An improved optical flow method to estimate Arctic sea ice velocity(winter 2014-2016)

Sea ice velocity impacts the distribution of sea ice,and the flux of exported sea ice through the Fram Strait increases with increasing ice velocity.Therefore,improving the accuracy of estimates of the sea ice velocity is important.We introduce a pyramid algorithm into the Horn-Schunck optical flow(HS-OF)method(to develop the PHS-OF method).Before calculating the sea ice velocity,we generate multilayer pyramid images from an original brightness temperature image.Then,the sea ice velocity of the pyramid layer is calculated,and the ice velocity in the original image is calculated by layer iteration.Winter Arctic sea ice velocities from 2014 to 2016 are obtained and used to discuss the accuracy of the HS-OF method and PHS-OF(specifically the 2-layer PHS-OF(2 LPHS-OF)and 4-layer PHS-OF(4 LPHS-OF))methods.The results prove that the PHS-OF method indeed improves the accuracy of sea ice velocity estimates,and the 2 LPHS-OF scheme is more appropriate for estimating ice velocity.The error is smaller for the 2 LPHS-OF velocity estimates than values from the Ocean and Sea Ice Satellite Application Facility and the Copernicus Marine Environment Monitoring Service,and estimates of changes in velocity by the 2 LPHS-OF method are consistent with those from the National Snow and Ice Data Center.Sea ice undergoes two main motion patterns,i.e.,transpolar drift and the Beaufort Gyre.In addition,cyclonic and anticyclonic ice drift occurred during winter 2016.Variations in sea ice velocity are related to the open water area,sea ice retreat time and length of the open water season.

Tracking and recognition algorithm for a robot harvesting oscillating apples

Apple fruits on trees tend to swing because of wind or other natural causes,therefore reducing the accuracy of apple picking by robots.To increase the accuracy and to speed up the apple tracking and identifying process,tracking and recognition method combined with an affine transformation was proposed.The method can be divided into three steps.First,the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue(2R-G-B)color feature;after improving the binary image,the apples were recognized with a local parameter adaptive Hough circle transformation method,thus improving the accuracy of recognition and avoiding the long,time-consuming process and excessive fitted circles in traditional Hough circle transformation.The process and results were verified experimentally.Second,the Shi-Tomasi corners detected and extracted from the first frame image were tracked,and the corners with large positive and negative optical flow errors were removed.The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm(RANSAC)to correct the scale of the template image and predict the apple positions.Third,the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm.The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8%and 20%in the absence of template correction and apple position prediction,respectively.In comparison,the running time of our algorithm was 25 ms,and the tracking error was less than 4%.Therefore,test results indicate that speed and efficiency can be greatly improved by using our method,and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits.