20. The non-transitory computer readable medium of claim 18 , wherein the computer executable instructions defining the step of selecting one of the plurality LV candidates by ranking the plurality of LV candidates based on geometrical relationships between each detected LV candidate and the other detected LV candidates, between each detected LV candidate and the detected apex candidates, and between each detected LV candidate and the detected base candidates using a trained ranking model comprise computer executable instructions defining the steps of: extracting a plurality of features for each detected LV candidate, said features including a first group of features based on geometrical relationships between each detected LV candidate and the other detected LV candidates, a second group of features based on geometrical relationships between each detected LV candidate and the detected apex candidates, and a third group of features based on geometrical relationships between each detected LV candidate and the detected base candidates; and ranking the plurality of LV candidates based on the extracted features using the trained ranking model.
20. The non-transitory computer readable medium of claim 18 , wherein the computer executable instructions defining the step of selecting one of the plurality LV candidates by ranking the plurality of LV candidates based on geometrical relationships between each detected LV candidate and the other detected LV candidates, between each detected LV candidate and the detected apex candidates, and between each detected LV candidate and the detected base candidates using a trained ranking model comprise computer executable instructions defining the steps of: extracting a plurality of features for each detected LV candidate, said features including a first group of features based on geometrical relationships between each detected LV candidate and the other detected LV candidates, a second group of features based on geometrical relationships between each detected LV candidate and the detected apex candidates, and a third group of features based on geometrical relationships between each detected LV candidate and the detected base candidates; and ranking the plurality of LV candidates based on the extracted features using the trained ranking model. 23. The non-transitory computer readable medium of claim 20 , wherein the third group of features includes a detection score for each of the detected base candidates, a distance between a predicted position of a base in each of the detected LV candidates and each of the detected base candidates, and an orientation distance between each of the detected LV candidates and each of the detected base candidates.