Pollen segmentation and feature evaluation for automatic classification in bright-field microscopy

• Pollen collection: 15 types – 120 samples/type.
• Proposal of contour–inner pollen segmentation: 50% accuracy rates.
• New contour profile descriptor.
• LogGabor descriptors firstly tested for pollen classification.
• Experiments of descriptor’s state of the art combination: rates above 99%.

Besides the well-established healthy properties of pollen, palynology and apiculture are of extreme importance to avoid hard and fast unbalances in our ecosystems. To support such disciplines computer vision comes to alleviate tedious recognition tasks. In this paper we present an applied study of the state of the art in pattern recognition techniques to describe, analyze, and classify pollen grains in an extensive dataset specifically collected (15 types, 120 samples/type). We also propose a novel contour–inner segmentation of grains, improving 50% of accuracy. In addition to published morphological, statistical, and textural descriptors, we introduce a new descriptor to measure the grain’s contour profile and a logGabor implementation not tested before for this purpose. We found a significant improvement for certain combinations of descriptors, providing an overall accuracy above 99%. Finally, some palynological features that are still difficult to be integrated in computer systems are discussed.