• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.12
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• pp.2783-2797
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• 1997

This paper proposes a new motion estimation algorithm that employs matching in a variable search area. Instead of uisg a fixed search range for coarse motion estimation, we examine a varying search range, which is determined adaptively by the peak signal to noise ratio (PSNR) of the frame difference. The hexagonal matching method is one of the refined methods in image warping. It produces improved image quality, but it requires a large amount of computataions. The proposed adaptive partial matching method reduces computational complexity below about 50% of the hexagonal matching method, while maintaining the image quality comparable. The performance of two motion compensation methods, which combine the affine or bilinear transformation with the proposed motion estimation algorithm, is evaluated based on the following criteria:computtational complexity, number of coding bits, and reconstructed image quality. The quality of reconstructed images by the proposed method is substantially improved relative to the conventional BMA method, and is comparable to the full hexagonal matching method;in addition, computational complexity and the number of coding bits are reduced significantly.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.1-9
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• 2022

Ship detection in synthetic aperture radar (SAR) images is an important application in marine monitoring for the military and civilian domains. Over the past decade, object detection has achieved significant progress with the development of convolutional neural networks (CNNs) and lot of labeled databases. However, due to difficulty in collecting and labeling SAR images, it is still a challenging task to solve SAR ship detection CNNs. To overcome the problem, some methods have employed conventional data augmentation techniques such as flipping, cropping, and affine transformation, but it is insufficient to achieve robust performance to handle a wide variety of types of ships. In this paper, we present a novel and effective approach for deep SAR ship detection, that exploits label-rich Electro-Optical (EO) images. The proposed method consists of two components: a data augmentation network and a ship detection network. First, we train the data augmentation network based on conditional generative adversarial network (cGAN), which aims to generate additional SAR images from EO images. Since it is trained using unpaired EO and SAR images, we impose the cycle-consistency loss to preserve the structural information while translating the characteristics of the images. After training the data augmentation network, we leverage the augmented dataset constituted with real and translated SAR images to train the ship detection network. The experimental results include qualitative evaluation of the translated SAR images and the comparison of detection performance of the networks, trained with non-augmented and augmented dataset, which demonstrates the effectiveness of the proposed framework.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.221-232
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• 2021

This study analyzed co-registration results according to the geometric processing level of reference image, which are Level 1R and Level 1G provided from KOMPSAT-3 and KOMPSAT-3A images. We performed co-registration using each Level 1R and Level 1G image as a reference image, and Level 1R image as a sensed image. For constructing the experimental dataset, seven Level 1R and 1G images of KOMPSAT-3 and KOMPSAT-3A acquired from Daejeon, South Korea, were used. To coarsely align the geometric position of the two images, SURF (Speeded-Up Robust Feature) and PC (Phase Correlation) methods were combined and then repeatedly applied to the overlapping region of the images. Then, we extracted tie-points using the SURF method from coarsely aligned images and performed fine co-registration through affine transformation and piecewise Linear transformation, respectively, constructed with the tie-points. As a result of the experiment, when Level 1G image was used as a reference image, a relatively large number of tie-points were extracted than Level 1R image. Also, in the case where the reference image is Level 1G image, the root mean square error of co-registration was 5 pixels less than the case of Level 1R image on average. We have shown from the experimental results that the co-registration performance can be affected by the geometric processing level related to the initial geometric relationship between the two images. Moreover, we confirmed that the better geometric quality of the reference image achieved the more stable co-registration performance.

• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1102-1111
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• 2012

In this paper, we propose a robust watermarking method against geometric attack even though the watermarked image is partially damaged. This method consists of standard image normalization which transforms any image into a predefined standard image and embedding watermark in DCT domain of standard normalized image using spread spectrum technique. The proposed standard image normalization method has an improvement over existing image normalization method, so it is robust to partial damage and geometric attack. The watermark embedding method using spread spectrum technique also has a robustness to image losses such as blurring, sharpening and compressions. In addition, the proposed watermarking method does not need an original image to detect watermark, so it is useful to public watermarking applications. Several experimental results show that the proposed watermarking method is robust to partial damage and various attacks including geometric deformation.

• Journal of IKEEE
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• v.24 no.1
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• pp.147-153
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• 2020

Conventional lane detection algorithms have problems in that the detection rate is lowered in road environments having a large change in curvature and illumination. The probabilistic Hough transform method has low lane detection rate since it exploits edges and restrictive angles. On the other hand, the method using a sliding window can detect a curved lane as the lane is detected by dividing the image into windows. However, the detection rate of this method is affected by road slopes because it uses affine transformation. In order to detect lanes robustly and avoid obstacles, we propose driving assist system using semantic segmentation based on deep learning. The architecture for segmentation is SegNet based on VGG-16. The semantic image segmentation feature can be used to calculate safety space and predict collisions so that we control a vehicle using adaptive-MPC to avoid objects and keep lanes. Simulation results with CARLA show that the proposed algorithm detects lanes robustly and avoids unknown obstacles in front of vehicle.

• The Journal of the Acoustical Society of Korea
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• v.24 no.5
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• pp.238-247
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• 2005

In this Paper. we Presents subband design techniques of a compensating filter and adaptive feedback canceller for the digital hearing aid. The sensorineural hearing loss has a hearing threshold that shows a nonlinear characteristic in frequency domain. and its compensation suffers from an echo that produced by an undesired time varying feedback path. Therefore. the digital hearing aid requires the compensator that can adjust gains nonlinearly in frequency bands and eliminate the echo rapidly In the Proposed digital hearing aid. the compensating filter is designed by the adaptive system identification method in subband structure, and the adaptive feedback canceller is designed by the subband affine projection algorithm. The designed compensation filter can control the nonlinear gain in each subband respectively, therefore precise compensation is possible. And the feedback canceller using the subband adaptive filter achieves fast convergence rate. The Performances of the Proposed method are verified by computer simulations as comparing with the behaviors of the previous trials.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.6C
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• pp.834-841
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• 2004

There can be lips transformed geometrically in the lip images according to the location or the pose of camera and speaker. This transformation of the lip images changes geometric information of original lip phases. Therefore, for enhancing global lip information by using partial information of lips to correct lip phases transformed geometrically, in this paper we propose a method that can geometrically correct lips. The method is composed of two steps - the feature-deciding step and the correcting step. In the former, it is for us to extract key points and features of source image according to the its lip model and to create that of target image according to the its lip model. In the latter, we decide mapping relation after partition a source and target image based on information extracted in the previous step into each 4 regions. and then, after mapping, we unite corrected sub-images to a result image. As experiment image, we use fames that contain pronunciation on short vowels of the Korean language and use lip symmetry for evaluating the proposed algorithm. In experiment result, the correcting rate of the lower lip than the upper lip and that of lips moving largely than little was highly enhanced.

• Journal of Korea Multimedia Society
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• v.3 no.1
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• pp.34-40
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• 2000

'[n this paper, we propose an efficient multi-view image coding algorithm which finds the optimal texture from a restricted number of multi-view image. The X-Y plane of the normalized object space is divided into the triangular patches. The depth of each node is determined by appling a block based disparity compensation method. Thereafter the texture of each patch is extracted by appling an affine transformation based disparity compensation method to the multi-view images. We reduced the number of images needed to determine the texture compared to traditional methods which use all the multi-view image in the texture extraction. The experimental results show that the SNR of images encoded by the proposed algorithm is better than that of images encoded by the traditional method by the approximately 0.2dB for the test sets of multi -view image called dragon, santa, city and kid. Image data recovered after encoding by the proposed method show a better visual results than after using traditional method.

• The KIPS Transactions:PartA
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• v.10A no.4
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• pp.357-368
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• 2003

Evaluation of shape similarity for 3D models is essential in many areas - medicine, mechanical engineering, molecular biology, etc. Moreover, as 3D models are commonly used on the Web, many researches have been made on the classification and retrieval of 3D models. In this paper, we describe methods for 3D shape representation and major concepts of similarity evaluation, and analyze the key features of recent researches for shape comparison after classifying them into four categories including multi-resolution, topology, 2D image, and statistics based methods. In addition, we evaluated the performance of the reviewed methods by the selected criteria such as uniqueness, robustness, invariance, multi-resolution, efficiency, and comparison scope. Multi-resolution based methods have resulted in decreased computation time for comparison and increased preprocessing time. The methods using geometric and topological information were able to compare more various types of models and were robust to partial shape comparison. 2D image based methods incurred overheads in time and space complexity. Statistics based methods allowed for shape comparison without pose-normalization and showed robustness against affine transformations and noise.

• The Korean Journal of Mycology
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• v.25 no.4 s.83
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• pp.354-361
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• 1997

The antifungal activities of 153 extracts from 51 species of Korean mushroom on six phytopathogenic fungi were investigated. The powder of fruit-body of each mushroom was extracted with petroleum ether, 80% ethanol and distilled water. The water extracts of four mushrooms including Amanita virosa showed antifungal activities on Alternaria alternata, Colletotrichum graminicola, Fusarium solani, Idriella bolleyie. The 80% ethanol extracts of seven mushrooms including Boletus auripes showed antibiotic activities against A. alternata, C. gramincola, Cylindrocarpon destructans, F. solani, F. oxysporum cucumerinum, and I. bolleyie. The petroleum ether extracts of six mushrooms including Amanita citrina showed antibiotic activities against A. alternata, C. destructans, and C. graminicola. The 102 extracts of 34 mushrooms including Agaricus arvensis didn't show antibiotic activities.