Segmentation method of overlapped double apples based on Snake model and corner detectors

Abstract: To achieve successful segmentation of overlapped apples, a segmentation method by using Snake model and corner detectors was presented. As contour is an important basis for detection and recognition of object, and remarkable characteristic of overlapped apples has some typical angular points, which are also called segmentation points and in the target contour. Since Snake model could better converge to target's concave places, Snake model was used to extract overlapped apples' outline. For searching overlapped apples' corner points, corner detection algorithm based distance was proposed: 1) overlapped apples' contour was coded; 2) the distance between contour points and the given 'center point' was calculated, where 'center point' was overlapped apples' centroid point for the simplicity of calculation; 3) the distance curve that was get in step 2 is useless as it may engender a lot of spurious corner points. This is caused by small disturbances of small distance, for removing spurious corner points, db1 wavelet was utilized to decomposed original signal at level three, there is a relationship between wavelet transform and digital filter banks. so the wavelet transform can be simply achieved by a tree of digital filter banks. The idea behind filter banks is to divide a signal into two parts: one is the low frequency part and the other is the high frequency part, which could be achieved by a set of filters, the low frequency that is approximate version of the original distance curve in this paper don't contain detail components of original distance and is beneficial to detect true corner points. But the problem with the use of these filters is that each of the two decomposed signals is subjected to downsampling, which simply means throwing away every second data point. After decomposition with three levels, the length of approximated signal reduced, which may cause the miss of the index of original contour point. As for this reason, the approximated signal must be recovered to its original length. In this paper, the interpolation algorithm with the use of splines is carried out to recover the length of approximated signal; 4) corner points represent the enormous changes of curvature and is shown the maximum or minimum on the distance curve, therefore, corner points can be detected by calculating the extrimum of distance curve. Detected corner points need to be selected to determine overlapping positions, a segmentation method based long axis segmentation rule was proposed to choose segmentation line: 1) overlapped apples were divided into uniform two parts approximately by long axis; 2) segmentation line was chose by calculating the distance between bilateral corner points and centroid point. Some split criteria were given as: 1) the direction of the detected points should be opposite, which meant that the detected points from the same region should not be used to split an object; 2) the length of split line should be short. By using these given criteria, the detected corner points were matched to realize the segmentation of overlapped apples. To validate the effectiveness of the algorithm, 20 overlapped apples in nature scenes were tested. Compared with segmentation line obtained by artificial calculation, highest segmentation error of the proposed method is 13.27°, minimum error is 1.20°, and the average error 6.41°. Experimental results show that the proposed segmentation algorithm has a preferable performance, and it is feasible and valid for overlapped apple segmentation in nature scenes.