Novel Edge Detection Using BP Neural Network Based on Threshold Binarization

H. Mehrara, Mohammad Zahedinejad, A. Pourmohammad
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引用次数: 38

Abstract

One of the most fundamental features of digital image and the basic steps in image processing, analysis, pattern recognition and computer vision is the Edge of image where the preciseness and reliability of its results will affect directly the comprehension machine system made for objective world. Several edge detectors have been developed in the past decades, although no single edge detectors have been developed satisfactorily enough for all application. In this paper, a new edge detection technique is proposed basis on the BP neural network. Here, it is classified the edge patterns of binary images into 16 possible types of visual patterns. In the following, After training the pre-defined edge patterns, the BP neural network is applied to correspond any type of edges with its related visual pattern. The results demonstrated that the new proposed technique provides the better results compared with traditional edge detection techniques while improved the computations complexity.
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基于阈值二值化的BP神经网络边缘检测
图像边缘是数字图像最基本的特征之一,也是图像处理、分析、模式识别和计算机视觉的基本步骤,其结果的准确性和可靠性将直接影响到面向客观世界的理解机系统。在过去的几十年里,已经开发了几种边缘检测器,尽管没有一种边缘检测器被开发得足以满足所有应用。本文提出了一种基于BP神经网络的边缘检测方法。本文将二值图像的边缘模式分为16种可能的视觉模式。在训练了预定义的边缘模式后,应用BP神经网络将任意类型的边缘与其相关的视觉模式相对应。结果表明,与传统的边缘检测技术相比,该方法在提高计算复杂度的同时取得了更好的检测效果。
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