A SCALE-SPACE TOGGLE OPERATOR FOR IMAGE TRANSFORMATIONS

The analysis of different representation levels has been largely used in several image analysis tasks to handle the multiscale nature of image data, allowing the extraction of specific features that become explicit at each scale. In this work, we explore the scale-space properties of a self-dual toggle operator defined on a scaled morphological framework. These properties conduce to a well-controlled image simplification where its maxima and minima interact at the same time during pixels' transformation, in contrast to other approaches that consider these extrema separately. In such a way, it is possible to identify significant image extrema information to be used in several high level tasks. To assess the robustness of our approach, we carry out tests on images of several classes and subjected to different lighting conditions for various applications, including segmentation and binarization.