Colorimetric background estimation for color blending reduction of OST-HMD

AbstractBinarization plays an important role in document analysis and recognition (DAR) systems. In this paper, we present our winning algorithm in ICFHR 2018 competition on handwritten document image binarization (H-DIBCO 2018), which is based on background estimation and energy minimization. First, we adopt mathematical morphological operations to estimate and compensate the document background. It uses a disk-shaped structuring element, whose radius is computed by the minimum entropy-based stroke width transform (SWT). Second, we perform Laplacian energy-based segmentation on the compensated document images. Finally, we implement post-processing to preserve text stroke connectivity and eliminate isolated noise. Experimental results indicate that the proposed method outperforms other state-of-the-art techniques on several public available benchmark datasets.

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Background estimation based on fuzzy model for thermal video surveillance

2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS) ◽

10.1109/icetets.2016.7603055 ◽

2016 ◽

Cited By ~ 1

Author(s):

M. K. Jeevarajan ◽

P. Nirmal Kumar

Keyword(s):

Video Surveillance ◽

Fuzzy Model ◽

Background Estimation ◽

Thermal Video

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Joint Motion Segmentation and Background Estimation in Dynamic Scenes

2014 IEEE Conference on Computer Vision and Pattern Recognition ◽

10.1109/cvpr.2014.54 ◽

2014 ◽

Cited By ~ 6

Author(s):

Adeel Mumtaz ◽

Weichen Zhang ◽

Antoni B. Chan

Keyword(s):

Motion Segmentation ◽

Joint Motion ◽

Dynamic Scenes ◽

Background Estimation

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Data Processing for Atomic Resolution Electron Energy Loss Spectroscopy

Microscopy and Microanalysis ◽

10.1017/s1431927612000244 ◽

2012 ◽

Vol 18 (4) ◽

pp. 667-675 ◽

Cited By ~ 69

Author(s):

Paul Cueva ◽

Robert Hovden ◽

Julia A. Mundy ◽

Huolin L. Xin ◽

David A. Muller

Keyword(s):

Energy Loss ◽

Electron Energy ◽

Electron Energy Loss Spectroscopy ◽

Principal Component ◽

Power Laws ◽

Atomic Resolution ◽

Background Error ◽

Background Estimation ◽

Electron Microscopes ◽

Electron Energy Loss

AbstractThe high beam current and subangstrom resolution of aberration-corrected scanning transmission electron microscopes has enabled electron energy loss spectroscopy (EELS) mapping with atomic resolution. These spectral maps are often dose limited and spatially oversampled, leading to low counts/channel and are thus highly sensitive to errors in background estimation. However, by taking advantage of redundancy in the dataset map, one can improve background estimation and increase chemical sensitivity. We consider two such approaches—linear combination of power laws and local background averaging—that reduce background error and improve signal extraction. Principal component analysis (PCA) can also be used to analyze spectrum images, but the poor peak-to-background ratio in EELS can lead to serious artifacts if raw EELS data are PCA filtered. We identify common artifacts and discuss alternative approaches. These algorithms are implemented within the Cornell Spectrum Imager, an open source software package for spectroscopic analysis.

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