• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.3
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• pp.317-327
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• 2016

The extraction of significant feature points from a video is directly associated with the suggested method's function. In particular, the occlusion regions in trees or people, or feature points extracted from the background and not from objects such as the sky or mountains are insignificant and can become the cause of undermined matching or recognition function. This paper classifies the feature points required for building recognition by using multi-frames in order to improve the recognition function(algorithm). First, through SIFT(scale invariant feature transform), the primary feature points are extracted and the mismatching feature points are removed. To categorize the feature points in occlusion regions, RANSAC(random sample consensus) is applied. Since the classified feature points were acquired through the matching method, for one feature point there are multiple descriptors and therefore a process that compiles all of them is also suggested. Experiments have verified that the suggested method is competent in its algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1853-1858
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• 2021

O-rings fill the gaps between mechanical parts. Until now, the sorting of defective products has been performed visually and manually, so classification errors often occur. Therefore, a camera-based defect classification system without human intervention is required. However, a binarization process is required to separate the required region from the background in the camera input image. In this paper, an adaptive binarization technique that considers the surrounding pixel values is applied to solve the problem that single-threshold binarization is difficult to apply due to factors such as changes in ambient lighting or reflections. In addition, the convex hull technique is also applied to compensate for the missing pixel part. And the learning model to be applied to the separated region applies the residual error-based deep learning neural network model, which is advantageous when the defective characteristic is non-linear. It is suggested that the proposed system through experiments can be applied to the automation of O-ring defect detection.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.73-79
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• 2012

This study attempts to analyze changes in land use patterns in a compound urban and agricultural city Kimje-si, using LANDSAT TM imagery and to forecast future changes accordingly. As a new approach to supervised classification, HSB(Hue, Saturation, Brightness)-transformed images were used to select training zones, and in doing so classification accuracy increased by more than 5 percent. Land use changes were forecasted by using a cellular automaton algorithm developed by applying Markov Chain techniques, and by taking into account classification results and GIS data, such as population of the pertinent region by area, DEMs, road networks, water systems. Upon comparing the results of the forecast of the land use changes, it appears that geographical features had the greatest influence on the changes. Moreover, a forecast of post-2030 land use change patterns demonstrates that 21.67 percent of mountain lands in Kimje-si is likely to be farmland, and 13.11 percent is likely to become city areas. The major changes are likely to occur in small mountain lands located in the heart of the city. Based on the study result, it seems certain that forecasting future land use changes can help plan land use in a compound urban and agricultural city to procure food resources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1173-1192
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• 2015

The Support Vector Data Description (SVDD) has achieved great success in anomaly detection, directly finding the optimal ball with a minimal radius and center, which contains most of the target data. The SVDD has some limited classification capability, because the hyper-sphere, even in feature space, can express only a limited region of the target class. This paper presents an anomaly detection algorithm for mitigating the limitations of the conventional SVDD by finding the minimum volume enclosing ellipsoid in the feature space. To evaluate the performance of the proposed approach, we tested it with intrusion detection applications. Experimental results show the prominence of the proposed approach for anomaly detection compared with the standard SVDD.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.248-250
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• 2003

An automatic approach and strategy for extracting building information from aerial images using combined image analysis and interpretation techniques is described in this paper. A dense DSM is obtained by stereo image matching. Multi-band classification, DSM, texture segmentation and Normalised Difference Vegetation Index (NDVI) are used to reveal building interest areas. Then, based on the derived approximate building areas, a shape modelling algorithm based on the level set formulation of curve and surface motion has been used to precisely delineate the building boundaries. Data fusion, based on the Dempster-Shafer technique, is used to interpret simultaneously knowledge from several data sources of the same region, to find the intersection of propositions on extracted information derived from several datasets, together with their associated probabilities. A number of test areas, which include buildings with different sizes, shape and roof colour have been investigated. The tests are encouraging and demonstrate that the system is effective for building extraction, and the determination of more accurate elevations of the terrain surface.

• The Journal of the Acoustical Society of Korea
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• v.17 no.2E
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• pp.38-46
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• 1998

In this paper, we propose a remote speech input device, a new method of user-friendly speech input in spontaneous speech recognition system. We focus the user friendliness on hands-free and microphone independence in speech recognition applications. Our method adopts two algorithms, the automatic speech detection and the microphone array delay-and-sum beamforming (DSBF)-based speech enhancement. The automatic speech detection algorithm is composed of two stages; the detection of speech and nonspeech using the pitch information for the detected speech portion candidate. The DSBF algorithm adopts the time domain cross-correlation method as its time delay estimation. In the performance evaluation, the speech detection algorithm shows within-200 ms start point accuracy of 93%, 99% under 15dB, 20dB, and 25dB signal-to-noise ratio (SNR) environments, respectively and those for the end point are 72%, 89%, and 93% for the corresponding environments, respectively. The classification of speech and nonspeech for the start point detected region of input signal is performed by the pitch information-base method. The percentages of correct classification for speech and nonspeech input are 99% and 90%, respectively. The eight microphone array-based speech enhancement using the DSBF algorithm shows the maximum SNR gaing of 6dB over a single microphone and the error reductin of more than 15% in the spontaneous speech recognition domain.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.1000-1009
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• 2007

This paper presents a content adaptive interpolation (CAI) for intra deinterlacing. The CAI consists of three steps: pre-processing, content classification, and adaptive interpolation. There are also three main interpolation methods in our proposed CAI, i.e. modified edge-based line averaging (M-ELA), gradient directed interpolation (GDI), and window matching method (WMM). Each proposed method shows different performances according to spatial local features. Therefore, we analyze the local region feature using the gradient detection and classify each missing pixel into four categories. And then, based on the classification result, a different do-interlacing algorithm is activated in order to obtain the best performance. Experimental results demonstrate that the CAI method performs better than previous techniques.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.870-878
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• 2013

In this paper, we propose a novel fingerprint classification method to enhance the accuracy and efficiency of the fingerprint identification system, one of biometrics systems. According to the previous researches, fingerprints can be categorized into the several patterns based on their pattern of ridges and valleys. After construction of fingerprint database based on their patters, fingerprint classification approach can help to accelerate the fingerprint recognition. The reason is that classification methods reduce the size of the search space to the fingerprints of the same category before matching. First, we suggest a method to extract region of interest (ROI) which have real information about fingerprint from the image. And then we propose a feature extraction method which combines gray level co-occurrence matrix (GLCM) and wavelet features. Finally, we compare the performance of our proposed method with the existing method which use only GLCM as the feature of fingerprint by using the multi-layer perceptron and support vector machine.

• The KIPS Transactions:PartB
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• v.14B no.3 s.113
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• pp.153-162
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• 2007

Sasang medicine is peculiar medicine that constitution of a human classify four types and differ treatment method by physical constitution. In this way the most important thing is very difficult problem that classification of Sasang constitution and discriminate correctly. Therefore, in this paper targets diagnosis medical appliances development of hybrid form that can behave constitution classification and sees among for this paper to propose about method to grasp characteristic that is morphology about eye, nose, ear and mouth be based on appearance and manner of speaking. In this paper, classified and verified this for Sasang constitution through the QSCC II program at 1 step and present method that more exactly and conveniently analyzing measure each physical constitution feature by survey about eye, nose, ear and mouth at 2 steps. Also, extraction and analyze and verified main area of physical constitution classification based on front face and side face at 3 steps. Such propose method to extraction the principal face region based on face color from front face and side face for correct physical constitution classification diagnosis appliance development through experiment consideration and verification process.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.100-118
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• 2015

This study established a land-cover classification method on objects using satellite images, and figured out distributional patterns of land cover according to categories through spatial statistics techniques. Object-based classification generated each land cover classification map by spectral information, texture information, and the combination of the two. Through assessment of accuracy, we selected optimum land cover classification map. Also, to figure out spatial distribution pattern of land cover according to categories, we analyzed hot spots and quantified them. Optimal weight for an object-based classification has been selected as the Scale 52, Shape 0.4, Color 0.6, Compactness 0.5, Smoothness 0.5. In case of using the combination of spectral information and texture information, the land cover classification map showed the best overall classification accuracy. Particularly in case of dry fields, protected cultivation, and bare lands, the accuracy has increased about 12 percent more than when we used only spectral information. Forest, paddy fields, transportation facilities, grasslands, dry fields, bare lands, buildings, water and protected cultivation in order of the higher area ratio of DMZ according to categories. Particularly, dry field sand transportation facilities in Yanggu occurred mainly in north areas of the civilian control line. dry fields in Cheorwon, forest and transportation facilities in Inje fulfilled actively in south areas of the civilian control line. In case of distributional patterns according to categories, hot spot of paddy fields, dry fields and protected cultivation, which is related to agriculture, was distributed intensively in plains of Yanggu and in basin areas of Cheorwon. Hot spot areas of bare lands, waters, buildings and roads have similar distribution patterns with hot spot areas related to agriculture, while hot spot areas of bare lands, water, buildings and roads have different distributional patterns with hot spot areas of forest and grasslands.