• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.871-891
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• 2019

Visual tracking is a challenging task that needs learning an effective model to handle the changes of target appearance caused by factors such as pose variation, illumination change, occlusion and motion blur. In this paper, a novel tracking algorithm based on weighted local sub-space reconstruction error is presented. First, accounting for the appearance changes in the tracking process, a generative weight calculation method based on structural reconstruction error is proposed. Furthermore, a template update scheme of occlusion-aware is introduced, in which we reconstruct a new template instead of simply exploiting the best observation for template update. The effectiveness and feasibility of the proposed algorithm are verified by comparing it with some state-of-the-art algorithms quantitatively and qualitatively.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.90-95
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• 2023

With the rapid development of deep learning and artificial intelligence, generative models have achieved remarkable success in the field of image generation. By combining the stable diffusion method with Web UI technology, a novel solution is provided for the application of AI painting generation. The application prospects of this technology are very broad and can be applied to multiple fields, such as digital art, concept design, game development, and more. Furthermore, the platform based on Web UI facilitates user operations, making the technology more easily applicable to practical scenarios. This paper introduces the basic principles of Stable Diffusion Web UI technology. This technique utilizes the stability of diffusion processes to improve the output quality of generative models. By gradually introducing noise during the generation process, the model can generate smoother and more coherent images. Additionally, the analysis of different model types and applications within Stable Diffusion Web UI provides creators with a more comprehensive understanding, offering valuable insights for fields such as artistic creation and design.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.85-91
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• 2024

Currently, generative artificial intelligence is a hot topic around the world. Generative artificial intelligence creates various images, art, video clips, advertisements, etc. The problem is that it is very difficult to verify the internal work of artificial intelligence. As a requirements engineer, I attempt to create a toon image by applying linguistic mechanisms to the current issue. This is combined with the UML object model through the semantic role analysis technique of linguists Chomsky and Fillmore. Then, the derived properties are linked to the toon creation template. This is to ensure productivity based on reusability rather than creativity in toon engineering. In the future, we plan to increase toon image productivity by incorporating software development processes and reusability.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.233-238
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• 2014

Creativity is crucial in designing and producing attractive accessaries and daily supplies as well as art works. Generative design can be a paradigm to be used to obtain novel ideas or motifs for creative design works. This paper introduces a generative design method which comes up with unique ring models using genetic algorithm. It presents how the genetic algorithm works in terms of candidate solution coding, operators, and fitness evaluation function. The proposed method allows the customers to express their personal preference and later the preference to be reflected in fitness evaluation. In the final stage of the proposed method, several ring models are suggested for customers to choose on their own. The chosen ring models can be put into physical rings with the help of a 3D printer because the models are expressed in 3D geometric structures.

• Journal of Science of Art and Design
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• v.6
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• pp.288-312
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• 2004

Despite the government's efforts and excessive investments in the establishment of an infrastructure for animation, it can be said that the reasons for the inferiority in character licensing and weak performances compared to other leading countries is attributed to the fundamental factor tying within the piece of work itself. Therefore, this research is aimed at finding a counterproposal by assuming that the reason for the failure of our country's animation 'Oseam' was because of its narrative tactics. First, it is possible for a narrative structure to be participated by the audience's creative strategy Second, the audience's participation makes it possible to create the character into a multilateral and a complex personality. Third, this kindof creative strategy is possible when semiotic analyses are utilized. The sole objective for this is to provide a comprehensive viewpoint for the semiotic approach in the creation process of the character.

• Journal of the Korea Fashion and Costume Design Association
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• v.25 no.1
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• pp.153-168
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• 2023

Non-representation creates difference and change that can be used as a creative design method that satisfies contradictory requirements for similarity and differentiation. This study drew upon the characteristics of the concept of non-representation expressed in contemporary art and architecture, in which Gilles Deleuze's philosophical thinking was reflected, and analyzed the non-representation depicted in contemporary fashion. The non-representation expressed in contemporary art and architecture is as follows. Non-representation of delaying becoming focuses on reverting to preexisting objects and redefining traditional meaning, thereby delaying the representation of latent meaning. Non-representation of non-becoming removes existing values and typical forms and expresses amorphousness. Non-representation of becoming by repetition or reiteration realizes the difference caused by the passage of time by repeating or overlapping shapes. Non-representation of becoming expresses the transformation of space by flowing through time rather than by actual movement. Non-representation in contemporary fashion shows the following expression characteristics. First, the non-representation of deferring becoming deconstructs the traditional values and forms of clothing and expresses designs by displacement or juxtaposition. Second, the non-representation of non-becoming is expressed concepts unrelated to the body and focus upon amorphous objects that do not become concretized forms. Third, generative non-representation by repetition and overlap expresses the possibility of change by overlapping clothing items or details expressed by repeating segmented objects. Fourth, generative non-representation by movement reproduces the meaning of space and time by moving the shape of the clothing or visually changing the surface of the material of clothing. As a result of the study, the non-representation shown in contemporary fashion aims for versatility to conform to social changes. This study provides new insight into the fashion design method by increasing the understanding of the cocnept of non-representation and showing its potential.

• Smart Media Journal
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• v.8 no.2
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• pp.46-57
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• 2019

In recent research, deep learning-based methods have achieved state-of-the-art performance in various computer vision tasks. However, these methods are commonly supervised, and require huge amounts of annotated data to train. Acquisition of data demands an additional costly effort, particularly for the tasks where it becomes challenging to obtain large amounts of data considering the time constraints and the requirement of professional human diligence. In this paper, we present a data level synthetic sampling solution to learn from small and imbalanced data sets using Generative Adversarial Networks (GANs). The reason for using GANs are the challenges posed in various fields to manage with the small datasets and fluctuating amounts of samples per class. As a result, we present an approach that can improve learning with respect to data distributions, reducing the partiality introduced by class imbalance and hence shifting the classification decision boundary towards more accurate results. Our novel method is demonstrated on a small dataset of 2789 tomato plant disease images, highly corrupted with class imbalance in 9 disease categories. Moreover, we evaluate our results in terms of different metrics and compare the quality of these results for distinct classes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1794-1806
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• 2023

This study presents a method for capturing photographs of users as input and converting them into 2D character animation sprites using a generative adversarial network-based artificial intelligence network. Traditionally, 2D character animations have been created by manually creating an entire sequence of sprite images, which incurs high development costs. To address this issue, this study proposes a technique that combines motion videos and sample 2D images. In the 2D sprite generation process that uses the proposed technique, a sequence of images is extracted from real-life images captured by the user, and these are combined with character images from within the game. Our research aims to leverage cutting-edge deep learning-based image manipulation techniques, such as the GAN-based motion transfer network (impersonator) and background noise removal (U² -Net), to generate a sequence of animation sprites from a single image. The proposed technique enables the creation of diverse animations and motions just one image. By utilizing these advancements, we focus on enhancing productivity in the game and animation industry through improved efficiency and streamlined production processes. By employing state-of-the-art techniques, our research enables the generation of 2D sprite images with various motions, offering significant potential for boosting productivity and creativity in the industry.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.1149-1152
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• 2005

Detecting faces in images with complex backgrounds is a difficult task. Our approach, which obtains state of the art results, is based on a generative neural network model: the Constrained Generative Model (CGM). To detect side view faces and to decrease the number of false alarms, a conditional mixture of networks is used. To decrease the computational time cost, a fast search algorithm is proposed. The level of performance reached, in terms of detection accuracy and processing time, allows to apply this detector to a real word application: the indexation of face images on the Web.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.211-216
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• 2024

The purpose of this study is to examine the functional aspects linked to the productivity innovation of ChatGPT, which emerged as a result of the rapid development of AI technology, and to identify ways to apply it in the jewelry industry. By analyzing the definition of ChatGPT and its features that improve productivity, I identify the scope of its application in the jewelry production process and derive meaningful implications. ChatGPT has the characteristics of 'learning', 'communication', and 'generative'. It enhances productivity by applying it to the jewelry industry. Social issues arise from the paradigm shift in the creation methods of generative AI. The version of ChatGPT is continuously upgraded along with the expansion of parameters. Accordingly, we would like to discuss ways to strengthen the competitiveness of the jewelry industry by conducting continuous research.