New Aggregation Approaches with HSV to Color Edge Detection
Pablo A. Flores-Vidal, Daniel Gómez, Javier Castro, Javier Montero
Abstract
Abstract The majority of edge detection algorithms only deal with grayscale images, while their use with color images remains an open problem. This paper explores different approaches to aggregate color information of RGB and HSV images for edge extraction purposes through the usage of the Sobel operator and Canny algorithm. This paper makes use of Berkeley’s image data set, and to evaluate the performance of the different aggregations, the F -measure is computed. Higher potential of aggregations with HSV channels than with RGB channels is found. This article also shows that depending on the type of image used, RGB or HSV, some methods are more appropriate than others.
Topics & Concepts
HSL and HSVSobel operatorRGB color modelArtificial intelligenceGrayscaleComputer scienceCanny edge detectorComputer visionImage gradientEdge detectionColor histogramColor imageEnhanced Data Rates for GSM EvolutionImage (mathematics)Set (abstract data type)Pattern recognition (psychology)Image processingVirologyBiologyVirusProgramming languageMedical Image Segmentation TechniquesImage Retrieval and Classification TechniquesRemote-Sensing Image Classification