A Framework for Intelligent Creation of Edge Detection Filters

In this paper, we introduce a framework for a system which intelligently assigns an edge detection filter to an image based only on features taken from the image. The system has four parts, the training set which consists of an image and its edge image ground truth, feature extraction, training filter creation, and system training. A prototype system of this framework is given. In the system feature extraction is performed using a GIST methodology which extracts color, intensity, and orientation information as features. The set of image features are used as the input to a single hidden layer feed forward neural network trained using back propagation. The system trains against a set of linear Cellular Automata filters which are determined to best solve the edge image according to the Bad delay Delta Metric. This metric takes into account false positives and false negative error by scaling the errors relative to the perpendicular distance that they are off from the ground truth. The system was trained and tested against the images from the Berkeley Segmentation Database. The results from the testing indicate that the system performs better than standard methods in many cases but on the whole is only on par across a wide range of images.