All-sky imaging systems are currently very popular. They are used in ground-based meteorological stations and as a crucial part of the weather monitors for autonomous robotic telescopes. Data from all-sky imaging cameras provide important information for controlling meteorological stations and telescopes, and they have specific characteristics different from widely-used imaging systems. A particularly promising and useful application of all-sky cameras is for remote sensing of cloud cover. Post-processing of the image data obtained from all-sky imaging cameras for automatic cloud detection and for cloud classification is a very demanding task. Accurate and rapid cloud detection can provide a good way to forecast weather events such as torrential rainfalls. However, the algorithms that are used must be specifically calibrated on data from the all-sky camera in order to set up an automatic cloud detection system. This paper presents an assessment of a modified k-means++ color-based segmentation algorithm specifically adjusted to the WILLIAM (WIde-field aLL-sky Image Analyzing Monitoring system) ground-based remote all-sky imaging system for cloud detection. The segmentation method is assessed in two different color-spaces (L*a*b and XYZ). Moreover, the proposed algorithm is tested on our public WMD database (WILLIAM Meteo Database) of annotated all-sky image data, which was created specifically for testing purposes. The WMD database is available for public use. In this paper, we present a comparison of selected color-spaces and assess their suitability for the cloud color segmentation based on all-sky images. In addition, we investigate the distribution of the segmented cloud phenomena present on the all-sky images based on the color-spaces channels. In the last part of this work, we propose and discuss the possible exploitation of the color-based k-means++ segmentation method as a preprocessing step towards cloud classification in all-sky images.

The field of CCTV surveillance is topical and widely used in many different applications. The fundamental part of the CCTV system is a reliable image evaluation by a human observer, whose effectiveness is influenced by many variables. Optimization of the effectiveness is a multidimensional problem related to both technical and human characteristics. In many applied systems, the overall performance is affected by a real performance of technical system (image compression, channel transmission, etc.). On the other hand these technical systems have different optimization criteria than a typical video system. TNO Defence, Security and Safety (formerly the TNO Physics and Electronics Laboratory) initiated a project in which the influence of several technical variables on the operator's effectiveness was studied. The whole project was carried out in close co-operation with the Czech Technical University in Prague (CTU). The paper demonstrates and summarizes selected experimental results and evaluated dependencies.

Ultra-wide-field of view (UWFOV) imaging systems are affected by various aberrations, most of which are highly angle-dependent. A description of UWFOV imaging systems, such as microscopy optics, security camera systems and other special space-variant imaging systems, is a difficult task that can be achieved by estimating the Point Spread Function (PSF) of the system. This paper proposes a novel method for modeling the space-variant PSF of an imaging system using the Zernike polynomials wavefront description. The PSF estimation algorithm is based on obtaining field-dependent expansion coefficients of the Zernike polynomials by fitting real image data of the analyzed imaging system using an iterative approach in an initial estimate of the fitting parameters to ensure convergence robustness. The method is promising as an alternative to the standard approach based on Shack–Hartmann interferometry, since the estimate of the aberration coefficients is processed directly in the image plane. This approach is tested on simulated and laboratory-acquired image data that generally show good agreement. The resulting data are compared with the results of other modeling methods. The proposed PSF estimation method provides around 5% accuracy of the optical system model.

The subjective image quality of image or video information is a crucial item in security imaging systems. During the last five years our lab has tested and verified various approaches to the image compression for security purposes and the evaluation of subjective image quality. In the paper we discuss selected important facts related to the subjective image quality evaluation and we present some anomalous experimental behavior of image compression techniques. An object-defined approach is investigated and advantageous characteristics of chosen methods are deployed to achieve the optimal performance of the surveillance video coder. Among others, we propose to use the artificial neural network (ANN) to predict resulting image quality rating scores. The proposed quality assessment model has been trained and tested using a set of grayscale images distorted by selected image compression algorithms