Oil spills have always been a threat to the marine ecological environment; thus, it is important identify and divide oil spill areas on ocean surface into segments after an accident occurs protect environment. However, area segmentation using ordinary optical images greatly interfered with by absorption of light deep sea distribution algal organisms surface, difficult improve accuracy. To addre...

Longitudinal reproducibility is an essential concern in automated medical image segmentation, yet has proven to be an elusive objective as manual brain structure tracings have shown more than 10% variability. To improve reproducibility, longitudinal segmentation (4D) approaches have been investigated to reconcile temporal variations with traditional 3D approaches. In the past decade, multi-atla...

The increasing availability of huge image collections in different application fields, such as medical diagnosis, remote sensing, transmission and encoding, machine/robot vision, and video processing, microscopic imaging has pressed the need, in the last few last years, for the development of efficient techniques capable of managing and processing large collection of image data. In particular, ...

Abstract For semantic segmentation of cardiac magnetic resonance image (MRI) with low recognition and high background noise, a fusion‐attention Swin Transformer is proposed based on cognitive science deep learning methods. It has U‐shaped symmetric encoding–decoding structure an attention‐based skip connection. The encoder realizes self‐attention for feature representation the decoder up‐sample...

Image Fusion, a technique which combines complimentary information from different images of the same scene so that the fused image is more suitable for segmentation, feature extraction, object recognition and Human Visual System. In the proposed thesis work, a fast and efficient algorithm based on wavelet transform for multi-sensor image data has been proposed that could retrieve the prime feat...