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Pollen Grain Recognition Using Convolutional Neural Network
P. 409–414.
This paper addresses two problems: the automated pollen
species recognition and counting them on an image obtained with a lighting
microscope. Automation of pollen recognition is required in several
domains, including allergy and asthma prevention in medicine and honey
quality control in the nutrition industry. We propose a deep learning solution
based on a convolutional neural network for classification, feature
extraction and image segmentation. Our approach achieves state-of-theart
results in terms of accuracy. For 5 species, the approach provides 99.8%
of accuracy, for 11 species — 95.9%.
Пузаченко Ю. Г., Sandlerskiy R., Krenke A. et al., Russian Journal of Forest Science 2014 Vol. 7 No. 7 P. 838–854
The article proposes approaches to the use of multispectral remote information in basic research on the spatiotemporal organization of biogeocenotic cover with and without the use of ground field measurements. It is postulated that remote measurements reflect the biophysical condition of biogeocenotic cover defined by the absorption and conversion of solar energy and can be ...
Added: September 3, 2023
A. N. Krenke, R. B. Sandlersky, A. S. Baybar et al., Известия РАН. Серия биологическая. 2023 Vol. 50 No. 1 P. S85–S99
Four main models of the appearance of boundaries (in a particular case, integrity), arising from the theory of nonlinear dynamic systems, are considered briefly. On the basis of Kotelnikov’s fundamental sampling theorem and, accordingly, general information theory, the character of a distinguished boundary as a function of the sampling frequency in a spatial series with a ...
Added: December 2, 2022
Voynov A., Morozov S., Babenko A., , in: Proceedings of the 38th International Conference on Machine Learning (ICML 2021)Vol. 139.: PMLR, 2021. P. 10596–10606.
Added: December 27, 2021
Switzerland: Springer, 2019.
This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Data Processing, IDP 2016, held in Barcelona, Spain, in October 2016.
The 11 revised full papers were carefully reviewed and selected from 52 submissions. The papers of this volume are organized in topical sections on machine learning theory with applications; intelligent data processing in life ...
Added: February 8, 2020
Springer, 2019.
This 2-volume set constitutes the refereed proceedings of the 9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019, held in Madrid, Spain, in July 2019.
The 99 papers in these volumes were carefully reviewed and selected from 137 submissions. They are organized in topical sections named:
Part I: best ranked papers; machine learning; pattern recognition; ...
Added: September 23, 2019
Cham: Springer, 2018.
The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018. The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; ...
Added: October 31, 2018
Springer, 2018.
The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.
The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; ...
Added: October 30, 2018
Osokin A., Kohli P., , in: Lecture Notes in Computer Science. Proceedings of the 13th European Conference on Computer Vision (ECCV 2014)* 2. Vol. 8690.: Zürich: Springer, 2014. P. 663–678.
Interactive image segmentation is an important computer vision problem that has numerous real world applications. Models for image segmentation are generally trained to minimize the Hamming error in pixel labeling. The Hamming loss does not ensure that the topology/structure of the object being segmented is preserved and therefore is not a strong indicator of the ...
Added: October 19, 2017
Kohli P., Osokin A., Jegelka S., , in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013).: Portland: IEEE, 2013. P. 1971–1978.
We discuss a model for image segmentation that is able to overcome the short-boundary bias observed in standard pairwise random field based approaches. To wit, we show that a random field with multi-layered hidden units can encode boundary preserving higher order potentials such as the ones used in the cooperative cuts model of [11] while ...
Added: October 19, 2017
Kirillov A., Gavrikov M., Lobacheva E. et al., , in: Proceedings of the 27th British Machine Vision Conference.: -, 2016. P. 1–12.
The Shape Boltzmann Machine (SBM) and its multilabel version MSBM have been recently introduced as deep generative models that capture the variations of an object shape. While being more flexible MSBM requires datasets with labeled parts of the objects for training. In the paper we present an algorithm for training MSBM using binary masks of ...
Added: February 24, 2017
Lobacheva E., Veksler O., Boykov Y., , in: Proceedings of the 2015 IEEE International Conference on Computer Vision.: Los Alamitos, Washington, Tokyo: IEEE Computer Society, 2015. P. 1626–1634.
Binary energy optimization is a popular approach for segmenting an image into foreground/background regions. To model region appearance, color, a relatively high dimensional feature, should be handled effectively. A full color histogram is usually too sparse to be reliable. One approach is to reduce dimensionality by color space clustering. Another popular approach is to fit ...
Added: October 1, 2015
Кириллов А. Н., Гавриков М. И., Lobacheva E. et al., Интеллектуальные системы. Теория и приложения 2015 Т. 19 № 2 С. 75–95
In this paper we consider the Shape Boltzmann Machine(SBM) and its multi-label version MSBM. We present an algorithm for training MSBM using only binary masks of objects and the seeds which approximately correspond to the locations of objects parts. ...
Added: September 30, 2015
Vetrov D., Voronin P., Pattern Recognition and Image Analysis 2013 Vol. 23 No. 2 P. 335–339
Added: July 12, 2014
Nekrasov K., Laptev D., Vetrov D., Pattern Recognition and Image Analysis 2013 Vol. 23 No. 1 P. 1–6
Added: July 12, 2014
Shapovalov R. V., Vetrov D., Kohli P., IEEE, 2013.
Added: July 12, 2014
Yangel B. K., Vetrov D., Lecture Notes in Computer Science 2013 Vol. 8081 P. 137–150
In the paper we address a challenging problem of incorporating preferences on possible shapes of an object in a binary image segmentation framework. We extend the well-known conditional random fields model by adding new variables that are responsible for the shape of an object. We describe the shape via a flexible graph augmented with vertex ...
Added: July 12, 2014
Borisenko B., Вестник Казанского технологического университета 2013 № 4 С. 286–291
Parameters that affect the perception quality of visual data has been investigated. Evaluation of such parameters due to distortion during filtering was determined. Segmentation methods according to colour and brightness similarity were discussed. Perceptive model for contrast sensitivity influence evaluation was discussed. The image region detection method for watermarking is suggested. ...
Added: March 15, 2013