Roman V. Shapovalov

student, researching programmer    /    Russian Federation, Москва

An up-and-coming student

My scientific interests

Publications

О миссии языковых разделов Википедии, отличных от английского

Роман Шаповалов, Анна Малютина
В Википедии имеется более 260 языковых разделов. Многие участники владеют несколькоми языками. В связи с этим актуален вопрос, как разделить их усилия, чтобы получить максимальную выгоду для всего проекта.

Автоматическая сегментация облаков точек на основе элементов поверхности

А. Велижев, Р. Шаповалов, Д. Потапов, Е. Третьяк, А. Конушин
Алгоритмы сегментации результатов лазерного сканирования, работающие с отдельными точками, чувствительны к шуму и требуют значительных вычислительных ресурсов. В последние годы были предложены алгоритмы сегментации, основанные на использовании иерархических деревьев, таких как kd- или окто-...

Efficient road mapping via interactive image segmentation

O. Barinova, R. Shapovalov, S. Sudakov, A. Velizhev, A. Konushin
Last years witnessed the growth of demand for road monitoring systems based on image or video analysis. These systems usually consist of a survey vehicle equipped with photo and video cameras, laser scanners and other instruments. Sensors mounted on the van collect different types of data while...
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Professional Experience

Research projects

GML BOLT

Balanced On-line Learning Toolkit is an open-source library that contains a set of on-line classifier interfaces and their implementations. On-line learning (also called data stream mining) is the task of learning from streaming data. It means that a classifier should be always able to classify some data, even if learning process has not been finished yet. Moreover, the time of single example...

GML LidarK

We develop the LidarK library. It is the open-source library which allows performing different operations on multidimensional point data such as 3D LIDAR scans. Currently it allows building a spatial index. Though it is intended to be used on LIDAR scans it can be applied to a wide range of problems which require spatial data processing.

Road monitoring

We develop an interactive semantic segmentation system which efficiently analyzes road pavement photos. First, a user gets a piece of an image and marks road marking and road defects on it, then the classifier is being learned. The next image part is pretended to be classified, so the user should correct classification errors. After this correction the classifier is learned again. While more...

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