• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.81-84
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• 2000

Digital Halftoning convert a continuous-tone images to a binary images. Inverse halftoning addresses the problem of recovering a continuous image from a halftoned binary image. Simple low pass filtering can remove the high frequency noise but it also removes the edge information. Thus the edge information should be separated from the halftoning noise. As a result, the edge of result image is blurring. This paper present that we obtain continuous-tone-image which using Anisotropic diffusion filter. To reduce noise without blurring the edges of reconstructed image use edge map. The experimental results show that proposed method gives a higher PSNR and better subjective quality than conventional methods. As a result, the edge information of reconstructed image reduce blurring.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1268-1271
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• 2003

In the edge detection of RS image, the useful detail losing and the spurious edge often appear. To solve the problem, we use the dyadic wavelet to detect the edge of surface features by combining the edge detecting with the multi-resolution analyzing of the wavelet transform. Via the dyadic wavelet decomposing, we obtain the RS image of a certain appropriate scale, and figure out the edge data of the plane and the upright directions respectively, then work out the grads vector module of the surface features, at last by tracing them we get the edge data of the object therefore build the RS image which obtains the checked edge. This method can depress the effect of noise and examine exactly the edge data of the object by rule and line. With an experiment of a RS image which obtains an airport, we certificate the feasibility of the application of dyadic wavelet in the object edge detection.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1464-1466
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• 2003

classification of the satellite images provides information about land cover and/or land use. Quality of the classification result depends mainly on the spatial and spectral resolutions of the images. In this study, image fusion in terms of resolution merging, and band integration with multi-source of the satellite images; Landsat ETM+ and Ikonos were carried out to improve classification. Resolution merging and band integration could generate imagery of high resolution with more spectral bands. Precise image co-registration is required to remove geometric distortion between different sources of images. Combination of unsupervised and supervised classification of the fused imagery was implemented to improve classification. 3D display of the results was possible by combining DEM with the classification result so that interpretability could be improved.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1365-1367
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• 2003

A new method, called dynamic space projection method that is suitable to remote sensing image, is adopted to encrypt GCP (ground control point) datum in this paper. The essence of this method is to encrypt enough GCP by using a few known GCP in order to realize the precise correction of remote sensing image. By making use of the method to the GCP datum encrypting and precise geometric correction of TM image and SPOT image, the precision of encrypted GCP is less than one pixel, the precision of precisely corrected image is less than two pixels.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.5
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• pp.89-95
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• 2008

The Acousto-Optic Tunable Filter (AOTF) is a high-speed full-field monochromator which generates two spectrally filtered light beams with ordinary and extraordinary polarization state. Thus, AOTF is widely used to build full-field spectral imaging system or spectral domain interferometer. However, AOTF has a big problem that the angle of diffracted light changes according to the scanning of wavelength, which makes image shift on CCD plane In this paper, we propose an analytic design of prism system to compensate the image shift. The detailed analysis of light paths in a prism and basic experimental results are presented to verify our proposed compensation method. The experimental results agree with simulation results based on suggested prism model and image shift is minimized at optimal condition. Also, it can be extended to compensate the image shift for ordinary and extraordinary polarized light simultaneously.

• Journal of the Semiconductor & Display Technology
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• v.9 no.3
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• pp.47-51
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• 2010

This paper presents a wafer pre-alignment system which is improved using the image of the entire wafer area. In the previous method, image acquisition for wafer takes about 80% of total pre-alignment time. The proposed system uses only one image of entire wafer area via a high-resolution CMOS camera, and so image acquisition accounts for nearly 1% of total process time. The larger FOV(field of view) to use the image of the entire wafer area worsen camera lens distortion. A camera calibration using high order polynomials is used for accurate lens distortion correction. And template matching is used to find a correct notch's position. The performance of the proposed system was demonstrated by experiments of wafer center alignment and notch alignment.

• Journal of the Optical Society of Korea
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• v.5 no.2
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• pp.33-36
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• 2001

Common methods of determining the spatial resolution of fiber-optic image guides are by measuring the diameter of individual microfibers or by the use of a resolution test target. However these methods cannot provide enough information of spatial resolution in ultrathin fiber-optic image guides. In this study, a simple method to measure the modulation transfer function (MTF) of an mage guide was developed. The MTFs of ultrathin image guides with 3 and 4${\mu}{\textrm}{m}$ Um diameter were measured by examining transmitted sharp edge image. This method should be especially useful for measuring spatial resolution of ultrahigh resolution image guides with less than 5 ${\mu}{\textrm}{m}$ diameter microfibers because their spatial resolution cannot be determined by individual microfiber diameter due to crosstalk and leaky ray phenomena.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.317-322
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• 2018

We use edge detection technique for the input image to extract the entire edges of the object in the image and then select only the edges that construct the outline of the object. By examining the positional relation between these pixels composing the outline, a simplified version of the outline of the object in the input image is generated by removing unnecessary pixels while maintaining the condition of connection of the outline. For each pixel constituting the outline, its direction is calculated by examining the positional relation with the next pixel. Then, we group the consecutive pixels with same direction into one and then change them to a line segment instead of a point. Among those line segments composing the outline of the object, a line segment whose length is smaller than a predefined minimum length of acceptable line segment is removed by merging it into one of the adjacent line segments. As a result, an outline composed of line segments of over a certain length is obtained through this process.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.1
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• pp.17-27
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• 2021

In this paper, we develop a galaxy image classification method based on hand-crafted features and machine learning techniques. Additionally, we provide an empirical analysis to reveal which combination of the techniques is effective for galaxy image classification. To achieve this, we developed a framework which consists of four modules such as preprocessing, feature extraction, feature post-processing, and classification. Finally, we found that the best technique for galaxy image classification is a method to use a median filter, ORB vector features and a voting classifier based on RBF SVM, random forest and logistic regression. The final method is efficient so we believe that it is applicable to embedded environments.

• Journal of IKEEE
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• v.26 no.4
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• pp.639-646
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• 2022

In this paper, to solve the large dataset problem, we collect images through an image collection method called web crawling and build datasets for use in image classification models through a data preprocessing process. We also propose a lightweight model that can automatically classify images by adding category values by incorporating transfer learning into the image classification model and an image classification model that reduces training time and achieves high accuracy.