Automatic detection and classification of grains of pollen based on shape and texture

Rodriguez-Damian, M.; Cernadas, E.; Formella, Arno; Fernández-Delgado, Manuel; Sa-Otero, Pilar De

doi:10.1109/tsmcc.2005.855426

Cited by 65 publications

(56 citation statements)

References 35 publications

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“…The results showed success rates between 90.54% and 92,81%, pointing out the quality of the presented parameters for pollen grain classification. Moreover, these results improve those achieved by other authors such as [10], [22], [11] and [13], even though the number of classified species was significantly larger.…”

Section: Discussionsupporting

confidence: 81%

“…[12] and [13] presented a more complex work, combining shape and ornamentation of the grains; using simple geometric measures, and concurrence matrices applied for the measurement of texture. Again, artificial neural networks were used for classification.…”

Section: Related Workmentioning

confidence: 99%

“…Several works such as [10], [22], [11] and [13] used artificial neural networks (ANNs) as classifiers. These algorithms works as follow:…”

Section: Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Image Processing for Pollen Classification

Pozo-Baños¹,

Ticay-Rivas²,

Cabrera-Falcón³

et al. 2012

Biodiversity Enrichment in a Diverse World

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Section: Discussionsupporting

confidence: 81%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Image Processing for Pollen Classification

Pozo-Baños¹,

Ticay-Rivas²,

Cabrera-Falcón³

et al. 2012

Biodiversity Enrichment in a Diverse World

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“…However, this process requires the laboratory work of melissopalynology experts, and is thus time consuming and costly. There have been many attempts to automate pollen grain identification by computer algorithms but there is no inexpensive, complete, and automated process (Allen, 2006;Boucher et al, 2002;France et al, 2000;Rodríguez-Damián et al, 2006). Experts use macroscopic identification of bee pollen loads by means of such properties as colour.…”

Section: Introductionmentioning

confidence: 99%

Discernment of bee pollen loads using computer vision and one-class classification techniques

Chica¹,

Campoy²

2012

Journal of Food Engineering

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In this paper, we propose a system for authenticating local bee pollen against fraudulent samples using image processing and classification techniques. Our system is based on the colour properties of bee pollen loads and the use of one-class classifiers to reject unknown pollen samples. The latter classification techniques allow us to tackle the major difficulty of the problem, the existence of many possible fraudulent pollen types.Also presented is a multi-classifier model with an ambiguity discovery process to fuse the output of the one-class classifiers. The method is validated by authenticating Spanish bee pollen types, the overall accuracy of the final system of being 94%. Therefore, the system is able to rapidly reject the non-local pollen samples with inexpensive hardware and without the need to send the product to the laboratory.

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“…Bonton et al [2] use the color information after a special staining of the air samples, but such staining procedures are impractical for our automated system. Rodriguez-Damian et al [3] tried different standard segmentation techniques, among them also a circular Hough transform, followed by a snake approach. Due to the use of manual prepared pure pollen samples without occlusions or agglomerations, the standard circular Hough transform was sufficient there.…”

Section: Introductionmentioning

confidence: 99%

Fast and robust segmentation of spherical particles in volumetric data sets from brightfield microscopy

Ronneberger¹,

Wang²,

Burkhardt³

2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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In this article we present an approach for a precise segmentation of spherical particles in transmitted light image stacks. A main goal was its fast operation and a high robustness to occlusions and agglomerations of the particles. The system is based on a voting procedure that finds the centers and radii of the particles and a subsequent precise segmentation with an active contour approach. To meet the demands of an online pollenmonitor for high speed and low memory consumption a multi-scale approach was applied. The proposed techniques successfully segmented the pollen grains in a vast amount of different air samples (about 2.7TB of raw data). The results on one of the most cluttered samples are presented in this paper.

show abstract

Automatic detection and classification of grains of pollen based on shape and texture

Cited by 65 publications

References 35 publications

Image Processing for Pollen Classification

Image Processing for Pollen Classification

Discernment of bee pollen loads using computer vision and one-class classification techniques

Fast and robust segmentation of spherical particles in volumetric data sets from brightfield microscopy

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