This paper addresses the problem of image fusion of optical (visible and thermal domain) data and radar data for the purpose of visualization. These types of images typically contain a lot of complimentary information, and their joint visualization can be useful and more convenient for human user than a set of individual images. To solve the image fusion problem we propose a novel algorithm that utilizes some peculiarities of human color perception and based on the grey-scale structural visualization. Benefits of presented algorithm are exemplified by satellite imagery.
Registration of images of different nature is an important technique used in image fusion, change detection, efficient information representation and other problems of computer vision. Solving this task using feature-based approaches is usually more complex than registration of several optical images because traditional feature descriptors (SIFT, SURF, etc.) perform poorly when images have different nature. In this paper we consider the problem of registration of SAR and optical images. We train neural network to build feature point descriptors and use RANSAC algorithm to align found matches. Experimental results are presented that confirm the method’s effectiveness.
We study a technique for improving visualization quality of noisy multispectral images. Contrast form visualization approach is considered, which guarantees a non-zero contrast in the output image when there is a difference between the spectra of the object and the background in the input image. The improvement is based on channel weighting according to estimation of the noise level. We show this approach to reduce noise in color visualization of real multispectral images. The low-noise visualizations are demonstrated to be more comprehensive to a human on examples from a publicly available dataset of Earth surface images. Noise variance estimation needed for weighting uses the method proposed earlier by the authors. The validation dataset consists of publicly available images of Earth surface.
We describe an original low cost hardware setting for efficient testing of stereo vision algorithms. The method uses a combination of a special hardware setup and mathematical model and is easy to construct, precise in applications of our interest. For a known scene we derive its analytical representation, called virtual scene. Using a four point correspondence between the scene and virtual one we compute extrinsic camera parameters, and project virtual scene on the image plane, which is the ground truth for depth map. Another result, presented in this paper, is a new depth map quality metric. Its main purpose is to tune stereo algorithms for particular problem, e.g. obstacle avoidance.
The study concerned deals with a new approach to the problem of detecting vehicle passes in vision-based automatic vehicle classification system. Essential non-affinity image variations and signals from induction loop are the events that can be considered as detectors of an object presence. We propose several vehicle detection techniques based on image processing and induction loop signal analysis. Also we suggest a combined method based on multi-sensor analysis to improve vehicle detection performance. Experimental results in complex outdoor environments show that the proposed multi-sensor algorithm is effective for vehicles detection.