• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.548-556
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• 2019

The purpose of this study is to analyze the impact of dietary lifestyles and demographic characteristics on the Brand image of restaurants with Nutritional labeling to provide basic marketing data for establishing differentiated Brand image strategies for restaurant businesses. To that end, the SPSS21.0 (ver.) program, frequency analysis, descriptive statistics, factor analysis, reliability analysis, correlation analysis, and multiple linear regression analysis were conducted to verify the hypothesis. As a result, the Brand image of restaurants with Nutritional labeling improved as the metropolitan area sought safety, non-capital area sought taste, males sought health, and females sought safety. In terms of age, it was analyzed that as more people in their 20s sought taste, those their 30s and 40s sought safety, and both married and unmarried people sought safety, the Brand image of restaurants with Nutritional labeling improved. In other words, it could be seen that people with Dietary lifestyles who pursued health and safety had positive images of restaurants with Nutritional labeling regardless of residential area, age, gender, marital status, or whether they had children.

• Journal of Korea Multimedia Society
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• v.4 no.4
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• pp.234-332
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• 2001

For a method improving fractal image segmentation which is a new application of fractal image coding, YST scheme have proposed an image segmentation scheme using labeling based on periodic points of pixel transformation and error-correction of labels by iterating fractal transformation. The scheme generates the high quality segmentation, however, it has the redundancy in the process of labeling and correction of labels. To solve this problem, we propose a labeling algorithm based on orbit of pixel transformation and restricted condition on iterating process of fractal transformation.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.701-715
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• 2021

The drastic advance of recent deep learning technologies is heavily dependent on training datasets which are essential to train models by themselves with less human efforts. In comparison with the work to design deep learning models, preparing datasets is a long haul; at the moment, in the domain of vision intelligent, datasets are still being made by handwork requiring a lot of time and efforts, where workers need to directly make labels on each image usually with GUI-based labeling tools. In this paper, we overview the current status of vision datasets focusing on what datasets are being shared and how they are prepared with various labeling tools. Particularly, in order to relieve the repetitive and tiring labeling work, we present an interactive smart image annotating system with which the annotation work can be transformed from the direct human-only manual labeling to a correction-after-checking by means of a support of automatic labeling. In an experiment, we show that automatic labeling can greatly improve the productivity of datasets especially reducing time and efforts to specify regions of objects found in images. Finally, we discuss critical issues that we faced in the experiment to our annotation system and describe future work to raise the productivity of image datasets creation for accelerating AI technology.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.153-156
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• 2002

The image labeling and classification is one of the important tasks for a content-based image retrieval and an image understanding. This paper propose a new technique to label and classify natural images with a color-spatial histogram and production rules. We show that our proposed method is very efficient for a natural image composed of a few regions.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.171-172
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• 1995

In this paper, an anatomical labeling system for assisting localization of region of interest on human brain imaging is represented. Model image for labeling anatomical name on the other image is Atlas. Object image to be labeled, such as CT, MR, and PET, is registered onto Atlas. And then, anatomical name for region of interest is appeared on a window by clicking mouse button on object image. The same part named anatomically on that region is labeled and drawn on object image.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.241-241
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• 2000

In this Paper, we present a new efficient algorithm that can segment an object in the image. There are many algorithms for segmentation and many studies for criteria or threshold value. But, if the environment or brightness is changed, their would not be suitable. Accordingly, we apply a clustering algorithm for adopting and compensating environmental factors. And applying labeling method, we try arranging segment by the similarity that calculated with the fuzzy algorithm. we also present simulations for searching an object and show that the algorithm is somewhat more efficient than the other algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.408-410
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• 2014

In this paper a license plate area extraction of labeling the vehicle images is proposed. Studies on license plate recognition systems have largely been conducted and there is a tendency of increasing license plate recognition rates. In this paper a license plate region is extracted from an image labeling for the region of interest and research on technology for labeling sample image using the Otsu algorithm to binary.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9C
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• pp.555-563
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• 2011

In this paper, we propose an object based 2D image to 3D conversion algorithm by using motion estimation, color labeling and non-local mean filtering methods. In the proposed algorithm, we first extract the motion vector of each object by estimating the motion between frames and then segment a given image frame with color labeling method. Then, combining the results of motion estimation and color labeling, we extract object regions and assign an exact depth value to each object to generate the right image. While generating the right image, occlusion regions occur but they are effectively recovered by using non-local mean filter. Through the experimental results, it is shown that the proposed algorithm performs much better than conventional conversion scheme by removing the eye fatigue effectively.

• Journal of information and communication convergence engineering
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• v.15 no.1
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• pp.49-52
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• 2017

Ganglion cysts are benign soft tissues usually encountered in the wrist. In this paper, we propose a method to extract a ganglion cyst region from ultrasonography images by using image segmentation. The proposed method using the possibilistic c-means (PCM) clustering method is applicable to ganglion cyst extraction. The methods considered in this thesis are fuzzy stretching, median filter, PCM clustering, and connected component labeling. Fuzzy stretching performs well on ultrasonography images and improves the original image. Median filter reduces the speckle noise without decreasing the image sharpness. PCM clustering is used for categorizing pixels into the given cluster centers. Connected component labeling is used for labeling the objects in an image and extracting the cyst region. Further, PCM clustering is more robust in the case of noisy data, and the proposed method can extract a ganglion cyst area with an accuracy of 80% (16 out of 20 images).

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.731-734
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• 1998

A scalable parallel algorithm is proposed for efficient image component labeling with local operatos on a mesh connected SIMD computer. In contrast to the conventional parallel labeling algorithms, where a single pixel is assigned to each PE, the algorithm presented here is scalable and can assign m$\times$m pixel set to each PE according to the input image size. The assigned pixel set is converted to a single pixel that has representative value, and the amount of the required memory and processing time can be highly reduced. For N$\times$N image, if m$\times$m pixel set is assigned to each PE of P$\times$P mesh, where P=N/m, the time complexity due to the communication of each PE and the computation complexity are reduced to O(PlogP) bit operations and O(P) bit operations, respectively, which is 1/m of each of the conventional method. This method also diminishes the amount of memory in each PE to O(P), and can decrease the number of PE to O(P2) =Θ(N2/m2) as compared to O(N2) of conventional method. Because the proposed parallel labeling algorithm is scalable, we can adapt to the increase of image size without the hardware change of the given mesh connected SIMD computer.