• 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.

• Archives of design research
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• v.14 no.4
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• pp.25-33
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• 2001

With the increasing access to the internet, the number of designers who rely on internet to use information on design is on the rise. Therefore common dictionary of design terminology need to be formed and shared among designers. To do so, internet is very useful medium. However as relating terminology increases rapidly through interactivity among designers, it will be far from taking full advantage of features of internet to set up and provide such information unilaterally on internet. This indicates that providing data on the internet, not via traditional books, requires in-depth study on process of establishment of database structure and appropriate interface design. Thus this study will show design terms database model that harnesses internet feature that enables establishment of information spontaneously through user's interactivity, departing from a model that conveys information unilaterally. This report summarized and analyzed various models and suggested classification system in accordance with user's learning cognition. Problems on existing dictionary of design terminology were identified and new methods addressing such problems were exploited. In a word, this report is intended to propose user oriented inter-constructive database model that highlights high level of openness and interactivity by enabling changes of text in the cyber space and encouraging user to participate in making design dictionary.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.2
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• pp.53-60
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• 2020

Sentiment analysis is the automated process of understanding attitudes and opinions about a given topic from written or spoken text. One of the sentiment analysis approaches is a dictionary-based approach, in which a sentiment dictionary plays an much important role. In this paper, we propose a method to automatically generate Korean sentiment lexicon from the well-known English sentiment lexicon called VADER (Valence Aware Dictionary and sEntiment Reasoner). The proposed method consists of three steps. The first step is to build a Korean-English bilingual lexicon using a Korean-English parallel corpus. The bilingual lexicon is a set of pairs between VADER sentiment words and Korean morphemes as candidates of Korean sentiment words. The second step is to construct a bilingual words graph using the bilingual lexicon. The third step is to run the label propagation algorithm throughout the bilingual graph. Finally a new Korean sentiment lexicon is generated by repeatedly applying the propagation algorithm until the values of all vertices converge. Empirically, the dictionary-based sentiment classifier using the Korean sentiment lexicon outperforms machine learning-based approaches on the KMU sentiment corpus and the Naver sentiment corpus. In the future, we will apply the proposed approach to generate multilingual sentiment lexica.

• The Journal of Korean Association of Computer Education
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• v.7 no.2
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• pp.57-65
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• 2004

For the purpose of an effective instruction-learning process by systematic management of knowledge between instructor and learner in e-Learning, we have developed the authoring system in which the instructor is able to author easily on various lecture frames according to the instructional design theory. The authored contents with the relations among the learning objects based on SCORM standard would help learner to conceptualize the contents. A knowledge map is constructed on the relations among the learning objects using RDF of the semantic web. We introduce the ontology in which the instructor can make a dictionary of terminology by registering the words of the teaching area. The learning activity and comprehension of students can be assessed using each student's learning map along the interaction points which are introduced to present the individual learning by considering each student's capacity of understanding and achievement.

• English Language & Literature Teaching
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• v.15 no.4
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• pp.37-54
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• 2009

This study investigated vocabulary learning strategies used by EFL middle school learners in Korea and examined the relationship between the middle school learners' vocabulary learning strategy (VLS) use and their vocabulary proficiency level. One hundred and forty-one students in a public middle school participated in the study and the data for this study were collected from a vocabulary learning strategy questionnaire and a vocabulary proficiency test. Based on the result of the vocabulary proficiency test, the participants were divided into three proficiency groups: high-, mid- and low- level proficiency groups. The overall findings of the study revealed that the participants used cognitive strategies most frequently and social strategies least frequently. The most frequently used individual strategies were 'using a bilingual dictionary,' 'studying the sound of a word' and 'practicing words through verbal repetition.' The least frequently used ones were 'interacting with native speakers' and 'studying or practicing the meaning of a word in a group.' The research results also showed that the vocabulary proficiency level has a significant influence on the vocabulary strategy use. The more proficient learners used vocabulary learning strategies more actively. More specifically, the high proficiency level group used metacognitive strategies the most. The middle and low proficiency groups used cognitive strategies the most. It is suggested that language teachers should facilitate the vocabulary learning process by helping learners develop appropriate strategies.

• Journal of Information Management
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• v.41 no.2
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• pp.31-46
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• 2010

Now named-entity recognition(NER) as a part of information extraction has been used in the fields of information retrieval as well as question-answering systems. Unlike words, named-entities(NEs) are generated and changed steadily in documents on the Web, newspapers, and so on. The NE generation causes an unknown word problem and makes many application systems with NER difficult. In order to alleviate this problem, this paper proposes a new feature generation method for machine learning-based NER. In general features in machine learning-based NER are related with words, but entities in named-entity dictionaries are related to phrases. So the entities are not able to be directly used as features of the NER systems. This paper proposes an encoding scheme as a feature generation method which converts phrase entities into features of word units. Futhermore, due to this scheme, entities with semantic information in WordNet can be converted into features of the NER systems. Through our experiments we have shown that the performance is increased by about 6% of F1 score and the errors is reduced by about 38%.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.81-88
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• 2017

This paper proposes a method of detecting rain regions from a single image. More specifically, a way of training the deep convolutional neural network based on the collected rain and non-rain patches is presented in a supervised manner. It is also shown that the proposed rain detection method based on deep convolutional neural network can provide better performance than the conventional rain detection method based on dictionary learning. Moreover, it is confirmed that the application of the proposed rain detection for rain removal can lead to some improvement in detail representation on the low-frequency regions of the rain-removed images. Additionally, this paper introduces the rain transfer method that inserts rain patterns into original images, thereby producing rain effects on the resulting images. The proposed rain transfer method could be used to augment rain patterns while constructing rain database.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1273-1280
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• 1996

In order perform high accuracy recognition of text recognition systems, the recognized text must be processed through a post-processing stage using contextual information. We present a system that combines multiple knowledge sources to post-process the output of an optical character recognition(OCR) system. The multiple knowledge sources include characteristics of word, wrongly recognized types of Hangul characters, and Hangul word learning In this paper, the wrongly recognized characters which are made by OCR systems are collected and analyzed. We imput a Korean dictionary with approximately 15 0,000 words, and Korean language texts of Korean elementary/middle/high school. We found that only 10.7% words in Korean language texts of Korean elementary/middle /high school were used in a Korean dictionary. And we classified error types of Korean character recognition with OCR systems. For Hangul word learning, we utilized indexes of texts. With these multiple knowledge sources, we could predict a proper word in large candidate words.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.113-121
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• 2013

In this paper, we propose a sub-pixel rendering algorithm using learning-based 2D FIR filters. The proposed algorithm consists of two stages: the learning and synthesis stages. At the learning stage, we produce the low-resolution synthesis information derived from a sufficient number of high/low resolution block pairs, and store the synthesis information into a so-called dictionary. At the synthesis stage, the best candidate block corresponding to each input high-resolution block is found in the dictionary. Next, we can finally obtain the low-resolution image by synthesizing the low-resolution block using the selected 2D FIR filter on a sub-pixel basis. On the other hand, we additionally enhance the sharpness of the output image by using pre-emphasis considering RGB stripe pattern of display. The simulation results show that the proposed algorithm can provide significantly sharper results than conventional down-sampling methods, without blur effects and aliasing.

• Smart Media Journal
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• v.12 no.9
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• pp.19-27
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• 2023

Since morphemes are the smallest unit of meaning in Korean, it is necessary to develop an accurate morphemes analyzer to improve the performance of the Korean language model. However, most existing analyzers present morpheme analysis results by learning word unit tokens as input values. However, since Korean words are consist of postpositions and affixes that are attached to the root, even if they have the same root, the meaning tends to change due to the postpositions or affixes. Therefore, learning morphemes using word unit tokens can lead to misclassification of postposition or affixes. In this paper, we use morpheme-level tokens to grasp the inherent meaning in Korean sentences and propose a morpheme analyzer based on a sequence generation method using Transformer. In addition, a user dictionary is constructed based on corpus data to solve the out - of-vocabulary problem. During the experiment, the morpheme and morpheme tags printed by each morpheme analyzer were compared with the correct answer data, and the experiment proved that the morpheme analyzer presented in this paper performed better than the existing morpheme analyzer.