# A new Edge Detector Based on Parametric Surface Model: Regression Surface Descriptor

## TL;DR

This paper introduces a novel edge detection method that models image content as a parametric surface, providing high customization and robustness to noise and blur, with demonstrated superior performance over existing detectors.

## Contribution

The paper proposes a new edge detection approach based on a parametric surface model, offering enhanced adaptability and noise robustness compared to traditional methods.

## Key findings

- High customization for various scales of edge detection
- Robustness to blurring and additive noise
- Superior performance in comparative studies

## Abstract

In this paper we present a new methodology for edge detection in digital images. The first originality of the proposed method is to consider image content as a parametric surface. Then, an original parametric local model of this surface representing image content is proposed. The few parameters involved in the proposed model are shown to be very sensitive to discontinuities in surface which correspond to edges in image content. This naturally leads to the design of an efficient edge detector. Moreover, a thorough analysis of the proposed model also allows us to explain how these parameters can be used to obtain edge descriptors such as orientations and curvatures. In practice, the proposed methodology offers two main advantages. First, it has high customization possibilities in order to be adjusted to a wide range of different problems, from coarse to fine scale edge detection. Second, it is very robust to blurring process and additive noise. Numerical results are presented to emphasis these properties and to confirm efficiency of the proposed method through a comparative study with other edge detectors.