In such a case instead of severe scattering processes unpredictable phase delays occur resulting in a wavefront distortion, thus disturbing or even hindering the image formation of objects behind such obstructing layer. In fact RBCs can be considered to be thin transparent phase objects. Here we show that sharp amplitude imaging and phase-contrast mapping of cells hidden behind biological occluding objects, namely RBCs, is possible in harsh noise conditions and with a large field-of view by Multi-Look Digital Holography microscopy (ML-DH). Noteworthy, we demonstrate that ML-DH benefits from the presence of the RBCs, providing enhancement in terms of numerical resolution and noise suppression thus obtaining images whose quality is higher than the quality achievable in the case of a liquid without occlusive objects.
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Healthc Technol Lett
December 2024
Robotics and Control Laboratory, Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada.
The Segment Anything model (SAM) is a powerful vision foundation model that is revolutionizing the traditional paradigm of segmentation. Despite this, a reliance on prompting each frame and large computational cost limit its usage in robotically assisted surgery. Applications, such as augmented reality guidance, require little user intervention along with efficient inference to be usable clinically.
View Article and Find Full Text PDFCurr Res Neurobiol
June 2025
Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, 62 Hillhead Street, Glasgow, G12 8QB, United Kingdom.
Identifying the objects embedded in natural scenes relies on recurrent processing between lower and higher visual areas. How is cortical feedback information related to objects and scenes organised in lower visual areas? The spatial organisation of cortical feedback converging in early visual cortex during object and scene processing could be retinotopically specific as it is coded in V1, or object centred as coded in higher areas, or both. Here, we characterise object and scene-related feedback information to V1.
View Article and Find Full Text PDFPLoS One
January 2025
North China Institute of Aerospace Engineering, Langfang, China.
As the global economy expands, waterway transportation has become increasingly crucial to the logistics sector. This growth presents both significant challenges and opportunities for enhancing the accuracy of ship detection and tracking through the application of artificial intelligence. This article introduces a multi-object tracking system designed for unmanned aerial vehicles (UAVs), utilizing the YOLOv7 and Deep SORT algorithms for detection and tracking, respectively.
View Article and Find Full Text PDFSci Rep
January 2025
School of Mathematics and Computer, Wuhan Polytechnic University, Wuhan, 430048, China.
The rapid changes in the global environment have led to an unprecedented decline in biodiversity, with over 28% of species facing extinction. This includes snakes, which are key to ecological balance. Detecting snakes is challenging due to their camouflage and elusive nature, causing data loss and feature extraction difficulties in ecological monitoring.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Automation Department, North China Electric Power University, Baoding 071003, China.
Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is proposed. Firstly, the particularity of the scene in the multi-fitting detection task is analyzed. Hence, the aggregation of the fittings is defined as the scene according to the professional knowledge of the power field and the habit of the operators in identifying the fittings.
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