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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

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2023

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Vol. 30, no. 1

41--54

EN

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.

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Rgb-D face recognition using LBP-DCT algorithm

Kumar Sunil B L, Kumari Sharmila M

Applied Computer Science

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2021

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Vol. 17, no 3

73--81

EN

Face recognition is one of the applications in image processing that recognizes or checks an individual's identity. 2D images are used to identify the face, but the problem is that this kind of image is very sensitive to changes in lighting and various angles of view. The images captured by 3D camera and stereo camera can also be used for recognition, but fairly long processing times is needed. RGB-D images that Kinect produces are used as a new alternative approach to 3D images. Such cameras cost less and can be used in any situation and any environment. This paper shows the face recognition algorithms’ performance using RGB-D images. These algorithms calculate the descriptor which uses RGB and Depth map faces based on local binary pattern. Those images are also tested for the fusion of LBP and DCT methods. The fusion of LBP and DCT approach produces a recognition rate of 97.5% during the experiment

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Detection of Modic changes in MR images of spine using local binary patterns

Athertya Jiyo S., Saravana Kumar Gurunathan, Govindaraj Jayaraj

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 1

17--29

EN

Background and objective: With increase in prevalence of lower back pain, fast and reliable computer aided methods for clinical diagnosis associated with the same is needed for improving the healthcare reach. The magnetic resonance images exhibit a change in signal intensity on the vertebral body close to end plates, which are termed as Modic changes (MC), and are known to be clear indicators of lower back pain. The current work deals with computer aided methods for automating the classification of signal changes between normal and degenerate cases so as to aid physicians in precise and suitable diagnosis for the ailment. Methods: In order to detect Modic changes in vertebrae, initially the vertebrae are segmented from sagittal MR T1 and T2 imaged using a semi automatic cellular automata based segmentation. This is followed by textural feature extraction using Local Binary Patterns (LBP) and its variants. Various classifiers based on machine learning approaches using Random Forest, kNN, Bayes and SVM were evaluated for its classification performance. Since medical image dataset in general have bias towards healthy and diseased state, data augmentation techniques were also employed. Results: The implemented method is tested and validated over a dataset containing 100 patients. The proposed framework achieves an accuracy of 81% and 91.7% with and without augmentation of data respectively. A comparative study with the state of art methods reported in literature shows that the method proposed in better in terms of computational cost without any compromise on classification accuracy. Conclusion: A novel approach to identify MC in vertebrae by exploiting textural features is proposed. This shall assist radiologists in detecting abnormalities and in treatment planning.

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Formulation and statistical evaluation of an automated algorithm for locating small bowel tumours in wireless capsule endoscopy

Jagadeesan A., Sivaraman J.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 4

782--793

EN

Wireless capsule endoscopy (WCE) is an imaging modality which is highly reliable in the diagnosis of small bowel tumors. But locating the frames carrying tumors manually from the lengthy WCE is cumbersome and time consuming. A simple algorithm for the automated detection of tumorous frames from WCE is proposed in this work. In the proposed algorithm, local binary pattern (LBP) of the contrast enhanced green channel is used as the textural descriptor of the WCE frames. The features employed to differentiate tumorous and nontumorous frames are skewness (S) and kurtosis (K) of the LBP histogram. The threshold value of the features which offers the trade-off between sensitivity and specificity is identified through Receiver Operating Characteristic (ROC) curve analysis. At the optimum threshold, both the features exhibited a sensitivity of 100% and specificity of 90%. The skewness and kurtosis of the LBP computed from the enhanced green channel of tumorous and nontumorous frames differ significantly ( p « 0.05) with a p-value of 2.2 x 10-16. The proposed method is helpful to reduce the time spent by the doctors for reviewing WCE.