Add to ORKGThis paper presents a novel method of foreground segmentation that distinguishes moving objects from their moving cast shadows in monocular image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian belief network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. The notion of Markov random field is used to encourage the spatial connectivity of the segmented regions. The solution is obtained by maximizing the posterior possibility density of the segmentation field.
Moving objects detection and visual analysis is an active research topic due to its growing demand i...
Background subtraction is a fundamental low-level processing task in numerous computer vision applic...
Foreground detection has been used extensively in many applications such as people counting, traffic...
[[abstract]]This paper presents a probabilistic approach for automatically segmenting foreground obj...
In in this paper we propose a new model regarding foreground and shadow detection in video sequence...
This paper presents a Bayesian approach for foreground segmentation in monocular image sequences. To...
In this paper we present a segmentation system for monocular video sequences with static camera that...
Low-cost systems that can obtain a high-quality foreground segmentation almostindependently of the e...
A robust foreground object segmentation technique is proposed, capable of dealing with image sequenc...
Foreground segmentation is an essential task in many image processing applications and a commonly us...
Foreground/background segmentation is an active research area for moving object analysis. Many appli...
We propose a region-based foreground object segmentation method capable of dealing with image sequen...
We propose a Bayesian learning method to capture the background statistics of a dynamic scene. We mo...
In this paper we present a segmentation system for monoc-ular video sequences with static camera tha...
Foreground detection has been used extensively in many applications such as people counting, traffic...
Moving objects detection and visual analysis is an active research topic due to its growing demand i...
Background subtraction is a fundamental low-level processing task in numerous computer vision applic...
Foreground detection has been used extensively in many applications such as people counting, traffic...
[[abstract]]This paper presents a probabilistic approach for automatically segmenting foreground obj...
In in this paper we propose a new model regarding foreground and shadow detection in video sequence...
This paper presents a Bayesian approach for foreground segmentation in monocular image sequences. To...
In this paper we present a segmentation system for monocular video sequences with static camera that...
Low-cost systems that can obtain a high-quality foreground segmentation almostindependently of the e...
A robust foreground object segmentation technique is proposed, capable of dealing with image sequenc...
Foreground segmentation is an essential task in many image processing applications and a commonly us...
Foreground/background segmentation is an active research area for moving object analysis. Many appli...
We propose a region-based foreground object segmentation method capable of dealing with image sequen...
We propose a Bayesian learning method to capture the background statistics of a dynamic scene. We mo...
In this paper we present a segmentation system for monoc-ular video sequences with static camera tha...
Foreground detection has been used extensively in many applications such as people counting, traffic...
Moving objects detection and visual analysis is an active research topic due to its growing demand i...
Background subtraction is a fundamental low-level processing task in numerous computer vision applic...
Foreground detection has been used extensively in many applications such as people counting, traffic...