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Hybrid texture and gradient modeling for dynamic background subtraction identification systemin tobacco plant using 5G data service

Gowda Thirthe M.T., Chandrika J.

Computer Assisted Methods in Engineering and Science

2023

Vol. 30, no. 1

41--54

Background: Detecting the plants as objects of interest in any vision-based input sequence is highly complex due to nonlinear background objects such as rocks, shadows,etc. Therefore, it is a difficult task and an emerging one with the development of precision agriculture systems. The nonlinear variations of pixel intensity with illuminationand other causes such as blurs and poor video quality also make the object detection taskchallenging. To detect the object of interest, background subtraction (BS) is widely usedin many plant disease identification systems, and its detection rate largely depends on thenumber of features used to suppress and isolate the foreground region and its sensitivitytoward image nonlinearity. Methodology: A hybrid invariant texture and color gradient-based approach is proposed to model the background for dynamic BS, and its performance is validated byvarious real-time video captures covering different kinds of complex backgrounds and various illumination changes. Based on the experimental results, a simple multimodal featureattribute, which includes several invariant texture measures and color attributes, yieldsfinite precision accuracy compared with other state-of-art detection methods. Experimental evaluation of two datasets shows that the new model achieves superior performanceover existing results in spectral-domain disease identification model. 5G assistance: After successful identification of tobacco plant and its analysis, the finalresults are stored in a cloud-assisted server as a database that allows all kinds of 5G servicessuch as IoT and edge computing terminals for data access with valid authentication fordetailed analysis and references.

Usuwanie tła w wideo nagraniach pochodzących z monitorowania basenu pływackiego

Reiter K., Kowalczuk Z.

Pomiary Automatyka Robotyka

2018

R. 22, nr 3

15--22

Automatyczna obróbka obrazu w czasie rzeczywistym jest kluczowa dla wielu rozwiązań monitoringu wykorzystywanych m.in. w celach bezpieczeństwa. Często jednym z ważniejszych etapów obróbki jest oddzielenie tła od obiektów na pierwszym planie, tak aby wykluczyć wszystkie nieistotne informacje z obrazu. Celem pracy jest podsumowanie doświadczenia zdobytego podczas śledzenia pływaków oraz pokazanie możliwości skutecznego automatycznego nadzoru wideo osób korzystających z basenu. Porównano skuteczność działania dwóch wybranych algorytmów (MOG i KNN) przy użyciu różnych odwzorowań kolorów oraz omówiono zalety i wady analizowanych metod.

Automatic real-time image processing is crucial for many (video surveillance) monitoring solutions used, among others for security purposes. Often one of the most important stages of computer vision processing is separating the background from the objects in the foreground, so as to exclude all irrelevant information from the image. The aim of this work is to summarize the experience gained while tracking swimmers and to show the possibility of effective automatic video surveillance of people using a swimming pool. The effectiveness of two selected algorithms (MOG and KNN) is compared using different color mappings and the advantages and disadvantages of the analyzed methods are discussed.