Multispectral Image Feature Points

Multispectral Image Feature Points

This paper presents a novel feature point descriptor for the multispectral image case: Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge ...

Image Surface Extremal Points, new feature points for Image Registration

Image Blur Assessment with Feature Points

Blur is a key factor in the perception of image quality, leading to spread of edges in images. The quantity of feature points extracted from images can represent image shape changes. Compared with sharp images, blurred images tend to contain less feature points, and the reduction of feature points is related to blur. In this paper, we propose a new blind blur assessment metric based on feature ...

Image alignment via kernelized feature learning

Machine learning is an application of artificial intelligence that is able to automatically learn and improve from experience without being explicitly programmed. The primary assumption for most of the machine learning algorithms is that the training set (source domain) and the test set (target domain) follow from the same probability distribution. However, in most of the real-world application...

Feature Extraction of Multispectral Data