• Journal of Information Processing Systems
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• v.16 no.1
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• Science of Emotion and Sensibility
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• v.15 no.1
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• pp.149-160
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• 2012

The relative effects of individual differences in cultural orientation (individualism vs. collectivism) and locus of control (LOC: internal vs. external control beliefs) combination types on global/local processing were compared by manipulating the compound stimulus types (arrows or letters), and the stimulus-stimulus congruence. The results can be summarized as followings. First, consistent with previous research on global/local processing of the compound stimuli, reaction time (RT) for global stimuli than for local stimuli, and that in the stimulus-stimulus congruent condition than in the stimulus-stimulus incongruent condition was faster. Second, faster RT was found in the compound arrows condition than in the compound letters. Third, individual difference in LOC, rather than that in the cultural orientations, appeared to be related to global precedence effect, when the compound letters were presented. These results indicated that the individual's LOC rather than cultural orientation can increase the size of the global precedence effect, which might be involved in the stage of cognitive analysis than that of feature detection.

• Journal of Broadcast Engineering
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• v.12 no.6
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• pp.574-584
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• 2007

As the real time multimedia service become more popular, the needs of transmission with low delay and high quality are getting more stronger. Among those video compression technologies, the rate control method dose an important role in getting the effective data transmitting and the high image quality. In this paper, we combined the feature of CBR and VBR coding methods to propose a new bit-rate control method witch allows each frame to generates bits in the defined fluctuation range and applies a scene change detection at a part of frame and also can maintain low-delay and high quality even if the perfect VBR transmission environment is not guaranteed. The experiment result shows the proposed algorithm provides more effective method than TMN8 in real time application.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.693-696
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• 2006

In this study, we made the system to measure variation of blood flow using bio-electrical impedance analysis method. The system, which could measure variation of impedance according to pressure change by artificial pressure, consists of pressure measurement and impedance measurement by 4-electrode method. Pressure measurement splits into semiconducting pressure sensor and electronic circuit for processing output signal. In addition, impedance measurement splits into constant current source circuit and lock-in amplifier for detection impedance signal. We experimented feature of impedance measurement using standard resistance to evaluate the system characteristic. As well as, we experimented to estimate variation of blood flow by measuring impedance and blood flow resistance ratio using mean arterial pressure and variation of blood flow with experimental group. As result of this study, blood flow resistance ratio and variation of blood flow were definitely in inverse proportion and were -0.96776 as correlation coefficient by correlation analysis.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.3
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• pp.139-147
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• 2007

In military transportation, the use of wide trailer for transporting the large and heavy weight equipments such as tank, armoured vehicle, and mobile gunnery is quite common. So, the vulnerability of causing traffic accidents for these wide military trailer to bump or collide with another car in adjacent lane is very high due to its broad width in excess of its own lane's width. Also, the possibility of these strayed accidents can be increased especially by the careless driver. In this paper, the recognition system of lane and vehicle headway direction is developed to detect the possible collision and warn the driver to prevent the fatal accident. In the system development, Kalman filtering is used first to extract the border of driving lane from the video images supplied by the CCD camera attached to the vehicle and the driving lane detection is completed with regression analysis. Next, the vehicle headway direction is recognized by using neural network scheme with the extracted parameters of the detected driving lane feature. The practical experiments for the developed system are also carried out in the real traffic road of Seoul city area and the results show us the more than 90% accuracy in recognizing the driving lane and vehicle headway direction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.434-442
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• 2018

In this paper, we propose a road lane color recognition method from the image obtained from a driving vehicle. In autonomous vehicle techniques, lane information becomes more important as the level of autonomous driving such as lane departure warning and dynamic lane keeping assistance is increased. In particular the lane color recognition, especially the white and the yellow lanes, is necessary technique because it is directly related to traffic accidents. In this paper, color information of lane and road area is mapped to a 2-dimensional S-color space based on lane detection. And the center of the feature distribution is obtained by using an improved mean-shift algorithm in the S-color space. The lane color is determined by using the distance between the center coordinates of the color features of the left and right lanes and the road area. In various illumination conditions, about 97% color recognition rate is achieved.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.189-202
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• 2004

In this paper, we propose a new salient region detection method in an image. The algorithm is based on the characteristics of human's bottom-up visual attention. Several features known to influence human visual attention like color, intensity and etc. are extracted from the each regions of an image. These features are then converted to importance values for each region using its local competition function and are combined to produce a saliency map, which represents the saliency at every location in the image by a scalar quantity, and guides the selection of attended locations, based on the spatial distribution of saliency region of the image in relation to its Perceptual importance. Results shown indicate that the calculated Saliency Maps correlate well with human perception of visually important regions.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.189-196
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• 2008

In this paper, a CMOS digital image sensor, which consists of A/D conversion, motion estimation circuits, and an attention module for ROI (Region of Interest) detection is presented. The functions of A/D conversion and motion estimation are implemented by $0.6{\mu}m$ CMOS processing circuit as hardware, and the attention module is implemented outside the circuit as software currently. Attention modules are taken to improve limited applications of the smart image sensor. The current smart image sensor responses to the changes of intensity, and uses the integration time to estimate motion. Therefore it is limited in its applications. To make up for inherent property of the sensor from circuit design and extend its applications we decide to introduce perception solutions to the image sensor. Attention modules for still and moving images are employed to achieve such purposes. The suggested approach makes the smart image sensor available with additional functions for such cases that motion estimation or intensity changes are not observed. Experimental result shows the usefulness and extension of the image sensor.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.3
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• pp.111-116
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• 2013

With technology scaling, soft error rate has greatly increased in embedded systems. Due to high performance and low power consumption, VLIW (Very Long Instruction Word) architectures have been widely used in embedded systems and thus many researches have been studied to improve the reliability of a system by duplicating instructions in VLIW architectures. However, existing studies have ignored the feature, called VLES (Variable Length Execution Set), which is adopted in most modern VLIW architectures to reduce code size. In this paper, we propose how to support instruction duplication in VLIW architecture with VLES. Our experimental results demonstrate that a VLIW architecture with VLES shows 64% code size decrement on average at the cost of about 4% additional cell area as compared to the case of a VLIW architecture without VLES when instruction duplication is applied to both architectures. Also, it is shown that the case with VLES does not cause extra execution time compared to the case without VLES.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.751-770
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• 2018

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.