Figure 1
The scoring pipeline. (a) The original image. (b) An image stack obtained from image processing. (i) Heatmaps of Gabor responses. White areas represent pixels of high response. (ii) Heatmaps of orientation histograms. White areas represent square centers with high `largest bin value' statistic. (c) Scanning the image and scoring each square. (i) Each square is associated with a feature vector encoding the values of 466 features. Each colored arrow is intended to represent a feature vector from one square subregion of the image. (ii) Each feature vector propagates differently through the alternating decision tree. (iii) A real-valued score is thereby associated with each feature vector. (d) The maximum score marked in red over all squares is taken as the image score. |