• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.995-1010
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• 2020

In this study, a multivariate analysis model of partial least square regression (PLSR) was developed to predict the moisture content of green peppers using hyperspectral imaging (HSI). In HSI, illumination is essential for high-quality image acquisition and directly affects the analytical performance of the visible near-infrared hyperspectral imaging (VIS/NIR-HSI) system. When green pepper images were acquired using a direct lighting system, the specular reflection from the surface of the objects and their intensities fluctuated with time. The images include artifacts on the surface of the materials, thereby increasing the variability of data and affecting the obtained accuracy by generating false-positive results. Therefore, images without glare on the surface of the green peppers were created using a polarization filter at the front of the camera lens and by exposing the polarizer sheet at the front of the lighting systems simultaneously. The results obtained from the PLSR analysis yielded a high determination coefficient of 0.89 value. The regression coefficients yielded by the best PLSR model were further developed for moisture content mapping in green peppers based on the selected wavelengths. Accordingly, the polarization filter helped achieve an uniform illumination and the removal of gloss and artifact glare from the green pepper images. These results demonstrate that the HSI technique with a polarized lighting system combined with chemometrics can be effectively used for high-throughput prediction of moisture content and image-based visualization.

• Journal of Korea Multimedia Society
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• v.11 no.5
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• pp.610-622
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• 2008

Moving object segmentation is an essential technique for various video surveillance applications. The result of moving object segmentation often contains shadow regions caused by the color difference of shadow pixels. Hence, moving object segmentation is usually followed by a shadow elimination process to remove the false detection results. The common assumption adopted in previous works is that, under the illumination variation, the value of chromaticity components are preserved while the value of intensity component is changed. Hence, color transforms which separates luminance component and chromaticity component are usually utilized to remove shadow pixels. In this paper, various color spaces (YCbCr, HSI, normalized rgb, Yxy, Lab, c1c2c3) are examined to find the most appropriate color space for shadow elimination. So far, there have been some research efforts to compare the influence of various color spaces for shadow elimination. However, previous efforts are somewhat insufficient to compare the color distortions under illumination change in diverse color spaces, since they used a specific shadow elimination scheme or different thresholds for different color spaces. In this paper, to relieve the limitations of previous works, (1) the amount of gradients in shadow boundaries drawn to uniform colored regions are examined only for chromaticity components to compare the color distortion under illumination change and (2) the accuracy of background subtraction are analyzed via RoC curves to compare different color spaces without the problem of threshold level selection. Through experiments on real video sequences, YCbCr and normalized rgb color spaces showed good results for shadow elimination among various color spaces used for the experiments.

• Journal of Biomedical Engineering Research
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• v.27 no.3
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• pp.125-130
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• 2006

The number of the living endothelial cells and the shape of those are very import clinical parameters for the evaluation of the quality of cornea. In this paper, we developed the automated endothelial cell counting and shape analysis algorithm for a confocal microscope. Since, the endothelial images from the confocal microscope has a non-uniform illumination and low contrast between cell boundaries and cell bodies, it is very difficult to segment the cells from the endothelial images. To cope with these difficulties, we proposed the new two stage image processing algorithm. At first stage algorithm, we used a high-pass filter and histogram equalization to compensate the non-uniform brightness pattern and a morphological filter and a watershed method are applied to detect the boundary of cells. From this stage, we could count the number of cells in an endothelial image. At second stage algorithm, we used a Voronoi diagram method to classify the shape of cells. This cell shape analysis and the percent of hexagonal cells are very sensitive in detecting the early endothelium damage. To evaluate the performance of the proposed system, we p개cessed seven endothelial images obtained using a confocal microscope. The proposed system correctly counted 95.5% cells and classified 92.0% of hexagonal cell shapes. This result is better than any others in this research area.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1705-1720
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• 2013

Human face gender recognition requires fast image processing with high accuracy. Existing face gender recognition methods used traditional local features and machine learning methods have shortcomings of low accuracy or slow speed. In this paper, a new framework for face gender recognition to reach fast face gender recognition is proposed, which is based on Local Ternary Pattern (LTP) and Extreme Learning Machine (ELM). LTP is a generalization of Local Binary Pattern (LBP) that is in the presence of monotonic illumination variations on a face image, and has high discriminative power for texture classification. It is also more discriminate and less sensitive to noise in uniform regions. On the other hand, ELM is a new learning algorithm for generalizing single hidden layer feed forward networks without tuning parameters. The main advantages of ELM are the less stringent optimization constraints, faster operations, easy implementation, and usually improved generalization performance. The experimental results on public databases show that, in comparisons with existing algorithms, the proposed method has higher precision and better generalization performance at extremely fast learning speed.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.6
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• pp.604-608
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• 2008

The TFT-LCD image allows non-uniform illumination variation and that is one of main difficulties of finding defect region. The SQI (self quotient image) has the HPF (high pass filter) shape and is used to reduce low frequency-lightness component. In this paper, we proposed the TFT-LCD defect-enhancement algorithm using characteristics of the SQI, that is the SQI has low-frequency flattening effect and maintains local variation. The proposed method has superior flattening effect and defect-enhancement effect compared with previous the TFT-LCD image preprocessing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2141-2155
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• 2020

In order to meet the requirements of background change, illumination variation, moving shadow interference and high accuracy in object detection of moving camera, and strive for real-time and high efficiency, this paper presents an object detection algorithm based on sparse approximation recursion and sparse coding migration in subspace. First, low-rank sparse decomposition is used to reduce the dimension of the data. Combining with dictionary sparse representation, the computational model is established by the recursive formula of sparse approximation with the video sequences taken as subspace sets. And the moving object is calculated by the background difference method, which effectively reduces the computational complexity and running time. According to the idea of sparse coding migration, the above operations are carried out in the down-sampling space to further reduce the requirements of computational complexity and memory storage, and this will be adapt to multi-scale target objects and overcome the impact of large anomaly areas. Finally, experiments are carried out on VDAO datasets containing 59 sets of videos. The experimental results show that the algorithm can detect moving object effectively in the moving camera with uniform speed, not only in terms of low computational complexity but also in terms of low storage requirements, so that our proposed algorithm is suitable for detection systems with high real-time requirements.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.802-811
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• 1996

Farmers need alternatives for weed control due to the desire to reduce chemicals used in farming. However, conventional mechanical cultivation cannot selectively remove weeds located in the seedline between crop plants and there are no selective heribicides for some crop/weed situations. Since hand labor is costly , an automated weed control system could be feasible. A robotic weed control system can also reduce or eliminate the need for chemicals. Currently no such system exists for removing weeds located in the seedline between crop plants. The goal of this project is to build a real-time , machine vision weed control system that can detect crop and weed locations. remove weeds and thin crop plants. In order to accomplish this objective , a real-time robotic system was developed to identify and locate outdoor plants using machine vision technology, pattern recognition techniques, knowledge-based decision theory, and robotics. The prototype weed control system is composed f a real-time computer vision system, a uniform illumination device, and a precision chemical application system. The prototype system is mounted on the UC Davis Robotic Cultivator , which finds the center of the seedline of crop plants. Field tests showed that the robotic spraying system correctly targeted simulated weeds (metal coins of 2.54 cm diameter) with an average error of 0.78 cm and the standard deviation of 0.62cm.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.156-158
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• 2007

The radiance observed from the ocean depends on the illumination and viewing geometry along with the water properties, and this variation is called the bidirectional effect which is important to be considered in ocean color remote sensing. In the present study, as a preliminary step, the spectral-angular radiances in coastal water were investigated with experiments for a range of viewing geometric conditions $(0-70^{\circ})$. Over a phytoplankton-dominated water surface the upward radiance for visible and near-infrared wavelengths (example, SeaWiFS and GOCI) increased at nadir and decreased toward the near-horizon, becoming dependent of viewing angles (with higher radiance at nadir view angle and lower radiance at near-horizon viewing angle). This variations were better expressed by the Q-factor, which relates upwelling radiance to the upwelling irradiance (i.e., $Q=E_u/L_u$, also dependent on Sun's position). The Q-factor for this case was more non-uniform with the considered wavelengths and was dependent on viewing geometric conditions. These experimental results confirm the previous similar findings in other coastal waters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.837-856
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• 2016

Underwater image enhancement has received considerable attention in last decades, due to the nature of poor visibility and low contrast of underwater images. In this paper, we propose a new automatic underwater image enhancement algorithm, which combines nonsubsampled contourlet transform (NSCT) domain enhancement techniques with the mechanism of the human visual system (HVS). We apply the multiscale retinex algorithm based on the HVS into NSCT domain in order to eliminate the non-uniform illumination, and adopt the threshold denoising technique to suppress underwater noise. Our proposed algorithm incorporates the luminance masking and contrast masking characteristics of the HVS into NSCT domain to yield the new HVS-based NSCT. Moreover, we define two nonlinear mapping functions. The first one is used to manipulate the HVS-based NSCT contrast coefficients to enhance the edges. The second one is a gain function which modifies the lowpass subband coefficients to adjust the global dynamic range. As a result, our algorithm can achieve contrast enhancement, image denoising and edge sharpening automatically and simultaneously. Experimental results illustrate that our proposed algorithm has better enhancement performance than state-of-the-art algorithms both in subjective evaluation and quantitative assessment. In addition, our algorithm can automatically achieve underwater image enhancement without any parameter tuning.

• Journal of the Korean Solar Energy Society
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• v.27 no.2
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• pp.1-9
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• 2007

Recently, the point of view about housing environment in the city has changed from the traditional point of view of the center of housing to the subject of land- utilization-control for interaction of buildings. Right of light is the center of this issue in other words. Also many interests about the beauties of the city have increasing centering around Europe etc. This is to change a city design into the characteristic design from the exiting uniform design. As if reflect this situation, recently we are setting up the night illumination and constructing a building which acted as Land Mark like the Jong-Ro Tower. And Apartment Housing was being built various form deviate from a existing standardized form and skyline. Existing studies about sunshine of Apartment Housing have dealt with just about a standardized Apartment Housing form. So this study analyzed a recently increasing interest for Right of light and change of sunshine environment on Apartment Housing which have a various skyline form.