Automatic Stitching for Hyperspectral Images Using Robust Feature Matching and Elastic Warp

Robust Automatic Feature Detection and Matching Between Multiple Images

Feature Extraction of Hyperspectral Images Using Matching Pursuit

Hyperspectral images contain rich and fine spectral information, an improvement of land use/cover classification accuracy is expected from the use of such images. However, the classification methods that have been successfully applied to multispectral data in the past are not as effective as to hyperspectral data. The major cause is that the size of training data set does not correspond to the ...

CUDA-accelerated feature matching for image stitching

Image stitching is an algorithmic pipeline that takes as input an unordered set of images and combines a subset of the input into one continuous canvas. It has applications in a wide variety of fields, including mobile phones, agriculture, military surveillance and medical imaging. For large problem sizes, the compute time of the pipeline is dominated by a step known as “feature matching”. How ...

Feature extraction of hyperspectral images using boundary semi-labeled samples and hybrid criterion

Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it is not sufficiently flexible to cope with the multi-modal distributed data. We propose a new fea...

Robust Feature Extraction and Matching for Omnidirectional Images

This paper presents a new and robust method for extracting and matching visual vertical features between images taken by an omnidirectional camera. Matching robustness is achieved by creating a descriptor which is unique and distinctive for each feature. Furthermore, the proposed descriptor is invariant to rotation. The robustness of the approach is validated through real experiments with a whe...