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A Method for Detecting Breaches and New Objects in Multiple Outdoor Images

机译:一种检测多个户外图像中的违规和新对象的方法

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This paper presents a new automated change detection method for detecting breaches in the integrity of and attached objects in front of fence wires in multiple outdoor images of the same scene containing fence wires acquired by a mobile camera from slightly different viewing positions, angles and at different times. To detect significant changes, edges of fence wires firstly have to be extracted from multiple outdoor images using a combination of the Sobel detector and an adaptive thresholding technique. Secondly, morphological operations such as dilation and erosion are applied into binary images produced by the previous process in enhancing the binary images. Next, an area-based algorithm is applied to enhanced binary images in separating small and big objects based on their average areas determined once in the calibration process. Finally, objects that survive are then fed into a fuzzy inference system in calculating their possibility values. Based on these possibility values, the survived objects can be classified as significant or unimportant changes. Experimental results demonstrate that the method has a high success rate (94.12%) in detecting true positives in these kinds of multiple outdoor images.
机译:本文介绍了一种新的自动变化检测方法,用于检测围栏前面的围栏线的完整性和附着物体中的泄露的泄漏,其中包含由移动摄像机获取的围绕从略微不同观察位置,角度和不同的移动相机时代。为了检测显着的变化,首先必须使用Sobel检测器的组合和自适应阈值技术的组合从多个室外图像中提取围栏线的边缘。其次,将诸如扩张和侵蚀的形态学操作被应用于通过先前的过程在增强二值图像中产生的二进制图像中。接下来,将基于区域的算法应用于基于在校准过程中确定的平均区域分离小和大物体的增强二进制图像。最后,然后将存活的对象馈入计算其可能性值的模糊推理系统中。基于这些可能性值,存活的对象可以被归类为显着或不重要的变化。实验结果表明,该方法具有高成功率(94.12%)在检测这些多种户外图像中的真正阳性方面。

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