6 research outputs found
ΠΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ΅ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠ΅ Π΄Π»Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΠΈ ΠΎΡΠΈΡΡΠΎΠ²ΠΊΠΈ Π°ΡΡ ΠΈΠ²Π½ΡΡ Π΄Π°Π½Π½ΡΡ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΠΉ ΠΏΠΎΠ»ΡΡΠ½ΡΡ ΡΠΈΡΠ½ΠΈΠΉ
One of the main tools for recording auroras is the optical observation of the sky in automatic mode using all-sky cameras. The results of observations are recorded in special mnemonic tables, ascaplots. Ascaplots provide daily information on the presence or absence of cloud cover and auroras in various parts of the sky and are traditionally used to study the daily distribution of auroras in a given spatial region, as well as to calculate the probability of their observation in other regions in accordance with the level of geomagnetic activity. At the same time, the processing of ascaplots is currently carried out manually, which is associated with significant time costs and a high proportion of errors due to the human factor. To increase the efficiency of ascaplot processing, we propose an approach that automates the recognition and digitization of data from optical observations of auroras. A formalization of the ascaplot structure is proposed, which is used to process the ascaplot image, extract the corresponding observation results, and form the resulting data set. The approach involves the use of machine vision algorithms and the use of a specialized mask - a debug image for digitization, which is a color image in which the general position of the ascaplot cells is specified. The proposed approach and the corresponding algorithms are implemented in the form of software that provides recognition and digitization of archival data from optical observations of auroras. The solution is a single-user desktop software that allows the user to convert ascaplot images into tables in batch mode, available for further processing and analysis. The results of the computational experiments have shown that the use of the proposed software will make it possible to avoid errors in the digitization of ascaplots, on the one hand, and significantly increase the speed of the corresponding computational operations, on the other. Taken together, this will improve the efficiency of processing ascaplots and conducting research in the relevant area.ΠΠ΄Π½ΠΈΠΌ ΠΈΠ· ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΎΠ² ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΠΏΠΎΠ»ΡΡΠ½ΡΡ
ΡΠΈΡΠ½ΠΈΠΉ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΠ΅ Π½Π΅Π±ΠΎΡΠ²ΠΎΠ΄Π° Π² Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠ΅ΠΆΠΈΠΌΠ΅ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΊΠ°ΠΌΠ΅Ρ Π²ΡΠ΅Π³ΠΎ Π½Π΅Π±Π°. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΠΉ ΡΠΈΠΊΡΠΈΡΡΡΡΡΡ Π² ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΡ
ΠΌΠ½Π΅ΠΌΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°Π±Π»ΠΈΡΠ°Ρ
, Π°ΡΠΊΠ°ΠΏΠ»ΠΎΡΠ°Ρ
. ΠΡΠΊΠ°ΠΏΠ»ΠΎΡΡ ΠΏΡΠ΅Π΄ΠΎΡΡΠ°Π²Π»ΡΡΡ ΡΡΡΠΎΡΠ½ΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΎ Π½Π°Π»ΠΈΡΠΈΠΈ ΠΈΠ»ΠΈ ΠΎΡΡΡΡΡΡΠ²ΠΈΠΈ ΠΎΠ±Π»Π°ΡΠ½ΠΎΠ³ΠΎ ΠΏΠΎΠΊΡΠΎΠ²Π° ΠΈ ΠΏΠΎΠ»ΡΡΠ½ΡΡ
ΡΠΈΡΠ½ΠΈΠΉ Π² ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠ°ΡΡΡΡ
Π½Π΅Π±ΠΎΡΠ²ΠΎΠ΄Π° ΠΈ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΡΡ Π΄Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΏΠΎΠ»ΡΡΠ½ΡΡ
ΡΠΈΡΠ½ΠΈΠΉ Π² Π·Π°Π΄Π°Π½Π½ΠΎΠΌ ΡΠ΅Π³ΠΈΠΎΠ½Π΅, Π° ΡΠ°ΠΊΠΆΠ΅ Π΄Π»Ρ ΡΠ°ΡΡΠ΅ΡΠ° Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠΈ ΠΈΡ
Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ Π² Π΄ΡΡΠ³ΠΈΡ
ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΡΡΠΎΠ²Π½Π΅ΠΌ Π³Π΅ΠΎΠΌΠ°Π³Π½ΠΈΡΠ½ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ. ΠΠ±ΡΠ°Π±ΠΎΡΠΊΠ° Π°ΡΠΊΠ°ΠΏΠ»ΠΎΡΠΎΠ² Π² Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ Π²ΡΡΡΠ½ΡΡ, ΡΡΠΎ ΡΠΎΠΏΡΡΠΆΠ΅Π½ΠΎ Ρ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠΌΠΈ Π²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠΌΠΈ Π·Π°ΡΡΠ°ΡΠ°ΠΌΠΈ ΠΈ Π²ΡΡΠΎΠΊΠΎΠΉ Π΄ΠΎΠ»Π΅ΠΉ ΠΎΡΠΈΠ±ΠΎΠΊ, Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡΠΈΡ
ΠΏΠΎ ΠΏΡΠΈΡΠΈΠ½Π΅ ΡΠ΅Π»ΠΎΠ²Π΅ΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ°ΠΊΡΠΎΡΠ°. ΠΠ»Ρ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π°ΡΠΊΠ°ΠΏΠ»ΠΎΡΠΎΠ² Π°Π²ΡΠΎΡΠ°ΠΌΠΈ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΠΈΠΉ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΡ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΠΈ ΠΎΡΠΈΡΡΠΎΠ²ΠΊΠΈ Π΄Π°Π½Π½ΡΡ
ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΠΉ ΠΏΠΎΠ»ΡΡΠ½ΡΡ
ΡΠΈΡΠ½ΠΈΠΉ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΡΠΎΡΠΌΠ°Π»ΠΈΠ·Π°ΡΠΈΡ ΡΡΡΡΠΊΡΡΡΡ Π°ΡΠΊΠ°ΠΏΠ»ΠΎΡΠ°, ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΠ°Ρ Π΄Π»Ρ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π΅Π³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΠ΅ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΡ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΠΉ ΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠΈΡΡΡΡΠ΅Π³ΠΎ Π½Π°Π±ΠΎΡΠ° Π΄Π°Π½Π½ΡΡ
. ΠΠΎΠ΄Ρ
ΠΎΠ΄ ΠΏΡΠ΅Π΄ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π·ΡΠ΅Π½ΠΈΡ (Π² ΡΠ°ΡΡΠ½ΠΎΡΡΠΈ, Π² Π΄Π°Π½Π½ΠΎΠΌ ΡΠ»ΡΡΠ°Π΅ ΠΈΠΌΠ΅Π΅Ρ ΠΌΠ΅ΡΡΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΠΎ ΠΏΡΠ°Π²ΠΈΠ»Π°ΠΌ) ΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΌΠ°ΡΠΊΠΈ β ΠΎΡΠ»Π°Π΄ΠΎΡΠ½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π΄Π»Ρ ΠΎΡΠΈΡΡΠΎΠ²ΠΊΠΈ, ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΡΡΠ΅Π³ΠΎ ΡΠΎΠ±ΠΎΠΉ ΡΠ²Π΅ΡΠ½ΠΎΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅, Π² ΠΊΠΎΡΠΎΡΠΎΠΌ Π·Π°Π΄Π°Π½ΠΎ ΠΎΠ±ΡΠ΅Π΅ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΡΡΠ΅Π΅ΠΊ Π°ΡΠΊΠ°ΠΏΠ»ΠΎΡΠΎΠ². ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΈ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ ΡΠ΅Π°Π»ΠΈΠ·ΠΎΠ²Π°Π½Ρ Π² ΡΠΎΡΠΌΠ΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ Π΄Π»Ρ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΠΈ ΠΎΡΠΈΡΡΠΎΠ²ΠΊΠΈ Π°ΡΡ
ΠΈΠ²Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΠΉ ΠΏΠΎΠ»ΡΡΠ½ΡΡ
ΡΠΈΡΠ½ΠΈΠΉ. Π Π΅ΡΠ΅Π½ΠΈΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅Ρ ΡΠΎΠ±ΠΎΠΉ ΠΎΠ΄Π½ΠΎΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΎΠ΅ Π½Π°ΡΡΠΎΠ»ΡΠ½ΠΎΠ΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ΅ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠ΅, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅Π΅ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ Π² ΠΏΠ°ΠΊΠ΅ΡΠ½ΠΎΠΌ ΡΠ΅ΠΆΠΈΠΌΠ΅ Π²ΡΠΏΠΎΠ»Π½ΡΡΡ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π°ΡΠΊΠ°ΠΏΠ»ΠΎΡΠΎΠ² Π² ΡΠ°Π±Π»ΠΈΡΡ, Π΄ΠΎΡΡΡΠΏΠ½ΡΠ΅ Π΄Π»Ρ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠ΅ΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ Π°Π½Π°Π»ΠΈΠ·Π°. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΡΡ
Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΎΠ² ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΡ ΠΈΠ·Π±Π΅ΠΆΠ°ΡΡ ΠΎΡΠΈΠ±ΠΎΠΊ ΠΏΡΠΈ ΠΎΡΠΈΡΡΠΎΠ²ΠΊΠ΅ Π°ΡΠΊΠ°ΠΏΠ»ΠΎΡΠΎΠ², Ρ ΠΎΠ΄Π½ΠΎΠΉ ΡΡΠΎΡΠΎΠ½Ρ, ΠΈ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎ ΠΏΠΎΠ²ΡΡΠΈΡΡ ΡΠΊΠΎΡΠΎΡΡΡ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΡ
Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ, Ρ Π΄ΡΡΠ³ΠΎΠΉ. Π ΡΠΎΠ²ΠΎΠΊΡΠΏΠ½ΠΎΡΡΠΈ ΡΡΠΎ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΡ ΠΏΠΎΠ²ΡΡΠΈΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π°ΡΠΊΠ°ΠΏΠ»ΠΎΡΠΎΠ² ΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠ΅ΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ
Robust Table Detection and Structure Recognition from Heterogeneous Document Images
We introduce a new table detection and structure recognition approach named
RobusTabNet to detect the boundaries of tables and reconstruct the cellular
structure of each table from heterogeneous document images. For table
detection, we propose to use CornerNet as a new region proposal network to
generate higher quality table proposals for Faster R-CNN, which has
significantly improved the localization accuracy of Faster R-CNN for table
detection. Consequently, our table detection approach achieves state-of-the-art
performance on three public table detection benchmarks, namely cTDaR TrackA,
PubLayNet and IIIT-AR-13K, by only using a lightweight ResNet-18 backbone
network. Furthermore, we propose a new split-and-merge based table structure
recognition approach, in which a novel spatial CNN based separation line
prediction module is proposed to split each detected table into a grid of
cells, and a Grid CNN based cell merging module is applied to recover the
spanning cells. As the spatial CNN module can effectively propagate contextual
information across the whole table image, our table structure recognizer can
robustly recognize tables with large blank spaces and geometrically distorted
(even curved) tables. Thanks to these two techniques, our table structure
recognition approach achieves state-of-the-art performance on three public
benchmarks, including SciTSR, PubTabNet and cTDaR TrackB2-Modern. Moreover, we
have further demonstrated the advantages of our approach in recognizing tables
with complex structures, large blank spaces, as well as geometrically distorted
or even curved shapes on a more challenging in-house dataset.Comment: Accepted by Pattern Recognition on 27 Aug. 202
Improving data management through automatic information extraction model in ontology for road asset management
lRoads are a critical component of transportation infrastructure, and their effective maintenance is paramount in ensuring their continued functionality and safety. This research proposes a novel information management approach based on state-of-the-art deep learning models and ontologies. The approach can automatically extract, integrate, complete, and search for project knowledge buried in unstructured text documents. The approach on the one hand facilitates implementation of modern management approaches, i.e., advanced working packaging to delivery success road management projects, on the other hand improves information management practices in the construction industry