Recent Advances in Digital Signal Analysis by Orthogonal Transformations

The analysis and processing of digital signals using orthogonal transforms is an important topic in the field of computer vision and pattern recognition. These transforms are used to characterize the structural and geometric properties of shapes present in 1D, 2D and 3D digital signals, offering several advantages, including compact and robust representation of shape features, description of objects, contours or patterns in a scale-invariant, translation-invariant and rotation-invariant way. In this article, we present a comparative study of some continuous and discrete orthogonal transforms, outlining their advantages and limitations in various applications in signal/image analysis and computer vision. The results thus obtained represent a clear roadmap for researchers concerning the use of discrete orthogonal transforms for the tasks of reconstruction, compression, and localization of Region of Interest (ROI) in digital signals.