• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3217-3238
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• 2018

In this paper, a depth image up-sampling method is put forward by using pixel classifying and jointed bilateral filtering. By analyzing the edge maps originated from the high-resolution color image and low-resolution depth map respectively, pixels in up-sampled depth maps can be classified into four categories: edge points, edge-neighbor points, texture points and smooth points. First, joint bilateral up-sampling (JBU) method is used to generate an initial up-sampling depth image. Then, for each pixel category, different refinement methods are employed to modify the initial up-sampling depth image. Experimental results show that the proposed algorithm can reduce the blurring artifact with lower bad pixel rate (BPR).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.556-566
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• 2001

This paper proposes a motion-adaptive de-interlacing algorithm based on EBMF(Edge Based Median Filter) and AMPDF(Adaptive Minimum Pixel Difference Fillet). To compensate 'motion missing'error, which is an important factor in motion-adaptive methods, we used AMPDF which estimates an accurate value using different thresholds after classifying the input image to 4 classes. To efficiently interpolate the moving diagonal edge, we also used EBMF which selects a candidate pixel according to the edge information. Finally, we, to increase the performance, adopted an adaptive interpolation after classifying the input image to moving region, stationary region, and boundary region. Simulation results showed that the proposed method provides better performance than the existing methods.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.198-200
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• 2003

This study proved if the high resolution satellite imagery of IKONOS is suitable for preparing digital forest cover map. Three methods, the pixel based classification with maximum likelihood (PML), the segment based classification with majority principle(SMP), and the segment based classification with maximum likelihood(SML), were applied to classify and delimitate forest cover of IKONOS imagery taken in May 2000 in a forested area in the central Korea. The segment-based classification was more suitable for classifying and deliminating forest cover in Korea using IKONOS imagery. The digital forest cover map in which each class is delimitated in the form of a polygon can be prepared on the basis of the segment-based classification.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.4
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• pp.237-243
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• 2007

In this paper, we propose the Real-time Dead-Pixel Detection and Compensation System for mobile camera and its hardware architecture. The CMOS image sensors as image input devices are becoming popular due to the demand for miniaturized, low-power and cost-effective imaging systems. However a conventional Dead-Pixel Detection Algorithm is disable to detect neighboring dead pixels and it degrades image quality by wrong detection and compensation. To detect dead pixels the proposed system is classifying dead pixels into Hot pixel and Cold pixel. Also, the proposed algorithm is processing line-detector and $5{\times}5$ window-detector consecutively. The line-detector and window-detector can search dead pixels by using one-dimensional(only horizontal) method in low frequency area and two-dimensional(vertical and diagonal) method in high frequency area, respectively. The experimental result shows that it can detect 99% of dead pixels. It was designed in Verilog hardware description language and total gate count is 23K using TSMC 0.25um ASIC library.

• The Korean Journal of Ecology
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• v.26 no.2
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• pp.75-81
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• 2003

This study was performed to prove if the high resolution satellite imagery of IKONOS is suitable for preparing digital vegetation map which is becoming increasingly important in ecological science. Seven classes for forest area and five classes for non-forest area were taken for classification. Three methods, such as the pixel based classification, the segment based classification with majority principle, and the segment based classification with maximum likelihood, were applied to classify IKONOS imagery taken in April 2000. As a whole, the segment based classification shows better performance in classifying the high resolution satellite imagery of IKONOS. Through the comparison of accuracies and kappa values of the above 3 classification methods, the segment based classification with maximum likelihood was proved to be the best suitable for preparing the vegetation map with the help of IKONOS imagery. This is true not only from the viewpoint of accuracy, but also for the purpose of preparing a polygon based vegetation map. On the basis of the segment based classification with the maximum likelihood, a digital vegetation map in which each vegetation class is delimitated in the form of a polygon could be prepared.

• Annual Conference of KIPS
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• 2019.05a
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• pp.515-518
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• 2019

Semantic segmentation has remained as a challenging problem in the field of computer vision. Given the immense power of Convolution Neural Network (CNN) models, many complex problems have been solved in computer vision. Semantic segmentation is the challenge of classifying several pixels of an image into one category. With the help of convolution neural networks, we have witnessed prolific results over the time. We propose a convolutional neural network model which uses Fully CNN with deconvolutional pixel layers. The goal is to create a hierarchy of features while the fully convolutional model does the primary learning and later deconvolutional model visually segments the target image. The proposed approach creates a direct link among the several adjacent pixels in the resulting feature maps. It also preserves the spatial features such as corners and edges in images and hence adding more accuracy to the resulting outputs. We test our algorithm on Karlsruhe Institute of Technology and Toyota Technologies Institute (KITTI) street view data set. Our method achieves an mIoU accuracy of 92.04 %.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2530-2538
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• 2015

The pixel value of depth image is depth value so that different objects which are placed on nearby position have similar pixel value. Moreover, the pixels of depth image have distinct pixel values compared to adjacent pixels while those of color image has very similar values. Accordingly distorted depth image of multiview video plus depth (MVD) needs proper error concealment methods considering the characteristics of depth image when transmission errors are happened. In this paper, classifying regions of depth image to consider edge directions and then applying adaptive error concealment methods to each region are proposed. Recovered depth images utilize with multiview video data to synthesize intermediate-view point video. The synthesized view is evaluated by objective quality metrics to demonstrate proposed method performance.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.1-12
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• 2009

The correct detection of skin color is an important preliminary process in fields of face detection and human motion analysis. It is generally performed by three steps: transforming the pixel color to a non-RGB color space, dropping the illuminance component of skin color, and classifying the pixels by the skin color distribution model. Skin detection depends on by various factors such as color space, presence of the illumination, skin modeling method. In this paper we propose a 3d skin color model that can segment pixels with several ethnic skin color from images with various illumination condition and complicated backgrounds. This proposed skin color model are formed with each components(Y, Cb, Cr) which transform pixel color to YCbCr color space. In order to segment the skin color of several ethnic groups together, we first create the skin color model of each ethnic group, and then merge the skin color model using its skin color probability. Further, proposed model makes several steps of skin color areas that can help to classify proper skin color areas using small training data.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.179-190
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• 2024

This study aims to compare supervised classification methods with phenology-based approaches, specifically pixel-based and segment-based methods, for accurate crop mapping in agricultural landscapes. We utilized Sentinel-2A imagery, which provides multispectral data for accurate crop mapping. 31 normalized difference vegetation index (NDVI) images were calculated from the Sentinel-2A data. Next, we employed phenology-based approaches to extract valuable information from the NDVI time series. A set of 10 phenology metrics was extracted from the NDVI data. For the supervised classification, we employed the maximum likelihood (MaxLike) algorithm. For the phenology-based approaches, we implemented both pixel-based and segment-based methods. The results indicate that phenology-based approaches outperformed the MaxLike algorithm in regions with frequent rainfall and cloudy conditions. The segment-based phenology approach demonstrated the highest kappa coefficient of 0.85, indicating a high level of agreement with the ground truth data. The pixel-based phenology approach also achieved a commendable kappa coefficient of 0.81, indicating its effectiveness in accurately classifying the crop types. On the other hand, the supervised classification method (MaxLike) yielded a lower kappa coefficient of 0.74. Our study suggests that segment-based phenology mapping is a suitable approach for regions like South Korea, where continuous cloud-free satellite images are scarce. However, establishing precise classification thresholds remains challenging due to the lack of adequately sampled NDVI data. Despite this limitation, the phenology-based approach demonstrates its potential in crop classification, particularly in regions with varying weather patterns.

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.633-641
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• 2016

Automatic defect inspection system is composed of the step in the pre-processing, defect candidate detection, and classification. Polarizing films containing various defects should be minimized over-detection for classifying defect blobs. In this paper, we propose a defect detection algorithm using a skewness of histogram for minimizing over-detection. In order to detect up defects with similar to background pixel, we are used the characteristics of the local region. And the real defect pixels are distinguished from the noise using the probability density function. Experimental results demonstrated the minimized over-detection by utilizing the artificial images and real polarizing film images.