Pollen classification using brightness-based and shape-based descriptors

Rodriguez-Damian, M.; Cernadas, E.; Formella, Arno; Sa-Otero, R.

doi:10.1109/icpr.2004.1334098

Cited by 24 publications

(12 citation statements)

References 8 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: 71%

“…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%

“…Moreover, it was one of the first works using artificial neural networks for the classification phase, along with texture parameters. Again, [11] used artificial neural networks for classification. This time, brightness and shape descriptors were extracted as pollen features.…”

Section: Related Workmentioning

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: 71%

“…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 1 more Smart Citation

Image Processing for Pollen Classification

Pozo-Baños¹,

Ticay-Rivas²,

Cabrera-Falcón³

et al. 2012

Biodiversity Enrichment in a Diverse World

View full text Add to dashboard Cite

“…The Fourier Descriptor represents the shape of the pollen grain in the frequency domain [10], [16]. Each boundary point is represented as a complex number.…”

Section: Fourier Descriptorsmentioning

confidence: 99%

“…Both techniques were combined in [8] and [9]. In [10] brightness and shape descriptors were used as vector features.…”

Section: Introductionmentioning

confidence: 99%

Pollen Classification Based on Geometrical, Descriptors and Colour Features Using Decorrelation Stretching Method

Ticay-Rivas

Pozo-Baños

Travieso

et al. 2011

IFIP Advances in Information and Communication Technology

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Abstract. Saving earth's biodiversity for future generations is an important global task, where automatic recognition of pollen species by means of computer vision represents a highly prioritized issue. This work focuses on analysis and classification stages. A combination of geometrical measures, Fourier descriptors of morphological details using Discrete Cosine Transform (DCT) in order to select their most significant values, and colour information over decorrelated stretched images are proposed as pollen grains discriminative features. A Multi-Layer neural network was used as classifier applying scores fusion techniques. 17 tropical honey plant species have been classified achieving a mean of 96.49% ± 1.16 of success.

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On Modeling Objects Using Sequence of Moment Invariants

Wiercioch

2018

Computer Information Systems and Industrial Management

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Pollen classification using brightness-based and shape-based descriptors

Cited by 24 publications

References 8 publications

Image Processing for Pollen Classification

Image Processing for Pollen Classification

Pollen Classification Based on Geometrical, Descriptors and Colour Features Using Decorrelation Stretching Method

On Modeling Objects Using Sequence of Moment Invariants

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