Contour Detection in Cassini ISS images based on Hierarchical Extreme Learning Machine and Dense Conditional Random Field

TL;DR

This paper introduces a fast and accurate contour detection method for Cassini ISS images using Hierarchical Extreme Learning Machine and DenseCRF, outperforming traditional and deep learning methods.

Contribution

The paper presents a novel contour detection algorithm combining H-ELM and DenseCRF tailored for Cassini ISS images, with improved accuracy and efficiency.

Findings

Outperforms traditional machine learning methods like SVM and ELM.

Achieves better contour accuracy than deep convolutional neural networks.

Operates efficiently on standard PC hardware.

Abstract

In Cassini ISS (Imaging Science Subsystem) images, contour detection is often performed on disk-resolved object to accurately locate their center. Thus, the contour detection is a key problem. Traditional edge detection methods, such as Canny and Roberts, often extract the contour with too much interior details and noise. Although the deep convolutional neural network has been applied successfully in many image tasks, such as classification and object detection, it needs more time and computer resources. In the paper, a contour detection algorithm based on H-ELM (Hierarchical Extreme Learning Machine) and DenseCRF (Dense Conditional Random Field) is proposed for Cassini ISS images. The experimental results show that this algorithm's performance is better than both traditional machine learning methods such as SVM, ELM and even deep convolutional neural network. And the extracted contour is closer to the actual contour. Moreover, it can be trained and tested quickly on the general configuration of PC, so can be applied to contour detection for Cassini ISS images.