Paper Info
Title
A Framework For Intelligent Creation Of Edge Detection Filters
Abstract
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 Baddelay 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.
Year
DOI
Venue
2015
10.1109/SMC.2015.472
2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS
Keywords
Field
DocType
Cellular Automata, multi-scene edge detection, edge filter training, scene classification
Image gradient,Feature detection (computer vision),Computer science,Edge detection,Artificial intelligence,Computer vision,Pattern recognition,Feature (computer vision),Image texture,Feature extraction,Ground truth,Backpropagation,Machine learning
Conference
ISSN
Citations 
PageRank 
1062-922X
0
0.34
References 
Authors
8
3
Name
Order
Citations
PageRank
Aaron Wilbee100.34
Ferat Sahin270645.49
U. Sahin3232.20