• Journal of Agricultural Extension & Community Development
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• v.20 no.4
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• pp.1181-1216
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• 2013

This study was accomplished to enhance brand value for hanwoo and to develop strategy for brand positioning that move customer's heart. This study in order to achieve the research was carried out as follows: First, the cluster analysis based on demographic characteristics for consumer on the basis of three types segmentation on market was conducted. Market A was consisted of a well-educated, high-income and young bracket. Market B was consisted of a well-educated, high-income and middle-aged bracket. Market C was consisted of a low-income and middle-aged class. Second, consumer's positioning map was analyzed based on perceiving data which are products' functional, emotional, and self-expressive benefits about consumer's feeling beef products. This study was analyzed each relative brand advantage and structure of competition on segmented market by conjoining each brands positioning map and feature vectors map. By the result of the analysis, each brand's positioning strategy was devised. As a result of the study, the hoengseong hanwoo is competitive about all kinds of market. We chooses that hoengseong hanwoo's target is A market, because that brand is evaluated as a high-ranked quality by high-class image of luxury price, quality, brand image. For management improvement sake, this brand(the hoengseong hanwoo) is needed to effort for promoting consumer's awareness about safety and reliability.

• Korean Journal of Optics and Photonics
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• v.20 no.1
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• pp.16-22
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• 2009

We propose a method to convert a two-dimensional movie to a three-dimensional movie using normalized cut and optical flow. In this paper, we segment an image of a two-dimensional movie to objects first, and then estimate the depth of each object. Normalized cut is one of the image segmentation algorithms. For improving speed and accuracy of normalized cut, we used a watershed algorithm and a weight function using optical flow. We estimate the depth of objects which are segmented by improved normalized cut using optical flow. Ordinal depth is estimated by the change of the segmented object label in an occluded region which is the difference of absolute values of optical flow. For compensating ordinal depth, we generate the relational depth which is the absolute value of optical flow as motion parallax. A final depth map is determined by multiplying ordinal depth by relational depth, then dividing by average optical flow. In this research, we propose the two-dimensional/three-dimensional movie conversion method which is applicable to all three-dimensional display devices and all two-dimensional movie formats. We present experimental results using sample two-dimensional movies.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.359-371
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• 2011

Edge detection in images is an important step in image segmentation and object recognition as preprocessing for image processing. This paper presents a new edge detection using local histogram analysis based on wavelet transform. In this work, the wavelet transform uses three components (horizontal, vertical and diagonal) to find the magnitude of the gradient vector, instead of the conventional approach in which tw components are used. We compare the magnitude of the gradient vector with the threshold that is obtained from a local histogram analysis to conclude that an edge is present or not. Some experimental results for our edge detector with a Sobel, Canny, Scale Multiplication, and Mallat edge detectors on sample images are given and the performances of these edge detectors are compared in terms of quantitative and qualitative measures. Our detector performs better than the other wavelet-based detectors such as Scale Multiplication and Mallat detectors. Our edge detector also preserves a good performance even if the Sobel and Canny detector are sharply low when the images are highly corrupted.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.75-87
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• 2011

This paper presents a method for region-based building extraction from high resolution satellite images(HRSI) using integrated information of spectral and color invariant features without user intervention such as selecting training data sets. The purpose of this study is also to evaluate the effectiveness of the proposed method by applying to IKONOS and QuickBird images. Firstly, the image is segmented by the MSRG method. The vegetation and shadow regions are automatically detected and masked to facilitate the building extraction. Secondly, the region merging is performed for the masked image, which the integrated information of the spectral and color invariant features is used. Finally, the building regions are extracted using the shape feature for the merged regions. The boundaries of the extracted buildings are simplified using the generalization techniques to improve the completeness of the building extraction. The experimental results showed more than 80% accuracy for two study areas and the visually satisfactory results obtained. In conclusion, the proposed method has shown great potential for the building extraction from HRSI.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.1
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• pp.10-21
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• 2009

In order to enhance the contrast in the regions where the pixels have similar intensities, this paper presents a new histogram equalization scheme. Conventional global equalization schemes over-equalizes those regions so that too bright or dark pixels are resulted and local equalization schemes produce unexpected discontinuities at the boundaries of the blocks. The proposed algorithm segments the original histogram into sub-histograms with reference to brightness level and equalizes each sub-histogram with the limited extents of equalization considering its mean and variance. The final image is determined as the weighted sum of the equalized images obtained by using the sub-histogram equalizations. By limiting the maximum and minimum ranges of equalization operations on individual sub-histograms, the over-equalization effect is eliminated. Also the result image does not miss feature information in low density histogram region since the remaining these area is applied separating equalization. This paper includes how to determine the segmentation points in the histogram. The proposed algorithm has been tested with more than 100 images having various contrast in the images and the results are compared to the conventional approaches to show its superiority.

• Journal of the Korea Society of Computer and Information
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• v.4 no.2
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• pp.120-134
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• 1999

Image processing steps consist of image acquisition, pre-processing, region segmentation and recognition, and the images are easily corrupted by noise during the data transmission, data capture, and data processing. Impulse noise and gaussian noise are major noises, which can occur during the process. Many filters such as mean filter, median filter, weighted median filter, Cheikh filter, and Kyu-cheol Lee filter were proposed as spatial noise reduction filters so far. Many researches have been focused on the reduction of impulse noise, but comparatively the research in the reduction of gaussian noise has been neglected. For the reduction of gaussian noise, subset averaged median filter, using median information and subset average information of pixels in a window. was proposed. At this time, consider of the window size as 3$^{*}$3 pixel. The window is divided to 4 subsets consisted of 4 pixels. First of all, we calculate the average value of each subset, and then find the median value by sorting the average values and center pixel's value. In this paper, a better reduction of gaussian noise was proved. The proposed algorithms were implemented by ANSI C language on a Sun Ultra 2 for testing purposes and the effects and results of the filter in the various levels of noise and images were proposed by comparing the values of PSNR, MSE, and RMSE with the value of the other existing filtering methods.thods.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.1-14
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• 2017

A land cover map provides basic information to help understand the current state of a region, but its utilization in the ecological research field has deteriorated due to limited temporal and spatial resolutions. The purpose of this study was to investigate the possibility of using a land cover map with data based on high resolution images acquired by UAV. Using the UAV, 10.5 cm orthoimages were obtained from the $2.5km^2$ study area, and land cover maps were obtained from object-based and pixel-based classification for comparison and analysis. From accuracy verification, classification accuracy was shown to be high, with a Kappa of 0.77 for the pixel-based classification and a Kappa of 0.82 for the object-based classification. The overall area ratios were similar, and good classification results were found in grasslands and wetlands. The optimal image segmentation weights for object-based classification were Scale=150, Shape=0.5, Compactness=0.5, and Color=1. Scale was the most influential factor in the weight selection process. Compared with the pixel-based classification, the object-based classification provides results that are easy to read because there is a clear boundary between objects. Compared with the land cover map from the Ministry of Environment (subdivision), it was effective for natural areas (forests, grasslands, wetlands, etc.) but not developed areas (roads, buildings, etc.). The application of an object-based classification method for land cover using UAV images can contribute to the field of ecological research with its advantages of rapidly updated data, good accuracy, and economical efficiency.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12A
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• pp.2109-2119
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• 2001

In this paper, a fast information extraction algorithm for object-based MPEG-4 application from MPEG-2 bit-steam is proposed. For object-based MPEG-4 conversion, we need to extract such information as object-image, shape-image, macro-block motion vector, and header information from MPEG-2 bit-stream. If we use the extracted information, fast conversion for object-based MPEG-4 is possible. The proposed object extraction algorithm has two important steps, namely the motion vectors extraction from MPEG-2 bit-stream and the watershed algorithm. The algorithm extracts objects using user\`s assistance in the intra frame and tracks then in the following inter frames. If we have an unsatisfactory result for a fast moving object, the user can intervene to correct the segmentation. The proposed algorithm consist of two steps, which are intra frame object extracts processing and inter frame tracking processing. Object extracting process is the step in which user extracts a semantic object directly by using the block classification and watersheds. Object tacking process is the step of the following the object in the subsequent frames. It is based on the boundary fitting method using motion vector, object-mask, and modified watersheds. Experimental results show that the proposed method can achieve a fast conversion from the MPEG-2 bit-stream to the object-based MPEG-4 input.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.9
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• pp.794-806
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• 2017

It is essential to protect the national space assets and space environment safely as a space development country from the continuously increasing space debris. And Active Debris Removal(ADR) is the most active way to solve this problem. In this paper, we studied the Artificial Neural Network(ANN) for a stable recognition model of vision-based space debris tracking system. We obtained the simulated image of the space environment by the KARICAT which is the ground-based space debris clearing satellite testbed developed by the Korea Aerospace Research Institute, and created the vector which encodes structure and color-based features of each object after image segmentation by depth discontinuity. The Feature Vector consists of 3D surface area, principle vector of point cloud, 2D shape and color information. We designed artificial neural network model based on the separated Feature Vector. In order to improve the performance of the artificial neural network, the model is divided according to the categories of the input feature vectors, and the ensemble technique is applied to each model. As a result, we confirmed the performance improvement of recognition model by ensemble technique.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.91-102
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• 2004

In this paper, a fast information extraction algorithm for object-based MPEG-4 application from MPEG-1,2 is proposed. For object-based MPEG-4 conversion, we need to extract such information as object-image, shape-image, macro-block motion vector, and header information from MPEG-1,2 bit-stream. If we use the extracted information, fast conversion for object-based MPEG-4 is possible. The proposed object extraction algerian has two important steps, namely the motion vector extraction from MPEG-1,2 bit-stream and the watershed algerian The algorithm extracts objects using user's assistance in the intra frame and tracks then in the following inter frames. If we have an unsatisfactory result for a fast moving object the user can intervene to connect the segmentation. The proposed algorithm consist of two steps, which are intra frame object extracting processing and inter frame tracking processing. Object extracting process is the step in which user extracts a semantic object directly by using the block classification and watersheds. Object tracking process is the step of the following the object in the subsequent frames. It is based on the boundary fitting method using motion vector, object-mask and modified watersheds. Experimental results show that the proposed method can achieve a fast conversion from the MPEG-1,2 bit-stream to the object-based MPEG-4 input.