• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.95-100
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• 1997

In this paper, we propose a coding method to improve compression efficiency for MR image. This can be achieved by combining coding and segmentation scheme which removes noisy background region, which is meaningless for diagnosis, in MR image. The wavelet coder encodes only diagnostically significant foreground regions refering to segmentation map. Our proposed algorithm provides about 15% of bitrate reduction when compared with the same coder which is not combined with segmentation scheme. And the proposed scheme shows better reconstructed image Qualify than JPEG at the same compression ratio.

• Smart Media Journal
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• v.4 no.3
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• pp.44-49
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• 2015

Text line segmentation is a preprocessing step in OCR, which can significantly influence the accuracy of document analysis applications. This paper proposes a novel methodology for the text line segmentation of handwritten documents. First, the average width of the connected components is used to form a 1-D Gaussian kernel and a smoothing operation is then applied to the input binary image. The adaptive binarization of the smoothed image forms the final text lines. In this work, the segmentation method involves two stages: firstly, the large connected components are labelled as a unique text line using text line area mapping. Secondly, the final refinement of the segmentation is performed using the Euclidean distance between the text line and small connected components. The group of uniquely labelled text candidates achieves promising segmentation results. The proposed approach works well on Korean and English language handwritten documents captured using a camera.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1995.06a
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• pp.143-148
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• 1995

In this paper, we describe a fast 3D video segmentation method using mathematical morphology. The proposed 3D video segmentation algorithm is composed of intra-frame segmentation step and inter-frame segmentation step. In the intra-frame segmentation step, the first frame is segmented using the fast hierarchical segmentation method. Then, in the inter-frame segmentation step, the next frames are segmented using markers that are extracted from the difference of previous segmentation result and simplified present image. Experimental results show that the proposed method has more fast structure and is suitable for video segmentation.

• Journal of Information Technology Applications and Management
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• v.19 no.2
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• pp.97-116
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• 2012

Since 2008 when IPTV service entered the broadcasting market, the competition among interactive broadcasting media has been growing more and more fierce. To make a market strategy under the harsh competition, this study tried to make an IPTV customer segmentation based on the characteristics of interactive broadcasting media. From previous literature, this study drew five characteristics of interactive broadcasting media : ease of use, two-way communications, active control, variety of content, and economic efficiency. Two-step clustering based on these characteristics identified four customer segments. There were statistically significant differences in the five characteristics among the customer segments. This study profiled the customer segments and proposed competitive strategies for each customer segment.

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.531-538
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• 2016

In this paper, we propose a novel post-processing method of semantic segmentation from indoor scenes with RGBD inputs. For accurate segmentation, various post-processing methods such as superpixel from color edges or Conditional Random Field (CRF) method considering neighborhood connectivity have been used, but these methods are not efficient due to high complexity and computational cost. To solve this problem, we maximize the efficiency of post processing by using depth superpixel extracted from disparity image to handle object silhouette. Our experimental results show reasonable performances compared to previous methods in the post processing of semantic segmentation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.1
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• pp.61-67
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• 2014

Motion segmentation is a fundamental technique for analysing image sequences of real scenes. A process of identifying moving objects from data is a typical task in many computer vision applications. In this paper, we propose motion segmentation that generally consists from background subtraction and foreground pixel segmentation. The Approximated Median Filter(AMF) was chosen to perform background modeling. Motion segmentation in this paper covers RGB video data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.1-8
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• 2022

Recently, Google MedeiaPipe presents a novel approach for neural network-based hair segmentation from a single camera input specifically designed for real-time, mobile application. Though neural network related to hair segmentation is relatively small size, it produces a high-quality hair segmentation mask that is well suited for AR effects such as a realistic hair recoloring. However, it has undesirable segmentation effects according to hair styles or in case of containing noises and holes. In this study, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood function. It is further optimized according to graph cuts algorithm and initial hair region is obtained. Finally, clustering algorithm and image post-processing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. The proposed method is applied to MediaPipe hair segmentation pipeline.

• Smart Media Journal
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• v.1 no.4
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• pp.59-71
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• 2012

Since mobile phones are used as common communication devices, their applications are increasingly important to human's life. Using smart-phones camera to collect daily life environment's information is one of targets for many applications such as text recognition, object recognition or context awareness. Studies have been conducted to provide important information through the recognition of texts, which are artificially or naturally included in images and movies acquired from mobile phones. In this study, a character segmentation method that improves character-recognition accuracy in images obtained from mobile phone cameras is proposed. The proposed method first classifies texts in a given image to printed letters and handwritten letters since segmentation approaches for them are different. For printed letters, rough segmentation process is conducted, then the segmented regions are integrated, deleted, and re-segmented. Segmentation for the handwritten letters is performed after skews are corrected and the characters are classified by integrating them. The experimental result shows our method achieves a successful performance for both printed and handwritten letters as 95.9% and 84.7%, respectively.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.701-710
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• 2022

With the development of artificial intelligence technology, various methods have been developed for recognizing objects in images using machine learning. Image segmentation is the most effective among these methods for recognizing objects within an image. Conventionally, image datasets of various classes are trained simultaneously. In situations where several classes require segmentation, all datasets have to be trained thoroughly. Such repeated training results in low training efficiency because most of the classes have already been trained. In addition, the number of classes that appear in the datasets affects training. Some classes appear in datasets in remarkably smaller numbers than others, and hence, the training errors will not be properly reflected when all the classes are trained simultaneously. Therefore, a new method that separates some classes from the dataset is proposed to improve efficiency during training. In addition, the accuracies of the conventional and proposed methods are compared.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.217-222
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• 2009

In this paper, a new approach is proposed for the segmentation of Computed Tomography (CT) head images. The approach consists of two-stage segmentation with each stage contains two different segmentation techniques. The ultimate aim is to segment the CT head images into three classes which are abnormalities, cerebrospinal fluid (CSF) and brain matter. For the first stage segmentation, k-means and fuzzy c-means (FCM) segmentation are implemented in order to acquire the abnormalities. Whereas for the second stage segmentation, modified FCM with population-diameter independent (PDI) and expectation-maximization (EM) segmentation are adopted to obtain the CSF and brain matter. The experimental results have demonstrated that the proposed system is feasible and achieve satisfactory results.