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Linearization and Approximation

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Related Experiment Video

Updated: May 29, 2026

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

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Image approximation by variable knot bicubic splines.

D G McCaughey¹, H C Andrews

¹MEMBER, IEEE, Department of Systems Engineering, University of Arizona, Tucson, AZ; The Analytic Sciences Corporation, McLean, VA 22102.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This summary is machine-generated.

This study quantifies image information content using degrees of freedom analysis. Bicubic splines offer parameter reduction for digital image processing, with knots indicating image activity.

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Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

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Published on: April 16, 2017

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Published on: September 28, 2018

Area of Science:

Digital Image Processing
Computational Imaging
Information Theory

Background:

Quantifying information content in digital images is crucial for efficient processing and storage.
Traditional methods often struggle to accurately represent the true degrees of freedom in sampled images.
Understanding image representability is key for advancements in digital sensors and computer vision.

Purpose of the Study:

To develop a method for analyzing the degrees of freedom (information content) of digital images.
To investigate the finite representability of images using approximation techniques.
To explore the utility of image activity indicators derived from this analysis.

Main Methods:

Developed a degree of freedom analysis for sampled images, treating it as an approximation problem.
Employed bicubic splines with variable knot placement for image approximation.
Designed algorithms for optimal knot placement to balance parameter reduction and error levels.

Main Results:

Spline approximations achieved significant parameter reductions at acceptable error levels.
Identified image knots as effective indicators of image activity.
Demonstrated the potential of knots for image segmentation and integration into smart sensor arrays.

Conclusions:

The degree of freedom analysis provides a robust method for quantifying image information content.
Bicubic splines offer an efficient way to approximate images, reducing data while maintaining fidelity.
Image knots present a promising avenue for advanced image processing functionalities, including segmentation and on-sensor computation.