• The Journal of the Korea Contents Association
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• v.11 no.6
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• pp.18-25
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• 2011

Salient object attracts more and more attention from researchers due to its important role in many fields of multimedia processing like tracking, segmentation, adaptive compression, and content-base image retrieval. Usually, a saliency map is binarized into black and white map, which is considered as the binary mask of the salient object in the image. Still, the threshold is heuristically chosen or parametrically controlled. This paper suggests using the global optimal threshold to perform saliency map thresholding. This work also considers the usage of multi-level optimal thresholds and the local adaptive thresholds in the experiments. These experimental results show that using global optimal threshold method is better than parametric controlled or local adaptive threshold method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.163-174
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• 2004

The 3D tooth model in which each tooth can be manipulated individualy is essential component for the orthodontic simulation and implant simulation in dental field. For the reconstruction of such a tooth model, we need an image segmentation algorithm capable of separating individual tooth from neighboring teeth and alveolar bone. In this paper we propose a CT image normalization method and adaptive optimal thresholding algorithm for the segmenation of tooth region in CT image slices. The proposed segmentation algorithm is based on the fact that the shape and intensity of tooth change gradually among CT image slices. It generates temporary boundary of a tooth by using the threshold value estimated in the previous imge slice, and compute histograms for the inner region and the outer region seperated by the temporary boundary. The optimal threshold value generating the finnal tooth region is computed based on these two histogram.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.133-140
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• 2004

Recently, the dental information systems were rapidly developed in order to store and process the data of patients. But, these systems should serve a doctor a good quality information against disease for diagnostic and surgery purpose so as to success in this field. This function of the system it important to persuade patients to undergo proper surgical operation they needed. Hence, 3D teeth model capable of simulating the dental surgery and treatment is necessary Teeth manipulation of dentistry is performed on individual tooth in dental clinic. io, 3D teeth reconstruction system should have the techniques of segmentation and 3D reconstruction adequate for individual tooth. In this paper, we propose the techniques of adaptive optimal segmentation to segment the individual area of tooth, and reconstruction method of tooth based on contour-based method. Each tooth can be segmented from neighboring teeth and alveolar bone in CT images using adaptive optimal threshold computed differently on tooth. Reconstruction of individual tooth using results of segmentation can be manipulated according to user's input and make the simulation of dental surgery and treatment possible.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.738-744
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• 2016

In this paper, a robust visual odometry system has been proposed and implemented in an environment with dynamic illumination. Visual odometry is based on stereo images to estimate the distance to an object. It is very difficult to realize a highly accurate and stable estimation because image quality is highly dependent on the illumination, which is a major disadvantage of visual odometry. Therefore, in order to solve the problem of low performance during the feature detection phase that is caused by illumination variations, it is suggested to determine an optimal threshold value in the image binarization and to use an adaptive threshold value for feature detection. A feature point direction and a magnitude of the motion vector that is not uniform are utilized as the features. The performance of feature detection has been improved by the RANSAC algorithm. As a result, the position of a mobile robot has been estimated using the feature points. The experimental results demonstrated that the proposed approach has superior performance against illumination variations.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.12
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• pp.51-57
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• 1999

In this paper, an adaptive thresholding of the nonuniformly contrasted images is proposed through using the contrast pre-enhancement of the local regions and the bilinear interpolation between the local threshold values. The nonuniformly contrasted image is decomposed into 9${\times}$9 sized local regions, and the contrast is enhanced by intensifying the gray level difference of each low contrasted or blurred region. Optimal threshold values are obtained by iterative method from the gray level distribution of each contrast-enhanced local region. Discontinuities are reduced at the region of interest or at the characters by using bilinear interpolation between the neighboring threshold surfaces. Character recognition experiments are conducted using backpropagation neural network on the characters extracted from the nonuniformly contrasted document, PCB, and wafer images binarized through using the proposed thresholding and the conventional thresholding methods, and the results prove the relative effectiveness of the proposed scheme.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.51-61
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• 2004

In the dental field, the 3D tooth model in which each tooth can be manipulated individually is an essential component for the simulation of orthodontic surgery and treatment. To reconstruct such a tooth model from CT slices, we need to define the accurate boundary of each tooth from CT slices. However, the global threshold method, which is commonly used in most existing 3D reconstruction systems, is not effective for the tooth segmentation in the CT image. In tooth CT slices, some teeth touch with other teeth and some are located inside of alveolar bone whose intensity is similar to that of teeth. In this paper, we propose an image segmentation algorithm based on B-spline curve fitting to produce smooth tooth regions from such CT slices. The proposed algorithm prevents the malfitting problem of the B-spline algorithm by providing accurate initial tooth boundary for the fitting process. This paper proposes an optimal threshold scheme using the intensity and shape information passed by previous slice for the initial boundary generation and an efficient B-spline fitting method based on genetic algorithm. The test result shows that the proposed method detects contour of the individual tooth successfully and can produce a smooth and accurate 3D tooth model for the simulation of orthodontic surgery and treatment.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.35-41
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• 2018

In this paper, we propose a swarm-based possibilistic c-means(PCM) algorithm in order to overcome the problems of PCM, which are sensitiveness of clustering performance due to initial cluster center's values and producing coincident or close clusters. To settle the former problem of PCM, we adopt a swam-based global optimization method which can be provided the optimal initial cluster centers. Furthermore, to settle the latter problem of PCM, we design an adaptive thresholding model based on the optimized cluster centers that yields preliminary clustered and un-clustered dataset. The preliminary clustered dataset plays a role of preventing coincident or close clusters and the un-clustered dataset is lastly clustered by PCM. From the experiment, the proposed method obtains a better performance than other PCM algorithms on a simulated magnetic resonance(MR) brain image dataset which is corrupted by various noises and bias-fields.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.145-150
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• 2008

This thesis explores the problem of reconstructing a three-dimensional(3D) scene given a set of images or image sequences of the scene. It describes efficient methods for the 3D reconstruction of static and dynamic scenes from stereo images, stereo image sequences, and images captured from multiple viewpoints. Novel methods for image-based and volumetric modelling approaches to 3D reconstruction are presented, with an emphasis on the development of efficient algorithm which produce high quality and accurate reconstructions. For image-based 3D reconstruction a novel energy minimisation scheme, Iterated Dynamic Programming, is presented for the efficient computation of strong local minima of discontinuity preserving energyy functions. Coupled with a novel morphological decomposition method and subregioning schemes for the efficient computation of a narrowband matching cost volume. the minimisation framework is applied to solve problems in stereo matching, stereo-temporal reconstruction, motion estimation, 2D image registration and 3D image registration. This thesis establishes Iterated Dynamic Programming as an efficient and effective energy minimisation scheme suitable for computer vision problems which involve finding correspondences across images. For 3D reconstruction from multiple view images with arbitrary camera placement, a novel volumetric modelling technique, Embedded Voxel Colouring, is presented that efficiently embeds all reconstructions of a 3D scene into a single output in a single scan of the volumetric space under exact visibility. An adaptive thresholding framework is also introduced for the computation of the optimal set of thresholds to obtain high quality 3D reconstructions. This thesis establishes the Embedded Voxel Colouring framework as a fast, efficient and effective method for 3D reconstruction from multiple view images.

• Journal of International Society for Simulation Surgery
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• v.1 no.1
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• pp.37-40
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• 2014

Purpose For living donor liver transplantation, liver segmentation is difficult due to the variability of its shape across patients and similarity of the density of neighbor organs such as heart, stomach, kidney, and spleen. In this paper, we propose an automatic segmentation of the liver using multi-planar anatomy and deformable surface model in portal phase of abdominal contrast-enhanced CT images. Method Our method is composed of four main steps. First, the optimal liver volume is extracted by positional information of pelvis and rib and by separating lungs and heart from CT images. Second, anisotropic diffusing filtering and adaptive thresholding are used to segment the initial liver volume. Third, morphological opening and connected component labeling are applied to multiple planes for removing neighbor organs. Finally, deformable surface model and probability summation map are performed to refine a posterior liver surface and missing left robe in previous step. Results All experimental datasets were acquired on ten living donors using a SIEMENS CT system. Each image had a matrix size of $512{\times}512$ pixels with in-plane resolutions ranging from 0.54 to 0.70 mm. The slice spacing was 2.0 mm and the number of images per scan ranged from 136 to 229. For accuracy evaluation, the average symmetric surface distance (ASD) and the volume overlap error (VE) between automatic segmentation and manual segmentation by two radiologists are calculated. The ASD was $0.26{\pm}0.12mm$ for manual1 versus automatic and $0.24{\pm}0.09mm$ for manual2 versus automatic while that of inter-radiologists was $0.23{\pm}0.05mm$. The VE was $0.86{\pm}0.45%$ for manual1 versus automatic and $0.73{\pm}0.33%$ for manaual2 versus automatic while that of inter-radiologist was $0.76{\pm}0.21%$. Conclusion Our method can be used for the liver volumetry for the pre-surgery planning of living donor liver transplantation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.697-705
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• 2014

This paper presents a vision based object width measurement method and its application where the light sectioning method is employed. The target object for measurement is a tread, which is the most outside component of an automobile tire. The entire system applying the measurement method consists of two processes, i.e. a calibration process and a detection process. The calibration process is to identify the relationships between a camera plane and a laser plane, and to estimate a camera lens distortion parameters. As the process requires a test pattern, namely a jig, which is elaborately manufactured. In the detection process, first of all, the region that a laser light illuminates is extracted by applying an adaptive thresholding technique where the distribution of the pixel brightness is considered to decide the optimal threshold. Then, a thinning algorithm is applied to the region so that the ends and the shoulders of a tread are detected. Finally, the tread width and the shoulder width are computed using the homography and the distortion coefficients obtained by the calibration process.