Abstract Moving Object Segmentation (MOS) is an important topic in computer vision. MOS
becomes a challenging problem in the presence of dynamic background and moving …

Background subtraction (BGS) is a fundamental video processing task which is a key
component of many applications. Deep learning-based supervised algorithms achieve very …

Moving object segmentation (MOS) is the process of identifying dynamic objects from video
frames, such as moving vehicles or pedestrians, while discarding the background. It plays …

Moving object detection has been a central topic of discussion in computer vision for its wide
range of applications like in self-driving cars, video surveillance, security, and enforcement …

Graph Signal Processing (GSP) is an emerging research field that extends the concepts of
digital signal processing to graphs. GSP has numerous applications in different areas such …

Moving object detection is the foundation of research in many computer vision fields. In
recent decades, a number of detection methods have been proposed. Relevant surveys …

Abstract Moving Object Detection (MOD) is a fundamental step for many computer vision
applications. MOD becomes very challenging when a video sequence captured from a static …

Recently, several successful methods based on deep neural networks have been proposed
for background subtraction. These deep neural algorithms have almost perfect performance …

Automatic ship segmentation from high-resolution synthetic aperture radar (SAR) remote-
sensing images has been a topic of interest that has gradually gained attention over the …

Graph total variation (GTV) is a widely employed regularization in graph-based semi-
supervised learning (GSSL), which enforce the piece-wise smoothness of the label values …