Konstantinos K. Delibasis

George Nousias, Konstantinos Delibasis, Ilias Maglogiannis

Estimating the homography matrix between images captured under radically different camera poses and zoom factors is a complex challenge. Traditional methods rely on the Random Sample Consensus (RANSAC) algorithm, which requires pairs of homologous points, pre-matched based on local image feature vectors. Sampling consensus is a core step in many Artificial Intelligence (AI) algorithms that enable computer systems to recognize patterns in data. In this paper, we propose H-RANSAC, an algorithm for homography estimation that eliminates the need for feature vectors or explicit point pairing, while it optionally supports point labeling into two classes. H-RANSAC introduces a novel geometric (cheiral) criterion that intelligently rejects implausible point configurations at the beginning of each iteration, while leveraging concave quadrilaterals typically discarded by similar algorithms. A post-hoc criterion at the end of each iteration improves accuracy further. Analytical derivations of the expected maximum iterations are provided, considering success probabilities and outlier rates, enabling adaptive performance tuning. The algorithm is validated on a demanding task: estimating homography between video frames of football matches captured by 12 cameras with highly divergent viewpoints. Results show that H-RANSAC significantly outperforms state-of-the-art classical methods, combined with deep learning-based salient point detection, in terms of average reprojection error and success rates. The relevant implementation is available in https://github.com/gnousias/H-RANSAC.

Nicholas Vaiopoulos, Alexander Vavoulas, Harilaos G. Sandalidis et al.

This letter derives closed-form expressions for the probability density function of the distance between two nodes located in heterogeneous concentric geometries, namely a disk or sphere and a surrounding annulus or spherical shell. Two scenarios are considered: (i) both nodes are independently distributed in different regions, disk or sphere and annulus or shell, and (ii) one node is static in the outer region while the other follows the stationary distribution of the random waypoint model in the inner region. The resulting expressions provide a tractable analytical tool for performance evaluation in concentric wireless regions.

Konstantinos K. Delibasis, Iro Oikonomou, Aristides I. Kechriniotis et al.

A number of basic image processing tasks, such as any geometric transformation require interpolation at subpixel image values. In this work we utilize the multidimensional coordinate Hermite spline interpolation defined on non-equal spaced, rectilinear grids and apply it to a very common image processing task, image zooming. Since Hermite interpolation utilizes function values, as well as partial derivative values, it is natural to apply it to image processing tasks as a special case of equi-spaced grid, using numerical approximations of the image partial derivatives at each pixel. Furthermore, the task of image interpolation requires the calculation of image values at positions with nono-zero fractional part. Thus, any spline interpolation can be written as convolution with an appropriate kernel. In this context we generate the Hermite kernels according to the derived $n-$dimensional interpolant of Theorem 2 in [1]. We show that despite the increased complexity of the interpolant, once the kernels are constructed, the Hermite spline interpolation can be applied to images as efficiently as any other less complicated method. Finally, we perform illustrative numerical examples to showcase the applicability and high accuracy of the proposed Hermite kernels for image zooming, compared to other interpolation methods, both traditional convolution-based, as well as employing deep learning, in terms of PSNR, as well as SSIM error metrics. The proposed Hermite spline kernels outperform all other methods in the majority of the test images, in experiments using many cascaded repetitions of the zoom operation. Interesting conclusions can be drawn considering all methods under comparison.

D. K. Iakovidis, M. Ooi, Y. C. Kuang et al.

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.