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Masking and Demasking Agents

Masking and Demasking Agents

EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on the metal...

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Related Experiment Video

Updated: May 8, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Pattern masking estimation in image with structural uncertainty.

Jinjian Wu, Weisi Lin, Guangming Shi

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|September 5, 2013

Summary

This summary is machine-generated.

This study introduces a novel pattern masking function that improves visual masking models by incorporating structural uncertainty alongside luminance contrast. This new model enhances perceptual image processing and just noticeable difference estimation.

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Area of Science:

Computer Vision
Human Visual System Perception

Background:

Existing visual masking models primarily use luminance contrast, leading to inaccuracies in edge and texture regions.
Human visual system perception is influenced by both luminance contrast and structural uncertainty.

Purpose of the Study:

To develop a novel pattern masking function incorporating both luminance contrast and structural uncertainty.
To improve the accuracy of visual masking models for perceptual image processing.
To extend the pattern masking function for enhanced just noticeable difference (JND) estimation.

Main Methods:

A prediction model mimics the human visual system (HVS) to isolate unpredictable image uncertainty.
An improved local binary pattern (LBP) method is used to compute structural uncertainty.
The novel pattern masking function is derived by combining luminance contrast and structural uncertainty.

Main Results:

The proposed pattern masking function demonstrates superior performance compared to existing models.
The novel pixel domain JND model, based on the pattern masking function, shows improved consistency with the HVS.
Experimental results validate the effectiveness of the new approach in perceptual tasks.

Conclusions:

Structural uncertainty is a critical factor in visual masking, alongside luminance contrast.
The novel pattern masking function offers a more accurate representation of visual perception.
The developed JND model provides more perceptually relevant estimations for image and video processing.