• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.10-17
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• 2015

Emotional computer that you want to study in a regular number change is the continuing sensitivity. Emotional Computing manner the sensibilities numbered and emotions were running through the trees. Emotional assessment of emotional sensibility computing was used as the coordinates of the key effects of the James A. Russell (Core Affect). Emotional tree runs purpose was to verify the correlation of sensitivity and emotion computing tree. Emotional tree attributes experiment color, brightness, saturation was configured with. When 50% brightness increase, about pleasure (X-axis) has increased by 10.49 points. Brightness 50%, GREEN 50% increase in the degree of pleasure (X-axis) of 10.49 points, tone (Y axis) has increased by 15.85 points. Brightness 50%, GREEN 50% increase in the degree of pleasure (X-axis) of 10.49 points, tone (Y axis) has increased by 15.85 points. Brightness 50% of the free-extent (X-axis), BLUE 50% when the tone (Y axis), pleasure extent (X-axis) of 10.49 points, tone (Y axis) as much as 14.65 points sensibilities have changed. When representatives emotions size changes have increased 50% brightness, color RED 50%, increased 5.4% Emotional excitement, emotion depressed declined -4.2%. 50% brightness, color GREEN 50% increase in emotional excitement had increased to 8.6%, declined by -5.5% this melancholy sensibility. Representative emotion and emotional changes increase or decrease the size of the emotional attributes were analyzed by quantitative methods. After the happy emotions number is needed to study more similar to the human emotion through the execution of the video image emotion emotional tree computing.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1071-1081
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• 2021

Remote Sensing using unmanned aerial vehicles(UAV) can acquire images with higher time resolution and spatial resolution than aerial and satellite remote sensing. However, UAV images are photographed at low altitude and the area covered by one image isrelatively narrow. Therefore multiple images must be processed to monitor large area. Since UAV images are photographed under different exposure conditions, there is difference in brightness values between adjacent images. When images are mosaicked, unnatural seamlines are generated because of the brightness difference. Therefore, in order to generate seamless mosaic image, a radiometric processing for correcting difference in brightness value between images is essential. This paper proposes a relative radiometric calibration and image blending technique. In order to analyze performance of the proposed method, mosaic images of UAV images in agricultural and mountainous areas were generated. As a result, mosaic images with mean brightness difference of 5 and root mean square difference of 7 were avchieved.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.105-111
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• 2018

This paper presents the analysis of image quality with FHD(Full HD) resolution camera equipped DIC(Digital Image Correlation) platform for the measurement of the architectural structure's relative displacement. DIC platform was designed based on i.MX6 of Freescale. Displacement measurement based on DIC method, the error is affected by image quality factors as pixel number, brightness, contrast, and SNR[dB](Signal to Noise Ratio). The effect were analyzed. The displacement of ROI(Region Of Interest) area within the image was measured by sub-pixel units based on DIC method. The non-contact telemetry property of DIC method, it can be used to long distance non-contact measurement. The various displacement results was measured and analyzed with the image quality factor adjustment according to the distance(25m, 35m, 50m).

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.138-145
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• 2016

LLAH(Locally Likely Arrangement Hashing) is a method which describes image features by exploiting the geometric relationship between their neighbors. Inherently, it is more robust to large view change and poor scene texture than conventional texture-based feature description methods. However, LLAH strongly requires that image features should be detected with high repeatability. The problem is that such requirement is difficult to satisfy in real applications. To alleviate the problem, this paper proposes a method that improves the matching rate of LLAH by exploiting together the brightness of features. Then, it is verified that the matching rate is increased by about 5% in experiments with synthetic images in the presence of Gaussian noise.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.12
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• pp.977-985
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• 2020

KOMPSAT-3A launched in 2015 provides Middle InfraRed(MIR) images with 3.3~5.2㎛. Though the satellite provide high resolution images for estimating bright temperature of ground objects, it is different from existing satellites developed for natural science purposes. An atmospheric compensation process is essential in order to estimate the surface brightness temperature from a single channel MIR image of KOMPSAT-3A. However, even after the atmospheric compensation process, there is a brightness temperature error due to various factors. In this paper, we analyzed the cause of the brightness temperature estimation error by tracking signal flow from camera physical characteristics to image processing. Also, we study on possibility of improvement of MIR brightness temperature bias error of KOMPSAT-3A using GEOKOMPSAT-2A. After bias compensation of a real nighttime image with a large bias error, it was confirmed that the surface brightness temperature of KOMPSAT-3A and GEOKOMPSAT-2A have correlation. We expect that the GEOKOMPSAT-2A images will be helpful to improve MIR brightness temperature bias error of KOMPSAT-3A.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1043-1064
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• 2020

A kind of Subjective Imaging Effect Assessment (SIEA) method and its applications on intelligent imaging terminal design in engineering site are presented. First, some visual assessment indices are used to characterize the imaging effect: the image brightness, the image brightness uniformity, the color image contrast, the image edge blur, the image color difference, the image saturation, the image noise, and the integrated imaging effect index. A linear weighted function is employed to carry out the SIEA computation and the Analytic Hierarchy Process (AHP) technique is used to estimate its weights. Second, a SIEA software is developed. It can play images after the settings of assessment index or assessment reaction time, etc. Third, two cases are used to illustrate the application effects of proposed method: the image enhancement system design for surveillance camera and the imaging environment perception system design for intelligent lighting terminal. A Prior Sequential Stimulus (PSS) experiment is proposed to improve the evaluation stability of SIEA method. Many experiment results have shown the proposed method can realize a stable system design or parameters setting for the intelligent imaging terminal in engineering site.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.4
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• pp.444-449
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• 2010

The PID control to the monitor color correction was applied. Converted Gamma LUT was applied to the video card. Color and brightness information from the PID controller was used as control inputs. Color and brightness information from a calibrated monitor is measured again. Then, the difference between the preset values for the PID controller was compensated by the feedback. The software measured by color meter was used for color and brightness information. All of the monitor does not measure the color and brightness was measured by sampling. The color and brightness from the monitor were measured not by complete enumeration but by sampling. In the experiment, the original image was displayed on the same monitor as well as two other brands, and then, Gamma and Contrast were measured and compared.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.6
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• pp.868-872
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• 2005

Individual management of the animal is the first step towards reaching the goal of precision livestock farming that aids animal welfare. Accurate recognition of each individual animal is important for precise management. Electronic identification of cattle, usually referred to as RFID (Radio Frequency Identification), has many advantages for farm management. In practice, however, RFID implementations can cause several problems. Reading speed and distance must be optimized for specific applications. Image processing is more effective than RFID for the development of precision farming system in livestock. Therefore, the aim of this paper is to attempt the identification of cattle by using image processing. The majority of the research on the identification of cattle by using image processing has been for the black-and-white patterns of the Holstein. But, native Japanese and Korean cattle do not have a consistent pattern on the body, so that identification by pattern is impossible. This research aims to identify to Japanese black cattle, which does not have a black-white pattern on the body, by using image processing and a neural network algorithm. 12 Japanese black cattle were tested. Values of input parameter were calculated by using the face image values of 12 cows. The face was identified by the associate neural memory algorithm, and the algorithm was verified by the transformed face image, for example, of brightness, distortion, noise and angle. As a result, there was difference due to a transformation ratio of the brightness, distortion, noise, and angle. The algorithm could identify 100% in the range from -30 to +30 degrees of brightness, -20 to +40 degrees of distortion, 0 to 60% of noise and -20 to +30 degree of angle transformed images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.540-546
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• 2019

This study suggests a binarization method that minimizes information loss for night illumination images. The object of the night illumination image is an image which is not focused due to the influence of illumination and is not identifiable. Also, the image has a brightness area in only a part of the brightness histogram. So the existing simple binarization method is hard to get good results. The proposed binarization method uses image segmentation method and image merging method. In the stepwise divided blocks, we divide into two regions using the triangular type of fuzzy logic. The value 0 of the membership degree is binarized at the present step, and the value of the membership degree 1 is binarized after the next step. Experimental results show that night illumination images with minimal loss of information can be obtained in a dark area brightness range.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.1874-1884
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• 1996

In this paper, we proposed automatic searching algorithm of thresholding value using multilevel thresholding for face segmentation from input bust image effectively. The proposed algorithm extracted the thresholding value of brightness that is formed background region, face region and hair region without illumination, background and face size from input image. The statistical projection analysis project the brightness of multilevel thresholding image into horizontal and vertical direction and decide the thresholding value of face. And the algorithm extracted elliptical type block of face from input image in order to reduce the back ground region and hair region efficiently. The proposed algorithm can reduce searching area of feature extraction and processing time for face recognication.