Results 1 to 10 of about 17,012 (262)
Illumination and Reflectance Estimation with its Application in Foreground Detection [PDF]
Sensors, 2015 In this paper, we introduce a novel approach to estimate the illumination and reflectance of an image. The approach is based on illumination-reflectance model and wavelet theory.
Gang Jun Tu +3 more
doaj +4 more sources
Foreground Detection with Deeply Learned Multi-Scale Spatial-Temporal Features [PDF]
Sensors, 2018 Foreground detection, which extracts moving objects from videos, is an important and fundamental problem of video analysis. Classic methods often build background models based on some hand-craft features.
Yao Wang, Zujun Yu, Liqiang Zhu
doaj +2 more sources
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
Shenwang Jiang +4 more
doaj +2 more sources
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
Real-Time Object Tracking with Template Tracking and Foreground Detection Network [PDF]
Sensors, 2019 In this paper, we propose a fast and accurate deep network-based object tracking method, which combines feature representation, template tracking and foreground detection into a single framework for robust tracking.
Kaiheng Dai, Yuehuan Wang, Qiong Song
doaj +2 more sources
Foreground separation knowledge distillation for object detection [PDF]
PeerJ Computer ScienceIn recent years, deep learning models have become predominant methods for computer vision tasks, but the large computation and storage requirements of many models make them challenging to deploy on devices with limited resources.
Chao Li +4 more
doaj +4 more sources
Oriented Object Detection Based on Foreground Feature Enhancement in Remote Sensing Images
Remote Sensing, 2022 Oriented object detection is a fundamental and challenging task in remote sensing image analysis and has received much attention in recent years. Optical remote sensing images often have more complex background information than natural images, and the ...
Peng Lin, Xiaofeng Wu, Bin Wang
doaj +1 more source
Frontiers in Plant Science, 2022 Using a reliable and accurate method to phenotype disease incidence and severity is essential to unravel the complex genetic architecture of disease resistance in plants, and to develop disease resistant cultivars.
Ashlyn Rairdin +10 more
doaj +1 more source
Foreground detection in camouflaged scenes [PDF]
2017 IEEE International Conference on Image Processing (ICIP), 2017 IEEE International Conference on Image Processing ...
Shuai Li 0005 +4 more
openaire +2 more sources
Background Subtraction Combining l1/2 Norm and Saliency Constraint [PDF]
Jisuanji gongcheng, 2022 The conventional background subtraction model extracts the foreground more effectively when the background is static and the foreground object propagates rapidly.However, when the background is dynamic or the foreground object propagates slowly, the ...
ZHANG Guoting, CHEN Lixia, ZHOU Zefeng
doaj +1 more source