The purpose of detecting sharp changes in image brightness is to capture important events and changes in properties of the world. The accuracy of edge detection methods in image processing determines the eventual success or failure of computerized analysis procedures which follow the initial edge detection determinations such as object recognition. Generally, edge detectors have been designed to capture simple ideal step functions in image data, but real image signal discontinuities deviate from this ideal form. Another three types of deviations from the step function which relate to real distortions occurring in natural images are examined according to their characteristics. These types are impulse, ramp, and sigmoid functions which respectively represent narrow line signals, simplified blur effects, and more accurate blur modeling. General rules for edge pattern characterization based upon the classification of edge types into four categories-ramp, impulse, step, and sigmoid (RISS) are developed from this analysis. Additionally, the proposed algorithm performs connectivity analysis on edge map to ensure that small, disconnected edges are removed. The performance analysis on experiments supports that the proposed edge detection algorithm with edge pattern analysis and characterization does lead to more effective edge detection and localization with improved accuracies. To expand the proposed algorithm into real-time applications, a parallel implementation on a graphics processing unit (GPU) is presented in this paper. For the various configurations in our test, the GPU implementation shows a scalable speedup as the resolution of an image increases. We also achieved 14 frames per second in real-time processing (1280×720).