• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.979-982
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• 2005

This paper is to be pre-processing that decides the text recognizability and quality contained in natural image. Differentiated with the existing studies, In this paper, it suggests the application of partially unified color models, Coiflet Wavelet and text extraction algorithm that uses the closed curve edge features of LoG (laplacian of gaussian)operator. The text image included in natural image such as signboard has the same hue, saturation and value, and there is a certain thickness as for their feature. Each color element is restructured into closed area by LoG operator, the 2nd differential operator. The text area is contracted by Hough Transform, logical AND-OR operator of each color model and Minimum-Distance classifier. This paper targets natural image into which text area is added regardless of the size and resolution of the image, and it is confirmed to have more excellent performance than other algorithms with many restrictions.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.371-376
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• 2012

In this paper, we propose a new data coding method and its circuits for minimizing data transition in text image. The proposed circuits can solve the synchronization problem between input data and output data in the modified LVDS algorithm. And the proposed algorithm is allowed to transmit two data signals through additional serial data coding method in order to minimize the data transition in text image and can reduce the operating frequency to a half. Thus, we can solve EMI(Electro-Magnetic Interface) problem and reduce the power consumption. The simulation results show that the proposed algorithm and circuits can provide an enhanced data transition minimization in text image and solve the synchronization problem between input data and output data.

• The Research Journal of the Costume Culture
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• v.18 no.5
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• pp.977-990
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• 2010

Today, as individuals show their social identities and reflect their being as the members of society with a culture, an art style and communication function are stood out in fashion photographs. Accordingly, the meanings of images into text are expanded in its interpretative width through the acceptor's various terms. This researcher looked into four theories of both positions on the textuality of language and image, and considered the point of discussion on image of each theory through modern fashion photographs. First, the theory which divides language and image as auditory and visual recognitions in the textuality of language and image is limited from the view it focuses on only one side without considering the ambivalent elements of each field. For the textuality in modern fashion photographs, the observer attempts to turn it into text to give meaning to it as the recognition through five senses conforming to the acceptor's condition. Second, the theory dividing language and image into the text of time properties and spacial properties has limitation in the text, for acceptor's experience of the object appears as the structured form in time and space rather than being defined as two things like time and space. Third, the theory classifying the language and image text into conventional taste and natural taste has limitation from the view that image text is hardly an object of consistent classification in ease of recognition by the code accepted in society. Thus, this can't be fundamental approach for the understanding of the text of decoding trend represented in modern fashion photographs. Fourth, accordingly, this researcher focussed on contextual and arbitrary text of fashion photographs through the theory of Nelson Goodman which discusses image text through the differences in textuality. Basic mechanism of perceiving and recognizing and distinguish image is closely related to habit and custom like language. So, each acceptor perceives the image as a text through arbitrary interpretation obtained by individual, empirical, historical, and educational viewpoints. The textuality of modern fashion photographs aims to widen the range of diverse knowledge and understanding, transcending the regulations of simple function of existing fashion photographs. Consequently, this researcher puts forward the opinion of consistent and diverse follow-up studies on instilling meaning into fashion photographs for the understanding de-regulatory and de-constructive through various senses by avoiding only one sense-dependent fixed and regulatory properties of it.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.57-67
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• 2021

Recently, multi-modal deep learning techniques that combine heterogeneous data for deep learning analysis have been utilized a lot. In particular, studies on the synthesis of Text to Image that automatically generate images from text are being actively conducted. Deep learning for image synthesis requires a vast amount of data consisting of pairs of images and text describing the image. Therefore, various data augmentation techniques have been devised to generate a large amount of data from small data. A number of text augmentation techniques based on synonym replacement have been proposed so far. However, these techniques have a common limitation in that there is a possibility of generating a incorrect text from the content of an image when replacing the synonym for a noun word. In this study, we propose a text augmentation method to replace words using word hierarchy information for noun words. Additionally, we performed experiments using MSCOCO data in order to evaluate the performance of the proposed methodology.

• Journal of IKEEE
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• v.27 no.4
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• pp.561-566
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• 2023

With the development of deep learning, it is possible to solve various computer non-specialized problems such as image processing. However, most image processing methods use only the visual information of the image to process the image. Text data such as descriptions and annotations related to images may provide additional tactile and visual information that is difficult to obtain from the image itself. In this paper, we intend to improve image classification accuracy through a deep learning model that analyzes images and texts using image-text pairs. The proposed model showed an approximately 11% classification accuracy improvement over the deep learning model using only image information.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.359-362
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• 2002

In this paper, we propose a new feature-based text watermarking for the binary text image. The structure of specific characters from preprocessed text image are modified to embed watermark. Watermark message are embedded and detected by the following method; Hole line disconnect using the connectivity of the character containing a hole, Center line shift using the hole area and Differential encoding using difference of flippable score points. Experimental results show that the proposed method is robust to rotation and scaling distortion.

• Archives of design research
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• v.14 no.4
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• pp.199-207
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• 2001

The public performance of drama has essence to convert text to visual images as stage and to represent, inform such visual images. Visual image is formed through a space as stage. Stage is a dish to fill the texts and is the mother's womb of visual image. Namely, visual image of drama equals space image. The purpose of this study is a trial to grasp the structure and system represented through interpreting. Especially, the concerns of this study are not semiology of letter imported in image but spacial image text importing just contents of text and the process and contents grasping the structure and meaning of dramatic space. Finally, this study proposed 'the system of space embodiment'from the semiotic point of view as interpretation methodology of actual memorial, symbolic space.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.36-41
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• 2021

Text-based image retrieval is not only cumbersome as it requires the manual input of keywords by the user, but is also limited in the semantic approach of keywords. However, content-based image retrieval enables visual processing by a computer to solve the problems of text retrieval more fundamentally. Vision applications such as extraction and mapping of image characteristics, require the processing of a large amount of data in a mobile environment, rendering efficient power consumption difficult. Hence, an effective image retrieval method on mobile platforms is proposed herein. To provide the visual meaning of keywords to be inserted into images, the efficiency of image retrieval is improved by extracting keywords of exchangeable image file format metadata from images retrieved through a content-based similar image retrieval method and then adding automatic keywords to images captured on mobile devices. Additionally, users can manually add or modify keywords to the image metadata.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.60.2-60
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• 2001

In this paper, manufacturing system and Internet are combined and NC milling machine engraves image and text on nameplate. Image and text are input through Internet. And NC tool path is obtained by thinning algorithm and NC part program is generated. Thinning algorithm detects center lines from image and text by using connectivity and tool path is obtained along the center line. Actually experiments are performed and thinning algorithm and G-code generation module are verified.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.32-37
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• 2009

Recently, electronic mail(E-mail) is the most popular communication manner in our society. In such conventional environments, spam increasingly congested in Internet. In this paper, Chinese spam could be effectively detected using text and image features. Using text features, keywords and reference templates in Chinese mails are automatically selected using genetic algorithm(GA). In addition, spam containing a promotion image is also filtered out by detecting the text characters in images. Some experimental results are given to show the effectiveness of our proposed method.