• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.3 s.309
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• pp.67-75
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• 2006

Facial expressions exhibit not only facial feature motions, but also subtle changes in illumination and appearance. Since it is difficult to generate realistic facial expressions by using only geometric deformations, detailed features such as textures should also be deformed to achieve more realistic expression. The existing methods such as the expression ratio image have drawbacks, in that detailed changes of complexion by lighting can not be generated properly. In this paper, we propose a nonlinear model for skin color change and a model-based synthesis method for facial expression that can apply realistic expression details under different lighting conditions. The proposed method is composed of the following three steps; automatic extraction of facial features using active appearance model and geometric deformation of expression using warping, generation of facial expression using a model for nonlinear skin color change, and synthesis of original face with generated expression using a blending ratio that is computed by the Euclidean distance transform. Experimental results show that the proposed method generate realistic facial expressions under various lighting conditions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.2C
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• pp.141-149
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• 2007

Associating the shoulder line with head location of the human body is useful in verifying, localizing and tracking persons in an image. Since the head line and the shoulder line, what we call ${\Omega}$-shape, move together in a consistent way within a limited range of deformation, we can build a statistical shape model using Active Shape Model (ASM). However, when the conventional ASM is applied to ${\Omega}$-shape fitting, it is very sensitive to background edges and clutter because it relies only on the local edge or gradient. Even though appearance is a good alternative feature for matching the target object to image, it is difficult to learn the appearance of the ${\Omega}$-shape because of the significant difference between people's skin, hair and clothes, and because appearance does not remain the same throughout the entire video. Therefore, instead of teaming appearance or updating appearance as it changes, we model the discriminative appearance where each pixel is classified into head, torso and background classes, and update the classifier to obtain the appropriate discriminative appearance in the current frame. Accordingly, we make use of two features in fitting ${\Omega}$-shape, edge gradient which is used for localization, and discriminative appearance which contributes to stability of the tracker. The simulation results show that the proposed method is very robust to pose change, occlusion, and illumination change in tracking the head and shoulder line of people. Another advantage is that the proposed method operates in real time.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.35-43
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• 2010

1D PCA utilized in the face appearance-based face recognition methods such as eigenface-based face recognition method may lead to less face representative power and more computational cost due to the resulting 1D face appearance data vector of high dimensionality. To resolve such problems of 1D PCA, 2D PCA-based face recognition methods had been developed. However, the face representation model obtained by direct application of 2D PCA to a face image set includes both face common features and face distinctive identity features. Face common features not only prevent face recognizability but also cause more computational cost. In this paper, we first develope a model of a face distinctive identity feature subspace separated from the effects of face common features in the face feature space obtained by application of 2D PCA analysis. Then, a novel robust face recognition based on the face distinctive identity feature subspace model is proposed. The proposed face recognition method based on the face distinctive identity feature subspace shows better performance than the conventional PCA-based methods (1D PCA-based one and 2D PCA-based one) with respect to recognition rate and processing time since it depends only on the face distinctive identity features. This is verified through various experiments using Yale A and IMM face database consisting of face images with various face poses under various illumination conditions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.162-177
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• 2020

The perception of traffic environment based on various sensors in autonomous driving system has a direct relationship with driving safety. Recently, as the perception model based on deep neural network is used due to the development of machine learning/in-depth neural network technology, a the perception model training and high quality of a training dataset are required. However, there are several realistic difficulties to collect data on all situations that may occur in self-driving. The performance of the perception model may be deteriorated due to the difference between the overseas and domestic traffic environments, and data on bad weather where the sensors can not operate normally can not guarantee the qualitative part. Therefore, it is necessary to build a virtual road environment in the simulator rather than the actual road to collect the traning data. In this paper, a training dataset collection process is suggested by diversifying the weather, illumination, sensor position, type and counts of vehicles in the simulator environment that simulates the domestic road situation according to the domestic situation. In order to achieve better performance, the authors changed the domain of image to be closer to due diligence and diversified. And the performance evaluation was conducted on the test data collected in the actual road environment, and the performance was similar to that of the model learned only by the actual environmental data.

• Food Engineering Progress
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• v.13 no.4
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• pp.297-301
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• 2009

This study was performed to develop a prediction model of sugar content for strawberry. Near-infrared (NIR) spectroscopy has been prevailed for on-line and portable applications for non-invasive quality assessment of intact fruit. This work presents effects of illumination method and coating of reflection surface of light source on prediction result of sugar content. Effect of preprocessing methods was also examined. A low-cost commercially available VIS/NIR spectrometer was used for estimation of total soluble solids content (Brix). To predict sugar contents of strawberry, the best results were obtained with the spectrum data measured under intensive illuminations at three locations induced from the light source with fiber optic bundles. Gold coating of reflection surface of light source lamp gave favorable effect to prediction result. The best results in validation of PLSR model were $r_{SEP}$ = 0.891 and SEP = 0.443 Brix under OSC preprocessing and those of PCR were $r_{SEP}$ = 0.845, SEP $r_{SEP}$= 0.520 Brix, under no preprocessing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2895-2912
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• 2014

Content-based image retrieval has been the most important technique for managing huge amount of images. The fundamental yet highly challenging problem in this field is how to measure the content-level similarity based on the low-level image features. The primary difficulties lie in the great variance within images, e.g. background, illumination, viewpoint and pose. Intuitively, an ideal similarity measure should be able to adapt the data distribution, discover and highlight the content-level information, and be robust to those variances. Motivated by these observations, we in this paper propose a probabilistic similarity learning approach. We first model the distribution of low-level image features and derive the free energy kernel (FEK), i.e., similarity measure, based on the distribution. Then, we propose a learning approach for the derived kernel, under the criterion that the kernel outputs high similarity for those images sharing the same class labels and output low similarity for those without the same label. The advantages of the proposed approach, in comparison with previous approaches, are threefold. (1) With the ability inherited from probabilistic models, the similarity measure can well adapt to data distribution. (2) Benefitting from the content-level hidden variables within the probabilistic models, the similarity measure is able to capture content-level cues. (3) It fully exploits class label in the supervised learning procedure. The proposed approach is extensively evaluated on two well-known databases. It achieves highly competitive performance on most experiments, which validates its advantages.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.92-101
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• 2005

It is difficult to detect the accurate detection which leads the camera it moves follows in change of the noise or illumination and Also, it could be recognized with backgound if the object doesn't move during hours. In this paper, the proposed method is updating changed background image as much as N＊M pixel mask as time goes on after get a difference between imput image and first background image. And checking image pixel can efficiently detect moving by computing fixed distance pixel instead of operate all pixel. Also, set up minimum area of object to use boundary point of object abstracted through checking image pixel and motion detect of object. Therefore motion detection is available as is fast and correct without doing checking image pixel every Dame. From experiment, the designed and implemented system showed high precision ratio in performance assessment more than 90 percents.

• Design & Manufacturing
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• v.7 no.1
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• pp.55-59
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• 2013

A Light Emitting Diode(LED) is a semiconductor device which converts electricity into light. LEDs are widely used in a field of illumination, LCD(Liquid Crystal Display) backlight, mobile signals because they have several merits, such as low power consumption, long lifetime, high brightness, fast response, environment friendly. In general, LEDs production does die bonding and wire bonding on board, and do silicon and phosphor dispensing to protect LED chip and improve brightness. Then lens molding process is performed using mixed liquid silicon rubber(LSR) by resin and hardener. A mixture of resin and hardener affect the optical characteristics of the LED lens. In this paper, computational design of static mixer was performed for mixing of liquid silicon. To evaluate characteristic of mixing efficiency, finite element model of static mixer was generated, and fluidic analysis was performed according to length of mixing element. Finally, optimal condition of length of mixing element was applied to static mixer from result of fluidic analysis.

• Environmental Engineering Research
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• v.20 no.4
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• pp.347-355
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• 2015

The growth kinetics of phototrophic microorganisms can be controlled by the light irradiance, the concentration of an inorganic nutrient, or both. A multi-component kinetic model is proposed and tested in novel batch experiments that allow the kinetic parameters for each factor to be estimated independently. For the cyanobacterium Synechocystis sp. PCC6803, the estimated parameters are maximum specific growth rate $({\mu}_{max})=2.8/d$, half-maximum-rate light irradiance $(K_L)=11W/m^2$, half-inhibition-rate light irradiance $(K_{L,I})=39W/m^2$, and half-maximum-rate concentration for inorganic carbon $(K_{S,Ci})=0.5mgC/L$, half-maximum-rate concentration for inorganic nitrogen $(K_{S,Ni})=1.4mgN/L$, and half-maximum-rate concentration for inorganic phosphorus $(K_{S,Pi})=0.06mgP/L$. Compared to other phototrophs having ${\mu}max$ estimates, PCC6803 is a fast-growing r-strategist relying on reaction rate. Its half-maximum-rate and half-inhibition rate values identify the ranges of light irradiance and nutrient concentrations that PCC6803 needs to achieve a high specific growth rate to be a sustainable bioenergy source. To gain the advantages of its high maximum specific growth rate, PCC6803 needs to have moderate light illumination ($7-62W/m^2$ for ${\mu}_{syn}{\geq}1/d$) and relatively high nutrient concentrations: $N_i{\geq}2.3 mgN/L$, $P_i{\geq}0.1mgP/L$, and $C_i{\geq}1.0mgC/L$.

• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.323-331
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• 2005

This paper proposes an improved system for recognizing facial expressions in various internal states that is illumination-invariant and without detectable rue such as a neutral expression. As a preprocessing to extract the facial expression information, a whitening step was applied. The whitening step indicates that the mean of the images is set to zero and the variances are equalized as unit variances, which reduces murk of the variability due to lightening. After the whitening step, we used the facial expression information based on principal component analysis(PCA) representation excluded the first 1 principle component. Therefore, it is possible to extract the features in the lariat expression images without detectable cue of neutral expression from the experimental results, we ran also implement the various and natural facial expression recognition because we perform the facial expression recognition based on dimension model of internal states on the images selected randomly in the various facial expression images corresponding to 83 internal emotional states.