Positron Emission Tomography(PET) is a sophisticated imaging technique used in nuclear medicine. Dynamic PET images provide more quantitative information compared to single-frame static PET images, ...

Radar cross section (RCS) target recognition is an active part of radar target recognition (RTR). But the complex modulation and strong non-stationarity of radar echo signals pose extremely challenges ...

A holistic understanding of dynamic scenes is of fundamental importance in real-world computer vision problems such as autonomous driving, augmented reality and spatio-temporal reasoning. In this ...

The Swede wore the no.2 shirt at United, but United have confirmed that Diogo Dalot will wear that shirt number next season. Dalot returned to Carrington for pre-season and training on Thursday, after ...

Dynamic image analysis revolutionizes particle characterization, offering fast, reliable measurements of size and shape for improved manufacturing outcomes.

Katie Shanahan has covered England at a number of major football tournaments and sheds some light on what it's like to follow ...

The improvement in imaging resolution and the increase in imaging swath of remote sensing satellites have enabled the acquisition of vast and complex remote sensing image data. Effectively and ...

Recent approaches to reconstructing city-sized areas from large image collections usually process them all at once and only produce disconnected descriptions of image subsets, which typically ...

This paper introduces SwinDCA-Net, a novel architecture tailored for medical image segmentation, with a specific emphasis on vessel segmentation. The model incorporates Swin Transformer Modules into ...

This paper proposes a low noise 8T voltage domain pixel for global shutter CMOS image sensors (CIS). Based on the modeling and analysis of pixel noise, a buried channel transistor is employed as a ...

Recent end-to-end image compressive sensing networks primarily use Convolutional Neural Networks (CNNs) and Transformers, each with distinct limitations: CNNs struggle with global feature capture, ...

Compressive autoencoders (CAEs) play an important role in deep learning-based image compression, but large-scale CAEs are computationally expensive. We propose a framework with three techniques to ...