• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.445-452
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• 2008

Preliminary results are reported on ship detection using coherence images computed from cross-correlating images of multi-look-processed dual-polarization data (HH and HV) of ENVISAT ASAR. The traditional techniques of ship detection by radars such as CFAR (Constant False Alarm Rate) rely on the amplitude data, and therefore the detection tends to become difficult when the amplitudes of ships images are at similar level as the mean amplitude of surrounding sea clutter. The proposed method utilizes the property that the multi-look images of ships are correlated with each other. Because the inter-look images of sea surface are covered by uncorrelated speckle, cross-correlation of multi-look images yields the different degrees of coherence between the images and water. In this paper, the polarimetric information of ships, land and intertidal zone are first compared based on the cross-correlation between HH and HV images, In the next step, we examine the technique when the dual-polarization data are split into two multi-look images, It was shown that the inter-look cross-correlation method could be applicable in the performance improvement of small ship detection and the land masking, It was also found that a simple combination of coherence images from each co-polarised (HH) inter-look and cross-polarised (HV) inter-look data can provide much higher target-detection possibilities.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.67-73
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• 2011

The image retrieval generally shows the same or similar images to a query image as a result. In the case of rotated image, however, its performance tends to be debased significantly. We propose a method to ensure a reliable image retrieval of rotated images as follows; First, to obtain feature points of query/DB images by Harris Corner Detector; and then, utilizing the feature points, to find the object's axis and query/DB images into rotation invariant images with Principal Components Analysis algorithm. We have experimented with 6,000 natural images which are 256 pixels in diameter. They are 1,000 Wang's images and their rotated images by $30^{\circ}$, $45^{\circ}$, $90^{\circ}$, $135^{\circ}$ and $180^{\circ}$. The simulation results show that the proposed method retrieves rotated images more effectively than the conventional method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.49-57
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• 2012

An image reconstruction algorithm is proposed to estimate transmitted original images from images displayed on a video terminal. The proposed algorithm acquires images that are displayed on video terminal screens by using a camera. The transmitted images are then estimated with the acquired images. However, camera-acquired images exhibit geometric and color distortions caused by characteristics of the camera and display devices. We make use of a geometric distortion correction algorithm that exploits homography and color distortions using a weighted-linear model. The experimental results show that the proposed algorithm yields promising estimation performance with respect to the peak signal-to-noise ratio (PSNR). PSNR values of the estimated images with respect to the corresponding original images range from 28-29 dB.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.669-680
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• 2012

Noise removal of document images is a necessary step during preprocessing to recognize characters effectively because it has influences greatly on processing speed and performance for character recognition. We have considered using the spatial filters such as traditional mean filters and Gaussian filters, and wavelet transformed based methods for noise deduction in natural images. However, these methods are not effective for the noise removal of document images. In this paper, we present noise removal of document images using support vector regression. The proposed approach consists of two steps which are SVR training step and SVR test step. We construct an optimal prediction model using grid search with cross-validation in SVR training step, and then apply it to noisy images to remove noises in test step. We evaluate our SVR based method both quantitatively and qualitatively for noise removal in Korean, English and Chinese character documents, and compare it to some existing methods. Experimental results indicate that the proposed method is more effective and can get satisfactory removal results.

• Imaging Science in Dentistry
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• v.32 no.1
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• pp.19-25
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• 2002

Purpose: To evaluate the degree of accuracy of DentaScan reformatted images of the maxillary sinus and periapical, periodontal lesions and to clarify the usefulness of the reconstructed 3-dimensional images to the dental clinical aspects. Materials and Methods: 33 sides of maxillae of the hemi-sectioned Korean heads were used in this study. Periapical radiographs, computed tomography and DentaScan reformatted cross-sectional images were taken for the radiographic evaluation of the peiapical and peiodontal lesions of the maxillary teeth and inferior wall of maxillary sinus. Results : Compared the degree of accuracy and findings of dental and periapical pathoses on the intraoral radiographs and DentaScan reformatted images with the cross-sectioned specimens, the DentaScan reformatted cross-sectional images were more accurate and more effective than the intraoral radiography with a viewpoint of the detection of dental and periapical pathoses. Conclusion: Comparing the lesions of specimens with intraoral radiographies and DentaScan reformatted images, the dental and periodontal pathoses and topographical structures were more clearly observed in the DentaScan reformatted images, providing the possibility of more applications of reformatted images to clinical dentistry.

• Journal of Astronomy and Space Sciences
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• v.33 no.4
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• pp.313-318
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• 2016

Communication, Ocean And Meteorological Satellite (COMS) Meteorological Imager (MI) images are processed for radiometric and geometric correction from raw image data. When intermediate image data are matched and compared with reference landmark images in the geometrical correction process, various techniques for edge detection can be applied. It is essential to have a precise and correct edged image in this process, since its matching with the reference is directly related to the accuracy of the ground station output images. An edge detection method based on neural networks is applied for the ground processing of MI images for obtaining sharp edges in the correct positions. The simulation results are analyzed and characterized by comparing them with the results of conventional methods, such as Sobel and Canny filters.

• Journal of the Korea Society of Computer and Information
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• v.20 no.7
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• pp.25-32
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• 2015

In this paper, we propose a license plate detection method of running cars in various road images. The proposed method first classifies the road image into day and night images to improve detection accuracy, and then the tail-light regions are detected by finding red color areas in RGB color space. The candidate regions of the license plate areas are detected by using symmetrical property, size, width and variance of the tail-light regions, and to find the license plate areas of the various sizes the morphological operations with adaptive size structuring elements are applied. Finally, the plate area is verified and confirmed with the geometrical and image features of the license plate areas. The proposed method was tested with the various road images and the detection rates (precisions) of 84.2% of day images and 87.4% of night images were achieved.

• Journal of Information Technology Applications and Management
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• v.29 no.2
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• pp.1-15
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• 2022

Image has more power in the 21st century than ever before. With the advance in technology and media, image is either made or received more easily and promptly using digital technology. Because of that, it has been hard for digital images to gain value as a work of art. But after NFT technology was developed and then applied to digital images, we have come to distinguish the original from the copy of it. People are trying various ways to allow digital images to be acknowledged as art, and changing the nature of digital images that makes new paradigms in the history of art. Through NFT artworks rising as a new style, this paper analyzed and investigated the new properties and changing characteristics of digital images in order to anticipate the characteristics of artworks that the public produces, enjoys, and consumes. This study is grounded on experimental analysis methods, and this author expects that it can contribute to figuring out which kinds of visual culture and artworks digital image is influencing currently from various perspectives.

• Journal of the Korea Institute of Building Construction
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• v.16 no.5
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• pp.471-477
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• 2016

The construction industry is very important to supply the facilities and infrastructure in Korea. However, the images of construction industry are negative in actual. In addition, the studies to improve the images of construction industry up to now have a limitation to research the images about that time. So, in this study, the authors examined and analyzed the peoples' trend changes of the images of construction industry with time. The results showed that the positive images of construction industry about the contribution to national economy and the future prospect are diminished. And it is revealed that the negative images of construction industry about the morality are decreased, too. The authors proposed the strategies to improve the images of construction industry by the based on these results.

• Journal of Information Technology Applications and Management
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• v.25 no.2
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• pp.41-52
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• 2018

This paper presents a method for accurate camera self-calibration based on SIFT Feature Detection and image quality assessment. We performed image quality assessment to select high quality images for the camera self-calibration process. We defined high quality images as those that contain little or no blur, and have maximum contrast among images captured within a short period. The image quality assessment includes blur detection and contrast assessment. Blur detection is based on the statistical analysis of energy and standard deviation of high frequency components of the images using Discrete Cosine Transform. Contrast assessment is based on contrast measurement and selection of the high contrast images among some images captured in a short period. Experimental results show little or no distortion in the perspective view of the images. Thus, the suggested method achieves camera self-calibration accuracy of approximately 93%.