Edible bird nest shape inspection using fourier descriptor (FD) and farthest fourier point signature (FFPS) method

Swiftlets are birds contained within the four genera aerodramus,hydrochous,schoutedenapus and collocalia. to date,the bird nest grading is based on weight,shape and size. current inspection and grading for raw,edible bird nest were performed visually by expert panels. this conventional method is relying more on human judgments and often biased. a novel hybrid method from fourier descriptor (fd) method and farthest fourier point signature (ffps) was developed using charge coupled device (ccd) image data to grade bird nest by its shape and size. from the result,the hybrid method was able to differentiate different shape such as super aaa,super and corner grade depending on the swiftlet species and geographical origin. the wilks' lambda analysis was invoked to transform and compress the data set comprising of a large number of interconnected variables to a reduced set of varieties. overall,the vision system was able to correctly classify 92.6 % of the super aaa,super and corner shaped grades using the combined fd and ffps features. © 2015 penerbit utm press. all rights reserved.