Cubic Spline Interpolation Segmenting over Conventional Segmentation Procedures: Application and Advantages

TL;DR

This paper introduces a novel image segmentation method using Cubic Spline Interpolation, comparing its efficiency and accuracy with traditional techniques like Otsu thresholding and polynomial least squares, demonstrating improved performance based on deviation and mean square error.

Contribution

The paper proposes a new spline interpolation-based segmentation method and evaluates its advantages over conventional techniques through comparative analysis.

Findings

Spline interpolation yields lower deviation and mean square error.

The proposed method outperforms traditional segmentation techniques.

Image equalization enhances segmentation accuracy.

Abstract

To design a novel method for segmenting the image using Cubic Spline Interpolation and compare it with different techniques to determine which gives an efficient data to segment an image. This paper compares polynomial least square interpolation and the conventional Otsu thresholding with spline interpolation technique for image segmentation. The threshold value is determined using the above-mentioned techniques which are then used to segment an image into the binary image. The results of the proposed technique are also compared with the conventional algorithms after applying image equalizations. The better technique is determined based on the deviation and mean square error when compared with an accurately segmented image. The image with least amount of deviation and mean square error is declared as the better technique.