Enhancement of Degraded Manuscript Images using Adaptive Gaussian Thresholding

Khetan, Nikunj; Kejriwal, Lakshya; Indu, S.

doi:10.14257/ijfgcn.2017.10.1.05

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…The smoothed image is then binarized using a local approach called adaptive Gaussian threshold [7] that has two parameters: a block size (BS) that controls the size of the area around each pixels that will be used to calculate that binarization threshold; and the C parameter that is subtracted from the weighted mean values around a pixel and is used to decide if this pixel will be changed to black or to white. After binarization, the images are processed using the opening and closing morphological operators to filter out small artifacts that are not linked to the root and to close gaps that may appear in the root image during processing or image capture.…”

Section: Methodsmentioning

confidence: 99%

Measuring the Root Length of Peanuts Grown in Rhizotrons Using Computer Vision

Stefanello

Silva

Dayrell

et al. 2021

Anais Do XVII Workshop De Visão Computacional (WVC 2021)

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Peanut is one of the most grown leguminous crops in the world, but it can suffer during water deficit periods. In this paper, a new method to help monitoring root growth for laboratory experiments with this plant is proposed. By using a new combination of smoothing, thresholding, morphological filtering and skeletonization, our method has achieved a correlation of 0.968 with the Tennant's standard approach.

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Section: Methodsmentioning

confidence: 99%

Measuring the Root Length of Peanuts Grown in Rhizotrons Using Computer Vision

Stefanello

Silva

Dayrell

et al. 2021

Anais Do XVII Workshop De Visão Computacional (WVC 2021)

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“…In this study, the adaptive Gaussian thresholding method [17] is used, which considers the spatial distance between pixels like Gaussian filtering and applies weighted thresholds with weights from Equation (3). As shown in Equations ( 4) and ( 5), each pixel is binarized based on its corresponding threshold value.…”

Section: ) Adaptive Gaussian Thresholdingmentioning

confidence: 99%

Detection of Tooth Position by YOLOv4 and Various Dental Problems Based on CNN With Bitewing Radiograph

Li,

Mao,

Lin

et al. 2024

IEEE Access

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Periodontitis is a high prevalence dental disease caused by bacterial infection of the bone that surrounds the tooth. Early detection and precision treatment can prevent more severe symptoms such as tooth loss. Traditionally, periodontal disease is identified and labeled manually by dental professionals. The task requires expertise and extensive experience, and it is highly repetitive and time-consuming. The aim of this study is to explore the application of AI in the field of dental medicine. With the inherent learning capabilities, AI exhibits remarkable proficiency in processing extensive datasets and effectively managing repetitive tasks. This is particularly advantageous in professions demanding extensive experiential knowledge, such as dentistry. By harnessing AI, the potential arises to amplify process efficiency and velocity. In this study, bitewing radiographs are used as the image source, and there are two major steps to detect the dental symptoms including (1) tooth position identification, and (2) symptom identification. The study combines image enhancement techniques and tooth position identification using Gaussian filtering and adaptive binarization for data preprocessing, facilitated by the YOLOv4 model to precisely mark tooth positions. The subsequent step enhances symptom area visibility via contrast enhancement, utilizing a CNN model, particularly the AlexNet model, with significant improvements in caries recognition accuracy (92.85%) and restorations recognition accuracy (96.55%) compared to prior research. Moreover, the inclusion of periodontal disease symptoms achieves an accuracy of 91.13%. By harnessing deep learning techniques based on CNN models, this research enhances diagnostic precision, reduces errors, and increases efficiency for dentists, thereby providing meticulous and swift patient care. This innovation not only saves time but also has the potential for widespread implementation in remote and preventive medicine, aligning with the aspiration of universal health care accessibility.

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