• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.610-616
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• 2012

In this paper, we present a method to detect an object such as ship, rock and buoy from sea IR image for the safety navigation. To this end, we do the image smoothing first and the apply watershed algorithm to segment image into subregions. Since watershed algorithm almost always produces over-segmented regions, it requires posterior merging process to get meaningful segmented regions. We propose an efficient merger algorithm that requires only two times of direct access to the pixels regardless of the number of regions. Also by analyzing IR image obtained from sea environments, we could find out that most horizontal edge come out from object regions. For the given input IR image we extract horizontal edge and eliminate isolated edges produced from background and noises by adopting morphological operator. Among the segmented regions, the regions that have horizontal edges are extracted as final results. Experimental results show the adequacy of the proposed method.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.23-28
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• 2017

Shape from focus (SFF) is a technique used to reconstruct 3D shape of objects from a sequence of images obtained at different focus settings of the lens. In this paper, a new shape from focus method for 3D reconstruction of microscopic objects is described, which is based on gradient operator in Mathematical Morphology. Conventionally, in SFF methods, a single focus measure is used for measuring the focus quality. Due to the complex shape and texture of microscopic objects, single measure based operators are not sufficient, so we propose morphological operators with multi-structuring elements for computing the focus values. Finally, an optimal focus measure is obtained by combining the response of all focus measures. The experimental results showed that the proposed algorithm has provided more accurate depth maps than the existing methods in terms of three-dimensional shape recovery.

• Imaging Science in Dentistry
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• v.51 no.3
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• pp.237-242
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• 2021

Purpose: The aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs. Materials and Methods: In order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords(deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed. Results: When dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects. Conclusion: Clinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.

• Journal of Periodontal and Implant Science
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• v.52 no.1
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• pp.54-64
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• 2022

Purpose: This in vitro study was conducted to evaluate the effects of different debridement techniques and conditioning procedures on root surface morphology and blood clot stabilization. Methods: Two debridement techniques (curette [CU] vs. high-speed ultrasound [US]) and 2 conditioning procedures (ethylenediaminetetraacetic acid [EDTA] and phosphoric acid [PA]) were used for the study. Seven experimental groups were tested on root surfaces: 1) no treatment (C); 2) CU; 3) US; 4) CU+EDTA; 5) US+EDTA; 6) CU+PA; and 7) US+PA. Three specimens per group were observed under scanning electron microscopy (SEM) for surface characterization. Additional root slices received a blood drop, and clot formation was graded according to the blood element adhesion index by a single operator. Data were statistically analyzed, using a threshold of P<0.05 for statistical significance. Results: The C group displayed the most irregular surface among the tested groups with the complete absence of blood traces. The highest frequency of blood component adhesion was shown in the CU+EDTA group (P<0.05), while no differences were detected between the CU, US+EDTA, and CU+PA groups (P<0.05), which performed better than the US and US+PA groups (P<0.05). Conclusions: In this SEM analysis, EDTA and conventional manual scaling were the most efficient procedures for enhancing smear layer removal, collagen fiber exposure, and clot stabilization on the root surface. This technique is imperative in periodontal healing and regenerative procedures.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.135-142
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• 2013

Contour detection is important for many computer vision applications, such as shape discrimination and object recognition. In many cases, local luminance changes turn out to be stronger in textured areas than on object contours. Therefore, local edge features, which only look at a small neighborhood of each pixel, cannot be reliable indicators of the presence of a contour, and some global analysis is needed. The novelty of this operator is that dilation is limited to Deluanary triangular. An efficient implementation is presented. The grouping algorithm is then embedded in a multi-threshold contour detector. At each threshold level, small groups of edges are removed, and contours are completed by means of a generalized reconstruction from markers. Both qualitative and quantitative comparison with existing approaches prove the superiority of the proposed contour detector in terms of larger amount of suppressed texture and more effective detection of low-contrast contour.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.2
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• pp.50-59
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• 2000

In this paper, a new preprocessing method is proposed to speedup the watershed-based image segmentation In the proposed method, the gradient correction values of ramp edges are calculated from the positions and width of the ramp edges using Laplacian operator, and then, unlike the conventional method in which the monoscale or multi scale gradient image is directly used as a reference iImage, the reference image is obtained by adding the threshold value to each position of the ramp edges in the monoscale gradient image And the marker image is reconstructed on the reference image by erosion By preprocessing the image for the watershed transformation in such a manner, we can reduce the oversegmentations far more than those of applying the conventional morphological filter to the simple monoscale or multiscale gradient-based reference image Thus, we can reduce the total image segmentation time by reducing the time of postprocessing of region merging, which consumes most of the processing time In the watershed-based image segmentation, Experimental results indicate that the proposed method can speedup the total image segmentation about twice than those of the conventional methods, without the loss of ramp edges and principal edges around the dense-edge region.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.10
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• pp.6019-6023
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• 2013

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.10
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• pp.520-529
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• 2005

A feature-based 3D modeling algorithm is presented in this paper. Since conventional methods use depth-based techniques, they need much time for the image matching to extract depth information. Even feature-based methods have less computation load than that of depth-based ones, the calculation of modeling error about whole pixels within a triangle is needed in feature-based algorithms. It also increase the computation time. Therefore, the proposed algorithm consists of three phases, which are an initial 3D model generation, model evaluation, and model refinement phases, in order to acquire an efficient 3D model. Intensity gradients and incremental Delaunay triangulation are used in the Initial model generation. In this phase, a morphological edge operator is adopted for a fast edge filtering, and the incremental Delaunay triangulation is modified to decrease the computation time by avoiding the calculation errors of whole pixels and selecting a vertex at the near of the centroid within the previous triangle. After the model generation, sparse vertices are matched, then the faces are evaluated with the size, approximation error, and disparity fluctuation of the face in evaluation stage. Thereafter, the faces which have a large error are selectively refined into smaller faces. Experimental results showed that the proposed algorithm could acquire an adaptive model with less modeling errors for both smooth and abrupt areas and could remarkably reduce the model acquisition time.

• Journal of Broadcast Engineering
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• v.15 no.4
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• pp.474-486
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• 2010

In this paper, we propose a new method for detection moving object contour using spatial and temporal edge. In general, contour pixels of the moving object are likely present around pixels with high gradient value along the time axis and the spatial axis. Therefore, we can detect the contour of the moving objects by finding pixels which have high gradient value in the time axis and spatial axis. In this paper, we introduce a new computation method, termed as temporal edge, to compute an gradient value along the time axis for any pixel on an image. The temporal edge can be computed using two input gray images at time t and t-2 using the Sobel operator. Temporal edge is utilized to detect a candidate region of the moving object contour and then the detected candidate region is used to extract spatial edge information. The final contour of the moving object is detected using the combination of these two edge information, which are temporal edge and spatial edge, and then the post processing such as a morphological operation and a background edge removing procedure are applied to remove noise regions. The complexity of the proposed method is very low because it dose not use any background scene and high complex operation, therefore it can be applied to real-time applications. Experimental results show that the proposed method outperforms the conventional contour extraction methods in term of processing effort and a ghost effect which is occurred in the case of entropy method.

• The Korean Journal of Mycology
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• v.46 no.4
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• pp.479-490
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• 2018

Red pepper is seriously damaged by thrips (Thrips palmi) and anthracnose caused by Colletotrichum acutatum throughout its development. Because of biotic constraints, producers often depend on chemicals that are expensive and have adverse effects on the environment, operator, and beneficial insects. In addition, resistance is developed because of the repeated use of chemicals. In recent decades, the use of microorganisms in crop protection has become a credible alternative because it is eco-friendly. In this study, we aimed to select isolates with insecticidal and fungicidal activities against the pathogens that cause anthracnose and thrips. We treated T. palmi adults and juveniles with 13 strains of entomopathogenic fungi (isolated from the soil by using the insect-bait method), and 6 strains showed excellent insecticidal activity (70-100%) 5 days after the treatment. The selected isolates were cultured with C. acutatum to screen for the strain with excellent anti-fungal activities, among which an isolate FT333 showed more than 95% control efficacy against C. acutatum in vitro. The isolate was identified as Isaria javanica through its morphological characteristics and phylogenetic analysis of the ITS and ${\beta}-tubulin$ nucleotide sequences. The Isaria javanica FT333 isolate could be used effectively for dual bio-control of thrips and anthracnose during red pepper cultivation.