Feature point detection in multiframe images

This paper deals with multiframe feature point detection, i.e. detection in two or more images of the same scene which are supposed to be blurred, noisy, rotated and shifted with respect to each other. We present a new method invariant under rotation, able to handledifferently blurred images. The point sets extracted from different frames have relatively high number of common elements, which is highly desirable for further processing. The performance of the method is demonstrated on satellite images

How to find it in the data?

The lecture aims to introduce the activities of the Image Processing Department of the Institute of Image Information of the CAS in the field of Copernicus data analysis to the professional public. The department has long been involved in the development of digital image processing and deep learning methods. During the last two years, in cooperation with the MFF UK and FJFI CTU, several student demonstration projects using data from Sentinel satellites have been finished, such as crop type recognition from Sentinel-2 time-series images, automatic segmentation of areas by land use or surface type using machine learning methods learning, more accurate cloud detection in Sentinel-2 data, in collaboration with the Institute of Hydrodynamics of the CAS Czech Republic, procedures for estimating landscape surface moisture from Sentinel-2 data and increasing the resolution of Sentinel-3 thermal data using deep learning methods. The second part will present the application of developed methods for other areas in remote sensing

Conservation of "The Last Judgement" mosaic: Image processing

Conservation of the Last Judgement Mosaic in Prague Castle. Evaluation of the reconstruction accuracy by image processing techniques

Combined invariants to linear filtering and rotation

A new class of moment-based features invariant to image rotation, translation, scaling, contrast changes and also to convolution with an unknown PSF are introduced in this paper. These features can be used for the recognition of objects captured by a nonideal imaging system of unknown position and blurring parameters