A novel hybrid edge detection technique: ABC-FA

Image processing is a vast research field with diversified set of practices utilized in so many application areas such as military, security, medical imaging, machine learning and computer vision based on extracted useful information from any kind of image data. Edges within images are undoubtedly accepted as one of the most significant features providing substantial practical information for various applications working on top of miscellaneous optimization algorithms to achieve better results. Artificial Bee Colony and Firefly algorithms are recently developed optimization algorithms and are used to obtain better results for various problems. In this study, a novel hybrid optimization technique is proposed by combining those algorithms aiming better quality in edge detection on grayscale images. The performance of the proposed algorithm is compared with individual performances of Artificial Bee Colony algorithm and the fundamental edge detection methods. The results are demonstrated that the proposed method is encouraging and also produces meaningful results for similar applications.Publisher's Versio

A Framework For Learning Scene Independent Edge Detection

In this work, a framework for a system which will intelligently assign an edge detection filter to an image based on features taken from the image is introduced. The framework has four parts: the learning stage, image feature extraction, training filter creation, and filter selection training. Two prototypes systems of this framework are given. The learning stage for these systems is the Berkeley Segmentation Database coupled with the Baddelay Delta Metric. Feature extraction is performed using a GIST methodology which extracts color, intensity, and orientation information. 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 Baddelay Delta Metric. One system uses cellular automata augmented with a fuzzy rule. The systems are trained and tested against the images from the Berkeley Segmentation Database. The results from the testing indicate that systems built on this framework can perform better than standard methods of edge detection on average across many types of images