François Chung, Ph.D.

Publication

Rafael Redondo, Gloria Bueno, François Chung, Rodrigo Nava, J. Víctor Marcos, Gabriel Cristóbal, Tomás Rodríguez, Amelia González-Porto, Cristina Pardo, Óscar Déniz, Boris Escalante-Ramírez; Pollen segmentation and feature evaluation for automatic classification in bright-field microscopy; In: Computers and Electronics in Agriculture (COMPAG), 110, pp. 56–69, 2015.

Abstract

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.

Mots-clés

• apiculture
• automatic classification
• bright-field microscopy
• feature extraction
• Fisher discriminant analysis
• image processing
• morphology descriptors
• pollen
• statistical descriptors
• texture descriptors

Références