• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1418-1433
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• 2019

In computer graphics, 3D mesh segmentation is a challenging research field. This paper presents a 3D mesh model segmentation algorithm that focuses on removing exterior salient parts from the original 3D mesh model based on prominent feature points and marching plane. To begin with, the proposed approach uses multi-dimensional scaling to extract prominent feature points that reside on the tips of each exterior salient part of a given mesh. Subsequently, a set of planes intersect the 3D mesh; one is the marching plane, which start marching from prominent feature points. Through the marching process, local cross sections between marching plane and 3D mesh are extracted, subsequently, its corresponding area are calculated to represent local volumes of the 3D mesh model. As the boundary region of an exterior salient part generally lies on the location at which the local volume suddenly changes greatly, we can simply cut this location with the marching plane to separate this part from the mesh. We evaluated our algorithm on the Princeton Segmentation Benchmark, and the evaluation results show that our algorithm works well for some categories.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.77-83
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• 1998

Generally a moving picture suffers from motion blur, due to relative motion between moving objects and the image formation system. The purpose of this paper is to propose teh model for the motion blur and the restoration method using the regularized iterative technique. In the proposed model, the boundary effect between moving objects and background is analyzed mathematically to overcome the limit of the spatially invariant model. And we present the motion-based image segmentation technique for the object-based image restoration, which is the modified version of the conventional segmentation method. Based on the proposed model, the restoration technique removes the motion blur by using the estimated motion parameter from the result of the segmentation.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.143-148
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• 2022

Brain tumor classification is an important process that allows doctors to plan treatment for patients based on the stages of the tumor. To improve classification performance, various CNN-based architectures are used for brain tumor classification. Existing methods for brain tumor segmentation suffer from overfitting and poor efficiency when dealing with large datasets. The enhanced CNN architecture proposed in this study is based on U-Net for brain tumor segmentation, RefineNet for pattern analysis, and SegNet architecture for brain tumor classification. The brain tumor benchmark dataset was used to evaluate the enhanced CNN model's efficiency. Based on the local and context information of the MRI image, the U-Net provides good segmentation. SegNet selects the most important features for classification while also reducing the trainable parameters. In the classification of brain tumors, the enhanced CNN method outperforms the existing methods. The enhanced CNN model has an accuracy of 96.85 percent, while the existing CNN with transfer learning has an accuracy of 94.82 percent.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2400-2419
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• 2014

Pre-segmented pixels can reduce the difficulty of segmentation and promote the segmentation performance. This paper proposes a novel segmentation method based on merging texture superpixels by computing inner similarity. Firstly, we design a set of Gabor filters to compute the amplitude responses of original image and compute the texture map by a salience model. Secondly, we employ the simple clustering to extract superpixles by affinity of color, coordinates and texture map. Then, we design a normalized histograms descriptor for superpixels integrated color and texture information of inner pixels. To obtain the final segmentation result, all adjacent superpixels are merged by the homogeneity comparison of normalized color-texture features until the stop criteria is satisfied. The experiments are conducted on natural scene images and synthesis texture images demonstrate that the proposed segmentation algorithm can achieve ideal segmentation on complex texture regions.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.147-153
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• 2018

In this paper, we propose an efficient WBC 14-Diff classification which performs using the WBC-ResNet-152, a type of CNN model. The main point of view is to use Super-pixel for the segmentation of the image of WBC, and to use ResNet for the classification of WBC. A total of 136,164 blood image samples (224x224) were grouped for image segmentation, training, training verification, and final test performance analysis. Image segmentation using super-pixels have different number of images for each classes, so weighted average was applied and therefore image segmentation error was low at 7.23%. Using the training data-set for training 50 times, and using soft-max classifier, TPR average of 80.3% for the training set of 8,827 images was achieved. Based on this, using verification data-set of 21,437 images, 14-Diff classification TPR average of normal WBCs were at 93.4% and TPR average of abnormal WBCs were at 83.3%. The result and methodology of this research demonstrates the usefulness of artificial intelligence technology in the blood cell image classification field. WBC-ResNet-152 based morphology approach is shown to be meaningful and worthwhile method. And based on stored medical data, in-depth diagnosis and early detection of curable diseases is expected to improve the quality of treatment.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.496-500
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• 2015

International construction projects are inherently more risky than domestic projects with multi-dimensional uncertainties that require complementary risk management at both the country and project levels. However, despite a growing need for systematic country evaluations, most studies have focused on project-level decisions and lack country-based approaches for firms in the construction industry. Accordingly, this study suggests data-driven approaches for evaluating countries using two quantitative models. The first is a two-stage country segmentation model that not only screens negative countries based on country attractiveness (macro-segmentation) but also identifies promising countries based on the level of past project performance in a given country (micro-segmentation). The second is a multi-criteria country segmentation model that combines a firm's business objective with the country evaluation process based on Kraljic's matrix and fuzzy preference relations (FPR). These models utilize not only secondary data from internationally reputable institutions but also performance data on Korean firms from 1990 to 2014 to evaluate 29 countries. The proposed approaches enable firms to enhance their decision-making capacity for evaluating and selecting countries at the early stage of corporate strategy development.

• The Transactions of the Korea Information Processing Society
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• v.6 no.10
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• pp.2838-2844
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• 1999

The motion-based video segmentation provides a powerful method of video compression, because it defines a region with similar motion, and it makes video compression system to more efficiently describe motion video. In this paper, we propose the Modified Fuzzy Competitive Learning Algorithm (MFCLA) to improve the traditional K-menas clustering algorithm to implement the motion-based video segmentation efficiently. The segmented region is described with the affine model, which consists of only six parameters. This affine model was calculated with optical flow, describing the movements of pixels by frames. This method could be applied in the low bit rate video transmission, such as video conferencing system.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.63-73
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• 2022

A more accurate understanding of the irrigation water supply is necessary for efficient agricultural water management. Although we measure water levels in an irrigation canal using ultrasonic water level gauges, some errors occur due to malfunctions or the surrounding environment. This study aims to apply CNN (Convolutional Neural Network) Deep-learning-based image classification and segmentation models to the irrigation canal's CCTV (Closed-Circuit Television) images. The CCTV images were acquired from the irrigation canal of the agricultural reservoir in Cheorwon-gun, Gangwon-do. We used the ResNet-50 model for the image classification model and the U-Net model for the image segmentation model. Using the Natural Breaks algorithm, we divided water level data into 2, 4, and 8 groups for image classification models. The classification models of 2, 4, and 8 groups showed the accuracy of 1.000, 0.987, and 0.634, respectively. The image segmentation model showed a Dice score of 0.998 and predicted water levels showed R² of 0.97 and MAE (Mean Absolute Error) of 0.02 m. The image classification models can be applied to the automatic gate-controller at four divisions of water levels. Also, the image segmentation model results can be applied to the alternative measurement for ultrasonic water gauges. We expect that the results of this study can provide a more scientific and efficient approach for agricultural water management.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.2C
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• pp.147-161
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• 2003

Video object segmentation is one of the core technologies for content-based real-time MPEG-4 encoding system. For real-time requirement, the segmentation algorithm should be fast and accurate but almost all existing algorithms are computationally intensive and not suitable for real-time applications. The MPEG-4 VM(Verification Model) has provided basic algorithms for MPEG-4 encoding but it has many limitations in practical software development, real-time camera input system and compression efficiency. In this paper, we implemented the preprocessing system for real-time camera input and VOP extraction for content-based video coding and also implemented motion detection to achieve the 180 : 1 compression rate for real-time and high compression MPEG-4 encoding.

• The Transactions of the Korea Information Processing Society
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• v.7 no.1
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• pp.286-295
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• 2000

We present a novel statistically adaptive method using the Minimum Description Length(MDL) principle for unsupervised segmentation of magnetic resonance(MR) images. In the method, Markov random filed(MRF) modeling of tissue region accounts for random noise. Intensity measurements on the local region defined by a window are modeled by a finite Gaussian mixture, which accounts for image inhomogeneities. The segmentation algorithm is based on an iterative conditional modes(ICM) algorithm, approximately finds maximum ${\alpha}$ posteriori(MAP) estimation, and estimates model parameters on the local region. The size of the window for parameter estimation and segmentation is estimated from the image using the MDL principle. In the experiments, the technique well reflected image characteristic of the local region and showed better results than conventional methods in segmentation of MR images with inhomogeneities, especially.