Paper Info
Title
Boundary connectedness based video cut for moving object segmentation
Abstract
A new algorithm for automatic segmentation of moving objects in video based on spatio-temporal saliency and Neutro-Connectedness is presented in this paper. First, we propose a simple model to compute video saliency by combining initial saliency maps computed in spatial and temporal domains. Then, based on the detected spatiotemporal saliency map and temporal superpixels, initial background and foreground regions can be detected and taken as input of our proposed boundary connectedness based video cut (BC-video cut) to achieve moving object segmentation. Our model predicts jointly appearance models, Neutro-Connectedness, and pixel labels via an iterative energy minimization framework. Experiments show a good performance of our algorithm to segment moving objects on benchmark datasets.
Year
DOI
Venue
2017
10.1109/ATSIP.2017.8075596
2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)
Keywords
Field
DocType
spatiotemporal saliency,Neutro-Connectedness,boundary connectedness,moving object segmentation
Computer vision,Social connectedness,Scale-space segmentation,Pattern recognition,Salience (neuroscience),Segmentation,Visualization,Segmentation-based object categorization,Image segmentation,Pixel,Artificial intelligence,Mathematics
Conference
ISBN
Citations 
PageRank 
978-1-5386-0552-3
0
0.34
References 
Authors
27
2
Name
Order
Citations
PageRank
Hiba Ramadan132.42
Hamid Tairi25717.49