Abstract:

In order to remove the drawbacks of the traditional single-threshold segmentation methods,namely heavycomputational burden,low efficiency and incapability of multi-target segmentation,an improved multi-thresholdsegmentation method of the snake-like robot is proposed based on the minimum interclass variance ( MIV) .In thismethod,first,the ROI ( Region of Interest) of the whole image is extracted to effectively reduce the search scopeand hence lighten the computational burden.Then,according to the multi-peak characteristics of histograms,the ROI is divided into multiple sub-regions,and the local optimal thresholds of the sub-regions are obtained by meansof the MIV segmentation.Thus,the multi-threshold segmentation of the joint groups of the snake-like robot issuccessfully implemented.Experimental results indicate that the proposed method is of high segmentation efficiency,good segmentation effect,constant real-time performance,stronger target recognition robustness to light intensitychange and lower sensitivity to gait change.

Key words: snake-like robot, multi-threshold segmentation, Region of Interest, minimum interclass variance