• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.170-175
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• 1999

Noncontact measuring methodology of 3-dimensional profile using CCD camera are very attractive because of it's high measuring speed and its's high sensitivity. Especially, when projecting a grid pattern over the object the captured image have 3 dimensional information of the object. Projection moire extract 3-D information with another grid pattern in front of CCD camera. However phase measuring profilometry(PMP) obtain similar results without additional grid pattern. In this paper, new method for grid pattern generation system by polygonal mirror and Laser Diode. This system is applied the projection moire and the PMP.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.377-378
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• 2007

This paper proposes an system to generate rolled-equivalent fingerprints by mosaicking sequential images captured by an toothless device. To capture rolled-equivalent fingerprints, previous works use multiple cameras. However, the method in this paper captures sequential fingerprint images with a single camera and mosaic the images by estimating the transform between images through optical flow.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.11
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• pp.2219-2222
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• 2009

A new touch-sensitive hydrogenated amorphous silicon(a-Si:H) display with embedded optical sensor arrays is presented. The touch-sensitive panel operation was successfully demonstrated on a prototype of 16-in. active-matrix liquid crystal display (AMLCD). The proposed system provides the finger touched point without the real-time image processing of information of the captured images. Due to the simple architecture of the system, we expect the introduction of large-area touch-sensitive display panels.

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.13-21
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• 2009

This paper describes research of image enhancement for detection of face area. Typical face recognition algorithms used fixed parameter filtering algorithms to optimize face images for the recognition process. A fixed filtering scheme introduces errors when applied to face images captured in various different environmental conditions. For acquiring face image of good quality from the image including complex background and illumination, we propose a method for image enhancement using the categories based on the image intensity values. When an image is acquired average values of image from sub-window are computed and then compared to training values that were computed during preprocessing. The category is selected and the most suitable image filter method is applied to the image. We used histogram equalization, and gamma correction filters with two different parameters, and then used the most suitable filter among those three. An increase in enrollment of filtered images was observed compared to enrollment rates of the original images.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.3
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• pp.104-113
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• 2000

In this study, we propose facial image recognition based on wavelet transform and neural network. This algorithm is proposed by following processes. First, two gray level images is captured in constant illumination and, after removing input image noise using a gaussian filter, differential image is obtained between background and face input image, and this image has a process of erosion and dilation. Second, a mask is made from dilation image and background and facial image is divided by projecting the mask into face input image Then, characteristic area of square shape that consists of eyes, a nose, a mouth, eyebrows and cheeks is detected by searching the edge of divided face image. Finally, after characteristic vectors are extracted from performing discrete wavelet transform(DWT) of this characteristic area and is normalized, normalized vectors become neural network input vectors. And recognition processing is performed based on neural network learning. Simulation results show recognition rate of 100 % about learned image and 92% about unlearned image.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.3
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• pp.224-230
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• 2002

In this paper, a counting algorithm hybridized with an adaptive automatic thresholding method based on Otsu's method and the algorithm that elongates markers obtained by the well-known watershed algorithm is proposed to enhance the exactness of the microcell counting in microscopic images. The proposed counting algorithm can be stated as follows. The transformed full image captured by CCD camera set up at microscope is divided into cropped images of m$\times$n blocks with an appropriate size. The thresholding value of the cropped image is obtained by Otsu's method and the image is transformed into binary image. The microbial cell images below prespecified pixels are regarded as noise and are removed in tile binary image. The smoothing procedure is done by the area opening and the morphological filter. Watershed algorithm and the elongating marker algorithm are applied. By repeating the above stated procedure for m$\times$n blocks, the m$\times$n segmented images are obtained. A superposed image with the size of 640$\times$480 pixels as same as original image is obtained from the m$\times$n segmented block images. By labeling the superposed image, the counting result on the image of microbial cells is achieved. To prove the effectiveness of the proposed mettled in counting the microbial cell on the image, we used Acinetobacter sp., a kind of ammonia-oxidizing bacteria, and compared the proposed method with the global Otsu's method the traditional watershed algorithm based on global thresholding value and human visual method. The result counted by the proposed method shows more approximated result to the human visual counting method than the result counted by any other method.

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.19-28
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• 2017

In this paper, we propose a method to reduce the difference in image characteristics when multiple camera images are captured for virtual space production. Sixty-four images were used by cross-mounting eight bodies and lenses, respectively. Image analysis compares and analyzes the standard deviation of the histogram and pixel distribution values. As a result of the analysis, it shows different image characteristics depending on the lens or image sensor, though it is a camera of the same model. In this paper, we have adjusted the distribution of the overall brightness value of the image to compensate for this difference. As a result, the average deviation was the maximum of (Indoor: 6.89, outdoor: 24.23), we obtained images with almost no deviation (Indoor: maximum 0.42, outdoor: maximum: 2.73). In the future, we will study and apply more accurate image analysis methods than image brightness distribution.

• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.5
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• pp.784-796
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• 2010

Consumers focus on information about the symbolic meaning of a product for highly involved and emotional products (such as cosmetic products). This study examines the effectiveness of the congruence between cosmetics advertising image and self-image on consumer attitudes. In addition, this study examines two additional moderating effects, which are 'product type' and 'message type'. For the experiment, four advertizing type factorial designs were performed. A total of 320 undergraduate female students in Korea participated in the experiment. This study captured the subjective judgments of consumers on these three comparisons in terms of advertizing attitude, brand attitude, and purchase intention. The results are as follows: First, the greater the self-congruity on cosmetic advertising, then the greater the effectiveness on advertizing attitude. Second, the increased self-congruity on cosmetic advertising did not create greater effectiveness on brand attitude. Third, increased advertizing attitudes on the congruence between cosmetics advertising image and self-image increased the effectiveness on brand attitude. Fourth, increased advertizing attitudes on the congruence between cosmetics advertising image and self-image improved the effectiveness on purchase intention. Fifth, the greater the brand attitude (on the congruence between the cosmetics advertising image and the self-image produced)increased the effectiveness for purchase intention. The results show a significant moderating role of the product type. Marketers can use the results of this study to understand the market of cosmetic products for promotion.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.84-89
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• 2014

In this paper, an image-based visual servo control strategy for tracking a target object is applied to a camera-mounted omni-directional mobile robot. In order to get target angular velocity of each wheel from image coordinates of the target object, in general, a mathematical image Jacobian matrix is built using a camera model and a mobile robot kinematics. Unlike to the well-known mathematical image Jacobian, a simple rule-based control strategy is proposed to generate target angular velocities of the wheels in conjunction with size of the target object captured in a camera image. A camera image is divided into several regions, and a pre-defined rule corresponding to the target-located image region is applied to generate target angular velocities of wheels. The proposed algorithm is easily implementable in that no mathematical description for image Jacobian is required and a small number of rules are sufficient for target tracking. Experimental results are presented with descriptions about the overall experimental system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.246-253
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• 2016

Infrared (IR) imaging has been researched for various applications such as surveillance. IR radiation has the capability to detect thermal characteristics of objects under low-light conditions. However, automatic segmentation for finding the object of interest would be challenging since the IR detector often provides the low spatial and contrast resolution image without color and texture information. Another hindrance is that the image can be degraded by noise and clutters. This paper proposes multi-level segmentation for extracting regions of interest (ROIs) and objects of interest (OOIs) in the IR scene. Each level of the multi-level segmentation is composed of a k-means clustering algorithm, an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering initializes the parameters of the Gaussian mixture model (GMM), and the EM algorithm estimates those parameters iteratively. During the multi-level segmentation, the area extracted at one level becomes the input to the next level segmentation. Thus, the segmentation is consecutively performed narrowing the area to be processed. The foreground objects are individually extracted from the final ROI windows. In the experiments, the effectiveness of the proposed method is demonstrated using several IR images, in which human subjects are captured at a long distance. The average probability of error is shown to be lower than that obtained from other conventional methods such as Gonzalez, Otsu, k-means, and EM methods.