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Potential Due to a Polarized Object

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A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...

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Updated: May 24, 2026

Automated Analysis of C. elegans Fluorescence Images using SegElegans

Automated Analysis of C. elegans Fluorescence Images using SegElegans

Published on: October 10, 2025

Semi-Huber potential function for image segmentation.

Osvaldo Gutiérrez¹, Ismael de la Rosa, Jesús Villa

¹Unidad Academica de Ingenieriıa Electrica, Universidad Autonoma de Zacatecas, Av. Lopez Velarde 801, Col. Centro, C. P. 98000, Zacatecas, Zacatecas, Mexico. osvaldo_gtz_mt@hotmail.com

|March 16, 2012

Summary

This summary is machine-generated.

A new Markov Random Field (MRF) model with a Semi-Huber potential simplifies image segmentation. This novel approach requires fewer parameters, enabling easier adjustment and efficient noise reduction in images.

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Area of Science:

Computer Vision
Image Processing
Computational Mathematics

Background:

Image segmentation is crucial for image analysis.
Noise significantly degrades segmentation accuracy.
Existing half-quadratic models can be complex with numerous parameters.

Purpose of the Study:

Introduce a novel Markov Random Field (MRF) model for image segmentation.
Develop a model utilizing a Semi-Huber potential function.
Simplify parameter tuning compared to existing methods.

Main Methods:

Proposed a new Markov Random Field (MRF) model.
Incorporated a Semi-Huber potential function.
Applied the model to image segmentation tasks with noisy images.

Main Results:

The novel MRF model successfully segmented images in the presence of noise.
The proposed model features a reduced and simpler set of parameters.
Experimental results demonstrated easier parameter adjustment and reasonable computation times.

Conclusions:

The Semi-Huber potential-based MRF model offers an effective and simplified approach to image segmentation.
The model's reduced parameter complexity facilitates practical application.
This method provides a computationally efficient solution for segmenting noisy images.