Gradient extraction operators for discrete interval-valued data
Carlos López-Molina, Cédric Marco-Detchart, Juan Cerrón, Humberto Bustince, Bernard De Baets · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2015
Digital images are generally created as discrete measurements of light, as performed by dedicated sensors.Consequently, each pixel contains a discrete approximation of the light inciding in a sensor element.The nature of this measurement implies certain uncertainty due to discretization matters.In this work we propose to model such uncertainty using intervals, further leading to the generation of so-called interval-valued images.Then, we study the partial differentiation of such images, putting a spotlight on antisymmetric convolution operators for such task.Finally, we illustrate the utility of the interval-valued images by studying the behaviour of an extended version of the well-known Canny edges detection method.