• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.120-121
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• 2006

In this paper, We have designed a face recognition system based on the embedded Linux. This paper has an aim in embedded system to recognize the face more exactly. At first, the contrast of the face image is adjusted with lightening compensation method, the skin and lip color is founded based on YCbCr values from the compensated image. To take advantage of the method based on feature and appearance, these methods are applied to the eyes which has the most highly recognition rate of all the part of the human face. For eyes detecting, which is the most important component of the face recognition, we calculate the horizontal gradient of the face image and the maximum value. This part of the face is resized for fitting the eye image. The image, which is resized for fit to the eye image stored to be compared, is extracted to be the feature vectors using the continuous wavelet transform and these vectors are decided to be whether the same person or not with PNN, to miminize the error rate, the accuracy is analyzed due to the rotation or movement of the face. Also last part of this paper we represent many cases to prove the algorithm contains the feature vector extraction and accuracy of the comparison method.

• Journal of the Korean Wood Science and Technology
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• v.44 no.4
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• pp.494-504
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• 2016

Eucalyptus oils are widely used as spices, perfume industrial materials, food flavorings, and medicines. Several types of Eucalyptus oils also have insecticidal activity and as carminative. This study investigated the chemical composition, insecticidal (larvicidal and repellent) activity of E. urophylla oil against filarial mosquito Culex quinquefasciatus. E. urophylla oil was obtained from fresh leaves by water-steam distillation with oil yield 1.08%. E. urophylla oil in this study had no color (clear), has odor (typical eucalyptus), with specific gravity 0.941; refractive index 1.465; miscibility in 70% ethanol 1 : 3; and optical rotation (-) $5.83^{\circ}$. The major compounds of the oil were ${\alpha}$-pinene (11.73%), 1,8-cineole (49.86%), ${\beta}$-ocimene (6.25%), ${\gamma}$-terpinene (9.11%), and ${\alpha}$-terpinyl acetate (7.63%). The result showed the excellent insecticide activity against C. quinquefasciatus. The oil provided larvicidal activity with $LC_{50}$: 80.21 ppm and $LC_{90}$: 210.18 ppm, and repellent activity with $IC_{50}$: 0.82% and $IC_{90}$: 4.88%. The present study showed the effectiveness of E. urophylla as natural insecticide against C. quinquefasciatus, the mosquito vector of filariasis.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2006.06a
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• pp.45-48
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• 2006

This paper presents an approach to visual information based temporal indexing of video sequences. The objective of this work is the integration of an automatic face detection and a matching system for video indexing. The face detection is done using color information. The matching stage is based on the Principal Component Analysis (PCA) followed by the Minimax Probability Machine (MPM). Using PCA one feature vector is calculated for each face which is detected at the previous stage from the video sequence and MPM is applied to these feature vectors for matching with the training faces which are manually indexed after extracting from video sequences. The integration of the two stages gives good results. The rate of 86.3% correctly classified frames shows the efficiency of our system.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.64-64
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• 2011

We present a new quasi-stellar object (QSO) selection algorithm using a Support Vector Machine, a supervised classification method, on a set of extracted time series features including period, amplitude, color, and autocorrelation value. We train a model that separates QSOs from variable stars, non-variable stars, and microlensing events using 58 known QSOs, 1629 variable stars, and 4288 non-variables in the MAssive Compact Halo Object (MACHO) database as a training set. To estimate the efficiency and the accuracy of the model, we perform a cross-validation test using the training set. The test shows that the model correctly identifies ~80% of known QSOs with a 25% false-positive rate. The majority of the false positives are Be stars. We applied the trained model to the MACHO Large Magellanic Cloud (LMC) data set, which consists of 40 million lightcurves, and found 1620 QSO candidates. During the selection, none of the 33,242 known MACHO variables were misclassified as QSO candidates. In order to estimate the true false-positive rate, we crossmatched the candidates with astronomical catalogs including the Spitzer Surveying the Agents of a Galaxy's Evolution (SAGE) LMC catalog and a few X-ray catalogs. The results further suggest that the majority of the candidates, more than 70%, are QSOs.

• Korean Journal of Computational Design and Engineering
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• v.22 no.1
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• pp.37-43
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• 2017

3D printing techniques can be used in various application fields and many researches have been reported. FDM (Fused Deposition modeling) can make multi-material or multi-color models with the simultaneous use of two or more filaments. In a dual-nozzle FDM printers, while the active nozzle is working, the remaining nozzle will be idle. The remaining molten resins inside an idle nozzle can ooze out unwantedly. The spill over from the resting nozzle produces unwanted remaining on the fabricated product. In this research, we suggest a method for optimizing building position of a model to minimize the unwanted spill-over that could possibly contaminate the final product. The method is based on minimizing the two intersection volumes. The first intersection volume is obtained by intersecting the volume defined by the first material and the Minkowski sum between the volume of the first material and the vector obtained by subtracting the center point of the first nozzle from the center point of the second nozzle. The second intersection volume can be obtained by reversing the role of the first and second volumes and nozzles. Some results obtained from the implementation using the Parasolid (Siemens) geometric modeling kernel is presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8B
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• pp.1391-1399
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• 2000

In this paper, we discussed the region-based image retrieval method using image segmentation. We proposed a segmentation method which can reduce the effect of a irregular light sources. The image segmentation method uses a region-merging, and candidate regions which are merged were selected by the energy values of high frequency bands in discrete wavelet transform. The content-based image retrieval is executed by using the segmented region information, and the images are retrieved by a color, texture, shape feature vector. The similarity measure between regions is processed by the Euclidean distance of the feature vectors. The simulation results shows that the proposed method is reasonable.

• The Journal of the Acoustical Society of Korea
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• v.13 no.2
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• pp.50-59
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• 1994

This paper presents a speech recognition method utilizing third-order cumulants as a feature vector and a neural network for recognition. The use of higher-order cumulants provides desirable uncoupling between the gaussian noise and speech, which enables us to estimate the coefficients of AR model without bias. Unlike the conventional method using second-order statistics, the proposed one exhibits low bias even in SNR as low as 0 dB at the expense of higher variance. It is confirmed through computer simulation that recognition rate of korean single-vowels with the cumulant-based method is much higher than the results with the conventional method even in low SNR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2078-2093
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• 2019

Visual smoke recognition is a challenging task due to large variations in shape, texture and color of smoke. To improve performance, we propose a novel smoke recognition method by combining dual-encoded features that are extracted from both spatial and Curvelet domains. A Curvelet transform is used to filter an image to generate fifty sub-images of Curvelet coefficients. Then we extract Local Binary Pattern (LBP) maps from these coefficient maps and aggregate histograms of these LBP maps to produce a histogram map. Afterwards, we encode the histogram map again to generate Dual-encoded Local Binary Patterns (Dual-LBP). Histograms of Dual-LBPs from Curvelet domain and Completed Local Binary Patterns (CLBP) from spatial domain are concatenated to form the feature for smoke recognition. Finally, we adopt Gaussian Kernel Optimization (GKO) algorithm to search the optimal kernel parameters of Support Vector Machine (SVM) for further improvement of classification accuracy. Experimental results demonstrate that our method can extract effective and reasonable features of smoke images, and achieve good classification accuracy.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.49-60
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• 2019

Plants are very crucial for life on Earth. There is a wide variety of plant species available, and the number is increasing every year. Species knowledge is a necessity of various groups of society like foresters, farmers, environmentalists, educators for different work areas. This makes species identification an interdisciplinary interest. This, however, requires expert knowledge and becomes a tedious and challenging task for the non-experts who have very little or no knowledge of the typical botanical terms. However, the advancements in the fields of machine learning and computer vision can help make this task comparatively easier. There is still not a system so developed that can identify all the plant species, but some efforts have been made. In this study, we also have made such an attempt. Plant identification usually involves four steps, i.e. image acquisition, pre-processing, feature extraction, and classification. In this study, images from Swedish leaf dataset have been used, which contains 1,125 images of 15 different species. This is followed by pre-processing using Gaussian filtering mechanism and then texture and color features have been extracted. Finally, classification has been done using Multiclass-support vector machine, which achieved accuracy of nearly 93.26%, which we aim to enhance further.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4043-4064
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• 2021

Night-time image quality evaluation is an urgent requirement in visual inspection. The lighting environment of night-time results in low brightness, low contrast, loss of detailed information, and colour dissonance of image, which remains a daunting task of delicately evaluating the image quality at night. A new blind quality assessment metric is presented for realistic night-time scenario through a comprehensive consideration of contrast, texture, and colour in this article. To be specific, image blocks' color-gray-difference (CGD) histogram that represents contrast features is computed at first. Next, texture features that are measured by the mean subtracted contrast normalized (MSCN)-weighted local binary pattern (LBP) histogram are calculated. Then statistical features in Lαβ colour space are detected. Finally, the quality prediction model is conducted by the support vector regression (SVR) based on extracted contrast, texture, and colour features. Experiments conducted on NNID, CCRIQ, LIVE-CH, and CID2013 databases indicate that the proposed metric is superior to the compared BIQA metrics.