• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.559-565
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• 2013

Spatial resolution of land covers from remotely sensed images can be increased using super-resolution mapping techniques for soft-classified land cover proportions. A further development of super-resolution mapping technique is downscaling the original remotely sensed image using super-resolution mapping techniques with a forward model. In this paper, the model for increasing spatial resolution of remote sensing multispectral image is tested with real SPOT 5 imagery at 10m spatial resolution for an area in Bac Giang Province, Vietnam in order to evaluate the feasibility of application of this model to the real imagery. The soft-classified land cover proportions obtained using a fuzzy c-means classification are then used as input data for a Hopfield neural network (HNN) to predict the multispectral images at sub-pixel spatial resolution. The 10m SPOT multispectral image was improved to 5m, 3,3m and 2.5m and compared with SPOT Panchromatic image at 2.5m resolution for assessment.Visually, the resulted image is compared with a SPOT 5 panchromatic image acquired at the same time with the multispectral data. The predicted image is apparently sharper than the original coarse spatial resolution image.

• Journal of IKEEE
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• v.18 no.2
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• pp.234-239
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• 2014

This paper proposes a new automatic detection method to inspect mura defects on display film surface using morphological image processing and labeling. This automatic detection method for mura defects on display films comprises 3 phases of preprocessing with morphological image processing, Gabor filtering, and labeling. Since distorted results could be obtained with the presence of non-uniform illumination, preprocessing step reduces illumination components using morphological image processing. In Gabor filtering, mura images are created with binary coded mura components using Gabor filters. Subsequently, labeling is a final phase of finding the mura defect area using the difference between large mura defects and values in the periphery. To evaluate the accuracy of the proposed detection method, detection rate was assessed by applying the method in 200 display film samples. As a result, the detection rate was high at about 95.5%. Moreover, the study was able to acquire reliable results using the Semu index for luminance mura in image quality inspection.

• Journal of the Semiconductor & Display Technology
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• v.12 no.3
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• pp.59-64
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• 2013

Accurate and robust image registration is important task in many applications such as image retrieval and computer vision. To perform the image registration, essential required steps are needed in the process: feature detection, extraction, matching, and reconstruction of image. In the process of these function, feature extraction not only plays a key role, but also have a big effect on its performance. There are two representative algorithms for extracting image features, which are scale invariant feature transform (SIFT) and speeded up robust feature (SURF). In this paper, we present and evaluate two methods, focusing on comparative analysis of the performance. Experiments for accurate and robust feature detection are shown on various environments such like scale changes, rotation and affine transformation. Experimental trials revealed that SURF algorithm exhibited a significant result in both extracting feature points and matching time, compared to SIFT method.

• Imaging Science in Dentistry
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• v.31 no.1
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• pp.1-7
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• 2001

Purpose : To evaluate the usefulness of the reconstructed 3-dimensional image on the personal computer in comparison with that of the CT workstation by quantitative comparison and analysis. Materials and Methods : The spiral CT data obtained from 27 persons were transferred from the CT workstation to a personal computer, and they were reconstructed as 3-dimensional image on the personal computer using V-works 2.0/sup TM/. One observer obtained the 14 measurements on the reconstructed 3-dimensional image on both the CT workstation and the personal computer. Paired Nest was used to evaluate the intraobserver difference and the mean value of the each measurement on the CT workstation and the personal computer. Pearson correlation analysis and % incongruence were also performed. Results: I-Gn, N-Gn, N-A, N-Ns, B-A, and G-Op did not show any statistically significant difference (p>0.05), B-O, B-N, Eu-Eu, Zy-Zy, Biw, D-D, Orbrd R, and L had statistically significant difference (p<0.05), but the mean values of the differences of all measurements were below 2 mm, except for D-D. The value of correlation coefficient y was greater than 0.95 at I-Gn, N-Gn, N-A, N-Ns, B-A, B-N, G-Op, Eu-Eu, Zy-Zy, and Biw, and it was 0.75 at B-O, 0.78 at D-D, and 0.82 at both Orbrd Rand L. The % incongruence was below 4% at I-Gn, N-Gn, N-A, N-Ns, B-A, B-N, G-Op, Eu-Eu, Zy-Zy, and Biw, and 7.18%, 10.78%, 4.97%, 5.89% at B-O, D-D, Orbrd Rand L respectively. Conclusion : It can be considered that the utilization of the personal computer has great usefulness in reconstruction of the 3-dimensional image when it comes to the economics, accessibility and convenience, except for thin bones and the landmarks which are difficult to be located.

• Journal of the Semiconductor & Display Technology
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• v.13 no.4
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• pp.91-96
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• 2014

Mobile image retrieval is one of the most exciting and fastest growing research fields in the area of multimedia technology. As the amount of digital contents continues to grow users are experiencing increasing difficulty in finding specific images in their image libraries. This paper proposes a new efficient and effective mobile image retrieval method that applies a weighted combination of color and texture utilizing spatial-color and second order statistics. The system for mobile image searches runs in real-time on an iPhone and can easily be used to find a specific image. To evaluate the performance of the new method, we assessed the iPhone simulations performance in terms of average precision and recall using several image databases and compare the results with those obtained using existing methods. Experimental trials revealed that the proposed descriptor exhibited a significant improvement of over 13% in retrieval effectiveness, compared to the best of the other descriptors.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.22 no.10
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• pp.864-869
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• 2009

The measurement of contact wire uplift in electric railways is one of the most important test parameters to accepting the maximum permitted speed of new electric vehicles and pantographs. The contact wire uplift can be measured over short periods when the pantograph passes monitoring stations. In this paper, a high-speed image measurement system and its image processing method are being developed to evaluate dynamic uplift of overhead contact wires caused by pantograph contact forces of Korea Tilting Train eXpress (TTX) and Korea Train eXpress (KTX). The image measurement system was implemented utilizing a high-speed CMOS (Complementary Metal Oxide Semiconductor) camera and gigabit ethernet LAN. Unlike previous systems, the uplift measurement system using high speed camera is installed on the side of the rail, making maintenance convenient. On-field verification of the uplift measurement system for overhead contact wire using high speed camera was conducted by measuring uplift of the TTX followed by operation speeds at the Honam conventional line and high-speed railway line. The proposed high-speed image measurement system to evaluate dynamic uplift of overhead contact wires shows promising on-field applications for high speed trains such as KTX and TTX.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.2
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• pp.35-47
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• 2018

The 4th industrial revolution, digital image technology has developed beyond the limit of multimedia industry to advanced IT fusion and composite industry. Particularly, application technology related to HCI element algorithm in 3D image object recognition field is actively developed. 3D image object recognition technology evolved into intelligent image sensing and recognition technology through 3D modeling. In particular, image recognition technology has been actively studied in image processing using object recognition recognition processing, face recognition, object recognition, and 3D object recognition. In this paper, we propose a research method of human factor 3D image recognition technology applying human factor algorithm for 3D object recognition. 1. Methods of 3D object recognition using 3D modeling, image system analysis, design and human cognitive technology analysis 2. We propose a 3D object recognition parameter estimation method using FACS algorithm and optimal object recognition measurement method. In this paper, we propose a method to effectively evaluate psychological research techniques using 3D image objects. We studied the 3D 3D recognition and applied the result to the object recognition element to extract and study the characteristic points of the recognition technology.

• Journal of the Korean Society of Costume
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• v.50 no.8
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• pp.43-55
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• 2000

The objectives of this study were to investigate the differences of image evaluation of baseball uniform by uniform design and perceiver's gender. and to examine how baseball uniform preferences vary according to perceiver's gender. Stimuli consisted of 12 color photographs of a male model wearing a baseball uniforms which were manipulated according to the color of shorts and pants. A semantic differential scale of 23 items were used to evaluate the image of the stimuli. Subjects were 288 males and females. Five dimensions derived to account for the image of baseball uniform. These were manly, ability, activity, preference, and visibility. Wearing of red shirts had a positive effect on the evaluation of ability, activity, and visibility. Dark blue shirts had a positive effect on the evaluation of preference. Grey uniforms had negative effects on the evaluation of ability, activity, and visibility. Men liked white uniforms and vertical stripes uniforms of black and white more than dud women. Women talked dark blue shirts more than did men.

• Journal of radiological science and technology
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• v.44 no.4
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• pp.327-333
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• 2021

Magnetic resonance imaging(MRI) uses strong magnetic field to image the cross-section of human body and has excellent image quality with no risk of radiation exposure. Because of above-mentioned advantages, MRI has been widely used in clinical fields. However, the noise generated in MRI degrades the quality of medical images and has a negative effect on quick and accurate diagnosis. In particular, examining a object with a detailed structure such as brain, image quality degradation becomes a problem for diagnosis. Therefore, in this study, we acquired T2 weighted 3D data of multiple sclerosis disease using BrainWeb simulation program, and used quantitative evaluation factors to find appropriate slice thickness among 1, 3, 5, and 7 mm. Coefficient of variation and contrast to noise ratio were calculated to evaluate the noise level, and root mean square error and peak signal to noise ratio were used to evaluate the similarity with the reference image. As a result, the noise level decreased as the slice thickness increased, while the similarity decreased after 5 mm. In conclusion, as the slice thickness increases, the noise is reduced and the image quality is improved. However, since the edge signal is lost due to overlapped signal, it is considered that selecting appropriate slice thickness is necessary.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.212-220
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• 2024

When a typhoon or natural disaster occurs, a significant number of orchard fruits fall. This has a great impact on the income of farmers. In this paper, we introduce an AI-based method to enhance low-quality raw images. Specifically, we focus on apple images, which are being used as AI training data. In this paper, we utilize both a basic program and an artificial intelligence model to conduct a general image process that determines the number of apples in an apple tree image. Our objective is to evaluate high and low performance based on the close proximity of the result to the actual number. The artificial intelligence models utilized in this study include the Convolutional Neural Network (CNN), VGG16, and RandomForest models, as well as a model utilizing traditional image processing techniques. The study found that 49 red apple fruits out of a total of 87 were identified in the apple tree image, resulting in a 62% hit rate after the general image process. The VGG16 model identified 61, corresponding to 88%, while the RandomForest model identified 32, corresponding to 83%. The CNN model identified 54, resulting in a 95% confirmation rate. Therefore, we aim to select an artificial intelligence model with outstanding performance and use a real-time object separation method employing artificial function and image processing techniques to identify orchard fruits. This application can notably enhance the income and convenience of orchard farmers.