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Foreground Detection in Camouflaged Scenes [PDF]
2017 IEEE International Conference on Image Processing (ICIP), 2017 Foreground detection has been widely studied for decades due to its importance in many practical applications. Most of the existing methods assume foreground and background show visually distinct characteristics and thus the foreground can be detected ...
Cook, Chris +4 more
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Foreground Detection Based on Superpixel and Semantic Segmentation. [PDF]
Comput Intell Neurosci, 2022 Foreground detection is an essential step in computer vision and video processing. Accurate foreground object extraction is crucial for subsequent high-level tasks such as target recognition and tracking. Although many foreground detection algorithms have been proposed, foreground detection in complex scenes is still a challenging problem.
Feng J, Liu P, Kim YK.
europepmc +3 more sources
Foreground detection enhancement using Pearson correlation filtering [PDF]
, 2018 Foreground detection algorithms are commonly employed as an initial module in video processing pipelines for automated surveillance. The resulting masks produced by these algorithms are usually postprocessed in order to improve their quality.
Domínguez-Merino, Enrique
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Foreground Detection Using the Choquet Integral [PDF]
2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services, 2008 Foreground Detection is a key step in background subtraction problem. This approach consists in the detection of moving objects from static cameras through a classification process of pixels as foreground or background. The presence of some critical situations i.e noise, illumination changes and structural background changes produces an uncertainty in ...
El Baf, Fida +2 more
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Fuzzy foreground detection for infrared videos [PDF]
2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008 We present a foreground detection algorithm based on a fuzzy integral that is particularly suitable for infrared videos. The proposed detection of moving objects is based on fusing intensity and textures using fuzzy integral. The detection results are then used to update the background in a fuzzy way. This method allows to robustly detect moving object
El Baf, Fida +2 more
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Foreground Feature Enhancement for Object Detection [PDF]
IEEE Access, 2019 Deep convolutional neural networks have shown great success in object detection. Most object detection methods focus on improving network architecture and introducing additional objective functions to improve the discrimination of object detectors, while the informative annotations of the training data obtained from enormous human effort are mainly ...
Shenwang Jiang +4 more
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Bayesian background modeling for foreground detection [PDF]
Proceedings of the third ACM international workshop on Video surveillance & sensor networks, 2005 We propose a Bayesian learning method to capture the background statistics of a dynamic scene. We model each pixel as a set of layered normal distributions that compete with each other. Using a recursive Bayesian learning mechanism, we estimate not only the mean and variance but also the probability distribution of the mean and covariance of each model.
Fatih Porikli, Oncel Tuzel
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Impact of Galactic polarized emission on B-mode detection at low multipoles [PDF]
, 2009 We use a model of polarized Galactic emission developed by the the Planck collaboration to assess the impact of foregrounds on B-mode detection at low multipoles.
Amblard +50 more
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Pixel Features for Self-organizing Map Based Detection of Foreground Objects in Dynamic Environments [PDF]
, 2016 Among current foreground detection algorithms for video sequences, methods based on self-organizing maps are obtaining a greater relevance. In this work we propose a probabilistic self-organising map based model, which uses a uniform distribution to ...
Domínguez, Enrique +4 more
core +1 more source
Foreground Detection with a Moving RGBD Camera [PDF]
, 2013 A method for foreground detection in data acquired by a moving RGBD camera is proposed. The background scene is initially in a reference model. An initial estimation of camera motion is provided by a conventional point cloud registration approach of matched keypoints between the captured scene and the reference model.
Koutlemanis P. +3 more
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