• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.1
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• pp.68-79
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• 2009

It is inevitable to have cutting line to get the 2D pattern from 3D body surface. In this paper the efficiency of curvature plot as a cutting line in the process of flattening 3D surface was investigated. As reference, basic clothing construction line was adopted to divide the 3D surface into small blocks to make the flattening process easy. Female dummy as well as human body were scanned and surface of the upper body was segmented using curvature plot and basic constructing line. 2D tight-fit pattern was developed using three software, the RapidForm 2004, 2C-AN and Yuka CAD. Gap between clothes and body, and the clothing pressure on the body was observed to determine the fit of the clothes. As results, clothes constructed with blocks divided by curvature plot displayed a similar level of tight fit as compared with those by basic construction line. It was found that curvature plot is effective method as a segmentation of the 3D surface even for the actual body which does not have any previous reference line. It is expected that application of curvature plot will be expanded in 3D apparel technology.

• Progress in Medical Physics
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• v.15 no.2
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• pp.112-119
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• 2004

We tried to build human models based on medical images of live Korean, instead of using standard data of human body structures. Characteristics of mechanical structures of human bodies were obtained from medical images such as CT and MR images. For each constitutional part of mechanical structures CT images were analyzed in terms of gray levels and MR images were analyzed in terms of pulse sequence. Characteristic features of various mechanical structures were extracted from the analyses. Based on the characteristics of each structuring element we peformed image segmentation on CT and MR images. We delineated bones, muscles, ligaments and tendons from CT and MR images using image segmentation or manual drawing. For the image segmentation we compared the edge detection method, region growing method and intensity threshold method and applied an optimal compound of these methods for the best segmentation results. Segmented mechanical structures of the head/neck part were three dimensionally reconstructed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.1
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• pp.74-80
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• 2010

The purpose of reverse engineering design using 3D measurement data is an accurate reconstruction of real body. In oder to accomplish this object, it is important that creating exact extracting edges should be studying out first of all. This study used edge-based method to find out edge point from the measuring point data. The characteristics are analysed using the mean curvature block method on the fitting NURBS curve and defined edges through block removal condition. The results showed that only using the NURBS curve of maximum curvature analysis to define correct edge of real geometry is limited, but this segmentation approach provides simplified necessary condition for edge classification, and an effectiveness to classify a straight line, curves and fillets etc.

• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.312-318
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• 2006

The conventional lip-synch system has a two-step process, speech segmentation and recognition. However, the difficulty of speech segmentation procedure and the inaccuracy of training data set due to the segmentation lead to a significant Performance degradation in the system. To cope with that, the connected vowel recognition method using Head-Body-Tail (HBT) model is proposed. The HBT model which is appropriate for handling relatively small sized vocabulary tasks reflects co-articulation effect efficiently. Moreover the 7 vowels are merged into 3 classes having similar lip shape while the system is optimized by employing a class dependent SCHMM structure. Additionally in both end sides of each word which has large variations, 8 components Gaussian mixture model is directly used to improve the ability of representation. Though the proposed method reveals similar performance with respect to the CHMM based on the HBT structure. the number of parameters is reduced by 33.92%. This reduction makes it a computationally efficient method enabling real time operation.

• Journal of the Korea Computer Graphics Society
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• v.22 no.3
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• pp.21-29
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• 2016

Cell segmentation is an important but time-consuming and laborious task in biological image analysis. An automated, robust, and fast method is required to overcome such burdensome processes. These needs are, however, challenging due to various cell shapes, intensity, and incomplete boundaries. A precise cell segmentation will allow to making a pathological diagnosis of tissue samples. A vast body of literature exists on cell segmentation in microscopy images [1]. The majority of existing work is based on input images and predefined feature models only - for example, using a deformable model to extract edge boundaries in the image. Only a handful of recent methods employ data-driven approaches, such as supervised learning. In this paper, we propose a novel data-driven cell segmentation algorithm for bright-field microscopy images. The proposed method minimizes an energy formula defined by two dictionaries - one is for input images and the other is for their manual segmentation results - and a common sparse code, which aims to find the pixel-level classification by deploying the learned dictionaries on new images. In contrast to deformable models, we do not need to know a prior knowledge of objects. We also employed convolutional sparse coding and Alternating Direction of Multiplier Method (ADMM) for fast dictionary learning and energy minimization. Unlike an existing method [1], our method trains both dictionaries concurrently, and is implemented using the GPU device for faster performance.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.171-177
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• 2021

It is difficult to accurately extract features of pedestrian because the pedestrian is photographed at a crosswalk using a camera positioned higher than the pedestrian. In addition, it is more difficult to extract features when a part of the pedestrian's body is covered by an umbrella or parasol or when the pedestrian is holding an object. Representative methods to solve this problem include Object Detection, Instance Segmentation, and Pose Estimation. Among them, this study intends to use the Pose Estimation method. In particular, we intend to increase the recognition rate of pedestrians in crosswalks by maintaining the image resolution through HigherHRNet and applying the foot estimation technique. Finally, we show the superiority of the proposed method by applying and analyzing several data sets covered by body parts to the existing method and the proposed method.

• Journal of Advanced Navigation Technology
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• v.11 no.1
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• pp.59-63
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• 2007

In this paper, an image segmentation algorithm is described to extract both the contour line and the inner grid patterns of body in case of ambiguous environment. The binary method using a threshold is used to extract image boundary. To reduce image noise, the $3{\times}3$ hybrid high pass filter adjusted for applying 3D information extraction of complicated shape object is proposed. This hybrid high pass filter algorithm can be applied to extract complicated shape object such as 3D body shape, CAD system, and factory automation since the processing time for image denoising is shorter than the conventional methods.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.4
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• pp.191-194
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• 2014

This paper proposes a fast and efficient method to extract the skin and bone automatically in CT images. First, the images were smoothed by applying an anisotropic diffusion filter to remove noise. The whole body was then detected by thresholding, which was set automatically. In addition, the contour of the skin was segmented using morphological operators and connected component labeling (CCL). Finally, the bone was extracted by iterative thresholding.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.705-708
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• 2009

This research describes a neural network-based phoneme segmenter for recognizing spontaneous speech. The input of the phoneme segmenter for spontaneous speech is 16th order mel-scaled FFT, normalized frame energy, ratio of energy among 0~3[KHz] band and more than 3[KHz] band. All the features are differences of two consecutive 10 [msec] frame. The main body of the segmenter is single-hidden layer MLP(Multi-Layer Perceptron) with 72 inputs, 20 hidden nodes, and one output node. The segmentation accuracy is 78% with 7.8% insertion.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.33-36
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• 2020

Virtual try on is getting interested from researchers these days because its application in online shopping. But single pose virtual try on is not enough, customer may want to see themselves in different pose. Multiple pose virtual try on is getting input as customer image, an in-shop cloth and a target pose, it will try to generate realistic customer wearing the in-shop cloth with the target pose. We first generate the target segmentation layout using conditional generative network (cGAN), and then the in-shop cloth are warped to fit the customer body in target pose. Finally, all the result will be combine using a Resnet-like network. We experiment and show that our method outperforms stage of the art.