IR image edge detection using neural network and clustering

Abstract

Nowadays image processing and feature extraction methods provide significantly important knowledge about images. The first step for identifying objects in an image is extracting the image properties. Edge detection is one the common features of image processing, because edges include useful information about an image. Although general public may not deal with Infrared images directly, this field is widely benefited in many sciences. Therefore, a proper infrared image edge detection method could result in thorough comprehension. In this study, infrared images are selected for edge detection due to their application in various technologies such as medical, military fields and surveillance purposes. According to the structure of these images, it is not possible to extract their edges using common methods. Therefore, a new method is proposed for edge detection of infrared images. In the proposed method, first the image is segmented by a clustering algorithm. Then, Neural Network algorithm is selected to extract the region of interest among the segmented clusters. In the last step, morphological operators are used to extract the edges from the Region of Interest. For segmentation, two K-means and Mean Shift clustering methods are applied separately, and their cluster features are used as the Neural Network inputs. Pursuant to the advantage of Mean Shift clustering algorithm in cluster number determination this method may be favorable in many cases. The evaluation results of the proposed method and comparison with other available methods indicate the method’s good performance for infrared image edge detection.