• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.690-693
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• 2021

경찰 수사에서 진술의 진실 여부를 인공지능 기법을 이용하여 판단하는 연구는 인적, 물적 자원의 낭비를 줄일 수 있다. 우리는 진술 동영상에서 이미지, 음성 데이터를 각각 추출하여 동시에 고려해 진술의 진실 여부를 자동으로 판단하는 시스템을 제안하였다. 실험을 통해 제안된 시스템이 유의미함을 알 수 있었다.

• Korean Journal of Forensic Psychology
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• v.11 no.2
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• pp.173-190
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• 2020

The purpose of this study is to verify whether it is possible for participants to discriminate between innocent and guilty suspects when they are exposed to criminal information utilizing an autobiographical implicit association test (aIAT). A total of 49 college students were randomly assigned to guilty group, innocent-aware group, or innocent-unaware group. Participants performed an aIAT to detect suspects after performing either mock crime or control task. It was verified that innocent suspect and guilty suspect exposed with crime information could be distinguished through D-score and reaction time, converted to symbolize strength of the association between guilty sentences, innocent sentences, and truth sentences. As a result of the analysis, guilty group showed significantly higher D-score than both innocent-aware group and innocent-unaware group. guilty group also showed faster response time in true-guilty condition than true-innocent condition. This shows that the association of true-guilty conditions is stronger than that of true-innocent conditions. On the other hand, the innocent-aware group showed a faster response time in the true-innocent condition than the true-guilty condition, and innocent-unaware group showed no significant difference between the two conditions. Through this, it was confirmed that innocent suspects exposed to criminal information can be discriminated according to the aIAT pattern, which has a faster reaction rate to the truth and innocence union than the guilty group. This study confirmed that suspects exposed to criminal information can be effectively discriminated using aIAT, and further suggests the usefulness and potential of aIAT in the field of lie detection.

• International Journal of Contents
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• v.6 no.2
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• pp.1-5
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• 2010

The rapid growth in communication technology has led to the development of effective ways of sharing ideas and information in the form of speech and images. Understanding this information has become an important research issue and drawn the attention of many researchers. Text in a digital image contains much important information regarding the scene. Detecting and extracting this text is a difficult task and has many challenging issues. The main challenges in extracting text from natural scene images are the variation in the font size, alignment of text, font colors, illumination changes, and reflections in the images. In this paper, we propose a connected component based method to automatically detect the text region in natural images. Since text regions in mages contain mostly repetitions of vertical strokes, we try to find a pattern of closely packed vertical edges. Once the group of edges is found, the neighboring vertical edges are connected to each other. Connected regions whose geometric features lie outside of the valid specifications are considered as outliers and eliminated. The proposed method is more effective than the existing methods for slanted or curved characters. The experimental results are given for the validation of our approach.

• Korean Journal of Culture and Social Issue
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• v.14 no.4
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• pp.1-18
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• 2008

The present study examined the accuracy of polygraph tests through two types of statistical methods with unknown ground truth. One method evaluated the accuracy based on the rates of agreements between polygraph test results of crime suspects and prosecutors' indictment decisions for them. Those crime suspects were tested with polygraph by the Prosecutors' Office of the Republic of Korea between 2000 and 2004. The other method estimated the accuracy by using the latent class analysis based on the frequency distributions of the polygraph results and indictments during 2006. Excluding cases that were 'inconclusive' on the polygraph test, the study showed that the accuracy of the polygraph tests is .914 (SE=.004) for the 2000-2004 data, and .885 (SE=.021) for the 2006 data. With the inclusion of 'inconclusive' cases in the 2006 data, the results from the latent class analysis showed the accuracy in the range between .707 and .734 (SE=.027~.031), with false positives between .078 and .087 (SE=.019~.023), and false negatives between .029 and .078 (SE=.010~.023). The probability that the polygraph test correctly classifies subjects appeared to be in the range between .912 and .925 (SE=.013-.016) for those who lie, and in the range between .867 to .955 (SE=.011-.040) for those who tell the truth.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.786-792
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• 2010

This paper presents a real-time lane detection method which can accurately find the lane-mark boundaries in complex road environment. Unlike many existing methods that pay much attention on the post-processing stage to fit lane-mark position among a great deal of outliers, the proposed method aims at removing those outliers as much as possible at feature extraction stage, so that the searching space at post-processing stage can be greatly reduced. To achieve this goal, a grid-based morphology operation is firstly used to generate the regions of interest (ROI) dynamically, in which a directional edge-linking algorithm with directional edge-gap closing is proposed to link edge-pixels into edge-links which lie in the valid directions, these directional edge-links are then grouped into pairs by checking the valid lane-mark width at certain height of the image. Finally, lane-mark colors are checked inside edge-link pairs in the YUV color space, and lane-mark types are estimated employing a Bayesian probability model. Experimental results show that the proposed method is effective in identifying lane-mark edges among heavy clutter edges in complex road environment, and the whole algorithm can achieve an accuracy rate around 92% at an average speed of 10ms/frame at the image size of $320{\times}240$.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1543-1561
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• 2020

Taxonomy of human brain activity is a complicated rather challenging procedure. Due to its multifaceted aspects, including experiment design, stimuli selection and presentation of images other than feature extraction and selection techniques, foster its challenging nature. Although, researchers have focused various methods to create taxonomy of human brain activity, however use of multivariate pattern analysis (MVPA) for image recognition to catalog the human brain activities is scarce. Moreover, experiment design is a complex procedure and selection of image type, color and order is challenging too. Thus, this research bridge the gap by using MVPA to create taxonomy of human brain activity for different categories of images, both colored and gray scale. In this regard, experiment is conducted through EEG testing technique, with feature extraction, selection and classification approaches to collect data from prequalified criteria of 25 graduates of University Technology PETRONAS (UTP). These participants are shown both colored and gray scale images to record accuracy and reaction time. The results showed that colored images produces better end result in terms of accuracy and response time using wavelet transform, t-test and support vector machine. This research resulted that MVPA is a better approach for the analysis of EEG data as more useful information can be extracted from the brain using colored images. This research discusses a detail behavior of human brain based on the color and gray scale images for the specific and unique task. This research contributes to further improve the decoding of human brain with increased accuracy. Besides, such experiment settings can be implemented and contribute to other areas of medical, military, business, lie detection and many others.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.4
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• pp.145-154
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• 2008

In this paper, we propose the pet robot control system using hand gesture recognition in image sequences acquired from a camera affixed to the pet robot. The proposed system consists of 4 steps; hand detection, feature extraction, gesture recognition and robot control. The hand region is first detected from the input images using the skin color model in HSI color space and connected component analysis. Next, the hand shape and motion features from the image sequences are extracted. Then we consider the hand shape for classification of meaning gestures. Thereafter the hand gesture is recognized by using HMMs (hidden markov models) which have the input as the quantized symbol sequence by the hand motion. Finally the pet robot is controlled by a order corresponding to the recognized hand gesture. We defined four commands of sit down, stand up, lie flat and shake hands for control of pet robot. And we show that user is able to control of pet robot through proposed system in the experiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.251-256
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• 2008

Many researchers are studying about human Brain-Computer Interface(BCI) that it based on electroencephalogram(EEG) signals of multichannel. The researches of EEG signals are used for detection of a seizure or a epilepsy and as a lie detector. The researches about an interface between Brain and Computer have been studied robots control and game of using human brain as engineering recently. Especially, a field of brain studies used EEG signals is put emphasis on EEG artifacts elimination for correct signals. In this paper, we measure EEG signals as human emotions and divide it into five frequence parts. They are calculated related the percentage of selecting range to total range. the calculating values are compared standard values by Bayesian Network. lastly, we show the human face avatar as human Emotion.

• Bulletin of the Korean Chemical Society
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• v.31 no.10
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• pp.2813-2818
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• 2010

A novel method was developed for the speciation of chromium in natural water samples based on homogeneous liquid-liquid extraction and determination by flame atomic absorption spectrometry (FAAS). In this method, Cr(III) reacts with a new Schiff's base ligand to form the hydrophobic complex, which is subsequently entrapped in the sediment phase, whereas Cr(VI) remained in aqueous phase. The Cr(VI) assay is based on its reduction to Cr(III) by the addition of sodium sulfite to the sample solution. Thus, separation of Cr(III) and Cr(VI) could be realized. Homogeneous liquid-liquid extraction based on the pH-independent phase-separation process was investigated using a ternary solvent system (water-tetrabutylammonium ion ($TBA^+$)-chloroform) for the preconcentration of chromium. The phase separation phenomenon occurred by an ion-pair formation of TBA and perchlorate ion. Then sedimented phase was separated using a $100\;{\mu}L$ micro-syringe and diluted to 1.0 mL with ethanol. The sample was introduced into the flame by conventional aspiration. After the optimization of complexation and extraction conditions such as pH = 9.5, [ligand] = $1.0{\times}10^{-4}\;M$, [$TBA^+$] = $2.0{\times}10^{-2}\;M$, [$CHCl_3$] = $100.0\;{\mu}L$ and [$ClO_4$] = $2.0{\times}10{-2}\;M$, a preconcentration factor (Va/Vs) of 100 was obtained for only 10 mL of the sample. The relative standard deviation was 2.8% (n = 10). The limit of detection was sufficiently low and lie at ppb level. The proposed method was applied for the extraction and determination of chromium in natural water samples with satisfactory results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.1
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• pp.12-22
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• 2016

Convenience and safety are the key words for the automotive driving and various sensor technologies have been studied for enhanced perception of driving environments. In frequency modulated continuous wave (FMCW) radar systems, single antenna is enough for range and velocity detection of multiple targets. Multiple array antenna is needed for estimating direction of arrival(DoA). Using DoA estimation algorithm in time domain as in the conventional systems, it is difficult to distinguish vehicles lie in the same angle. In order to facilitate the enhanced angle estimation, DoA estimation algorithm is applied in frequency domain. In this paper, the method for applying multiple signal classification(MUSIC) algorithm in frequency domain is suggested and the performance is analyzed.