In the past weeks I have been looking at feature extraction methods like HOG, SIFT and SURF. These papers have given me great insight and some ideas. While I was reading these papers, I was always thinking to myself that they did not exploit one of the main characteristics of my problem: I’m working with time series.
I’m wondering what other approaches there are to extract features from a series of pictures taken from the same place. What we will have in Zackenberg is a time series of pictures that will document the growth (phenological change) of the flowers. It will contain pictures of different stages in the flower’s growth. Moreover, it will contain pictures with different lighting of the same stage of the same flower. This is all additional information that can be included in the feature and could be used for detecting.
I’m also expecting that the process needs to be spread out within a season as opposed to just taking information from one picture. The workflow needs to be “incremental”. At a particular moment in time, it needs to gather image information (plant location, species, sex…) from the start of the season so as to have correct conclusions. It can happen, for example, that a flower becomes occluded at the beginning of the season and comes out again towards the end. This is information that can be used to add to the accuracy of the flower count.