• Journal of the Korean association of regional geographers
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• v.12 no.4
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• pp.496-507
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• 2006

A hybrid approach integrating both high-resolution and hyperspectral data sets was used to map vegetation cover of a coastal lowland area in the Hawaii Volcanoes National Park. Three common grass species (broomsedge, natal redtop, and pili) and other non-grass species, primarily shrubs, were focused in the study. A 3-step, hybrid approach, combining an unsupervised and a supervised classification schemes, was applied to the vegetation mapping. First, the IKONOS 1-m high-resolution data were classified to create a binary image (vegetated vs. non--vegetated) and converted to 20-meter resolution percent cover vegetation data to match AVIRIS data pixels. Second, the minimum noise fraction (MNF) transformation was used to extract a coherent dimensionality from the original AVIRIS data. Since the grasses and shubs were sparsely distributed and most image pixels were intermingled with lava surfaces, the reflectance component of lava was filtered out with a binary fractional cover analysis assuming that tile total reflectance of a pixel was a linear combination of the reflectance spectra of vegetation and the lava surface. Finally, a supervised approach was used to classify the plant species based on tile maximum likelihood algorithm.

• The KIPS Transactions:PartA
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• v.10A no.3
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• pp.207-214
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• 2003

Quadtree, which is a hierarchical data structure, is a very important data structure to represent binary images. The linear quadtree representation as a way to store a quadtree is efficient to save space compared with other representations. Therefore, it has been widely studied to develop efficient algorithms to execute operations related with quadtrees. The linear translation is one of important operations in image processing, which moves the image by a given distance. In this paper, we present an algorithm to perform the linear translation of binary images represented by quadtrees, using three-dimensional $n{\times}n{\times}n$ processors on RMESH (Reconfigurable MESH). This algorithm has constant-time complexity by using efficient basic operations to route the locational codes of quardtree on the hierarchical structure of n${\times}$n${\times}$n RMESH.

• Nuclear Engineering and Technology
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• v.46 no.1
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• pp.109-116
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• 2014

An electrical resistance tomography (ERT) technique combining the particle swarm optimization (PSO) algorithm with the Gauss-Newton method is applied to the visualization of two-phase flows. In the ERT, the electrical conductivity distribution, namely the conductivity values of pixels (numerical meshes) comprising the domain in the context of a numerical image reconstruction algorithm, is estimated with the known injected currents through the electrodes attached on the domain boundary and the measured potentials on those electrodes. In spite of many favorable characteristics of ERT such as no radiation, low cost, and high temporal resolution compared to other tomography techniques, one of the major drawbacks of ERT is low spatial resolution due to the inherent ill-posedness of conventional image reconstruction algorithms. In fact, the number of known data is much less than that of the unknowns (meshes). Recalling that binary mixtures like two-phase flows consist of only two substances with distinct electrical conductivities, this work adopts the PSO algorithm for mesh grouping to reduce the number of unknowns. In order to verify the enhanced performance of the proposed method, several numerical tests are performed. The comparison between the proposed algorithm and conventional Gauss-Newton method shows significant improvements in the quality of reconstructed images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.45-48
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• 2019

Detection of crosswalk is an important issue for the blind to walk without the help of others. There is a braille block on the sidewalk, which helps the blind to walk. On the other hand, crosswalk is more dangerous due to the moving vehicles. However, there is no appropriate means to induce the blind. In this paper, we propose a method to detect crosswalk in front of a blind and estimate its direction using an image sensor. We adopt multi-ROIs and make their binary versions. In order to determine whether it is a crosswalk, two features are extracted; one is the number of crossing in the binary image and the other is the ratio of white area. We can also estimate the direction of the crosswalk through the slope of the projection data. We evaluated the performance using experimental dataset and the proposed algorithm showed 80% accuracy of detection.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.27-34
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• 2019

In this paper, we propose a method to remove the background area by analyzing the pattern of the character area. In the character detection result of the MSER(Maximally Stable External Regions) method which distinguishes a region having a constant contrast background regions were detected. To solve this problem, we use the MSER method in natural images, the background is removed by calculating the change rate by searching the character area and the background area which are not different from the areas where the contrast values are different from each other. However, in the background removed image, using the LBP(Local Binary Patterns) method, the area with uniform values in the image was determined to be a character area and character detection was performed. Experiments were carried out with simple images with backgrounds, images with frontal characters, and images with slanted images. The proposed method has a high detection rate of 1.73% compared with the conventional MSER and MSER + LBP method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.933-943
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• 2022

As the Internet develops and the utilization rate of computers increases, the threats posed by malware keep increasing. This leads to the demand for a system to automatically analyzes a large amount of malware. In this paper, an automatic malware analysis technique using a deep learning algorithm is introduced. Our proposed method uses CNN (Convolutional Neural Network) to analyze the malicious features represented as images. To reflect semantic information of malware for detection, our method uses the opcode frequency data of binary for image generation, rather than using bytes of binary. As a result of the experiments using the datasets consisting of 20,000 samples, it was found that the proposed method can detect malicious codes with 91% accuracy.

• Journal of radiological science and technology
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• v.41 no.5
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• pp.463-469
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• 2018

Morphological information such as shape and margin of micro lesion is important information for diagnosis of disease in clinical imaging. In this study, we investigated the morphological changes of the micro lesions by comparing the contrast and area in grid suppressed DR images according to grid frequency. In the profile analysis of the image, the mass showed an average intensity variation of 8.6 ~ 72.4 after suppression, The higher the grid frequency, the more the contrast was increased. However, in the images obtained using 103 lp / inch, which is a grid frequency less than the sampling frequency, the contrast of the mass in the vertical direction decreased after suppression. In the binary image, the area change of the mass was also large. As a result, the shape, size, and margin of the mass changed. In the case of very small calcification, the higher the grid frequency is the larger the change in contrast, so that a clear image can be obtained in the post-suppression image. However, we could confirm that the margin of the lesion was blurred and the lesion was lost in some of the images using the 103 lp / inch grid. The higher the frequency of the grid, The change of the contrast of fiber occurred largely and clear boundary was confirmed. The decrease of the number of pixels was small and morphological change was small. In conclusion, when using a grid frequency that is not suitable for the sample frequency, morphological changes or lesion loss of micro lesions in the post- suppression image may give the possibility of misdiagnosis in diagnosis and differentiation of the image.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.120-123
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• 2003

In this paper, a digital watermarking scheme whichis effective in protecting a copyright of video image under an image transformation and impulse noise is proposed. The proposing scheme is to use a coded watermark that insert the personal ID of copyrighter. The recovery ability is improved by the coded watermark. Also the coded watermark is abel to trace the illegal distributors. Binary image is used as watermark image, the value of PSNR and recovered rates of watermark are obtained in order to confirm the required invisibility and robustness in watermark system. The experimental results show that image quality is less degraded as the PSNR of 98.21 ㏈. It is also observed that excellent watermark recovery is achieved under the image transformation and impulse noise.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.626-629
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• 2004

Texture imaging, which means texture image creation by co-occurrence relation, has been known as one of useful image analysis methodologies. For this purpose, most commercial remote sensing software provides texture analysis function named GLCM (Grey Level Co-occurrence Matrix). In this study, texture-imaging program for GLCM algorithm is newly implemented in the MS Visual IDE environment. While, additional texture imaging modules based on GLDV (Grey Level Difference Vector) are contained in this program. As for GLCM/GLDV texture variables, it composed of six types of second order texture function in the several quantization levels of 2(binary image), 8, and 16: Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment, and Contrast. As for co-occurrence directionality, four directions are provided as $E-W(0^{\circ}),\;N-E(45^{\circ}),\;S-W(135^{\circ}),\;and\;N-S(90^{\circ}),$ and W-E direction is also considered in the negative direction of E- W direction. While, two direction modes are provided in this program: Omni-mode and Circular mode. Omni-mode is to compute all direction to avoid directionality problem, and circular direction is to compute texture variables by circular direction surrounding target pixel. At the second phase of this study, some examples with artificial image and actual satellite imagery are carried out to demonstrate effectiveness of texture imaging or to help texture image interpretation. As the reference, most previous studies related to texture image analysis have been used for the classification purpose, but this study aims at the creation and general uses of texture image for urban remote sensing.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.4 s.34
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• pp.3-9
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• 2005

The extraction of traffic information based on image processing is under broad research recently because the method based on image processing takes less cost and effort than the traditional method based on physical equipment. The main purpose of the algorithm based on image processing is to extract vehicles from an image correctly. Before the extraction, the algorithm needs the pre-processing such as background subtraction and binary image thresholding. During the pre-processing much noise is brought about because roadside tree and passengers in the sidewalk as well as vehicles are extracted as traffic flow. The noise undermines the overall accuracy of the algorithm. In this research, most of the noise could be removed by extracting the exact road area which does not include sidewalk or roadside tree. To extract the exact road area, traffic lanes in the image were used. Algorithm speed also increased. In addition, with the ratio between the sequential images, the problem caused by vehicles' shadow was minimized.