• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1745-1753
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• 2004

A method of estimating the pose of a three-dimensional object from a set of two-dimensioal images based on parametric eigenspace method is proposed. A Gaussian blurred edge image is used as an input image instead of the original image itself as has been used previously. The set of input images is compressed using K-L transformation. By comparing the estimation errors for the original, blurred original, edge, and blurred edge images, we show that blurring with the Gaussian function and the use of edge images enhance the data compression ratio and decrease the resulting from smoothing the trajectory in the parametric eigenspace, thereby allowing better pose estimation to be achieved than that obtainable using the original images as it is. The proposed method is shown to have improved efficiency, especially in cases with occlusion, position shift, and illumination variation. The results of the pose angle estimation show that the blurred edge image has the mean absolute errors of the pose angle in the measure of 4.09 degrees less for occlusion and 3.827 degrees less for position shift than that of the original image.

• Journal of Korea Multimedia Society
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• v.19 no.9
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• pp.1659-1668
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• 2016

Understanding 3D structure of scenes is of a great interest in various vision-related tasks. In this paper, we present a unified approach for estimating depth from a single monocular image. The key idea of our approach is to take advantages both of parametric learning and non-parametric sampling method. Using a parametric convolutional network, our approach learns the relation of various monocular cues, which make a coarse global prediction. We also leverage the local prediction to refine the global prediction. It is practically estimated in a non-parametric framework. The integration of local and global predictions is accomplished by concatenating the feature maps of the global prediction with those from local ones. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively.

• Journal of Broadcast Engineering
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• v.11 no.4 s.33
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• pp.396-406
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• 2006

This paper proposes an omni-directional image generation algorithm with parametric image compensation. The algorithm generates an omni-directional image by transforming each planar image to the spherical image on spherical coordinate. Parametric image compensation method is presented in order to compensate vignetting and illumination distortions caused by properties of a camera system and lighting condition. The proposed algorithm can generates realistic and seamless omni-directional video and synthesize any point of view from the stitched omni-directional image on the spherical image. Experimental results show that the proposed omni-directional system with vignetting and illumination compensation is approximately $1{\sim}4dB$ better than that which does not consider the said effects.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.2
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• pp.41-51
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• 2014

This paper describes a method to estimate the noise power using the minimum statistics approach, which was originally proposed for audio processing. The proposed minimum statistics-based method separates a noisy image into multiple frequency bands using the three-level discrete wavelet transform. By assuming that the output of the high-pass filter contains both signal detail and noise, the proposed algorithm extracts the region of pure noise from the high frequency band using an appropriate threshold. The region of pure noise, which is free from the signal detail part and the DC component, is well suited for minimum statistics condition, where the noise power can be extracted easily. The proposed algorithm reduces the computational load significantly through the use of a simple processing architecture without iteration with an estimation accuracy greater than 90% for strong noise at 0 to 40dB SNR of the input image. Furthermore, the well restored image can be obtained using the estimated noise power information in parametric image restoration algorithms, such as the classical parametric Wiener or ForWaRD image restoration filters. The experimental results show that the proposed algorithm can estimate the noise power accurately, and is particularly suitable for fast, low-cost image restoration or enhancement applications.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.4
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• pp.211-216
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• 2017

This paper proposes image processing techniques that improve usability and performance in a diagnostic system of the contrast-enhanced ultrasonography. For a methodology for visualizing diagnostic parameter data in an ultrasonic medical image, an expression of transition time data with successive pixel values and a method of generating a lesion diagnostic parameter image with four categorized values are presented. We also introduce a MRF-based image enhancement technique to eliminate noises from generated parametric images. Such parametric image generation technique can overcome the difficulty of discriminating dynamic change in patterns in the ultrasonography. The technique clarifies the contour of the region in the original image and facilitates visual determination of the characteristics of the lesion through four colors. With regard to this MRF-based image enhancement, we define the energy function of consecutive pixel values and develop a technique to optimize it, and the usability of the proposed theory is examined through experiments with medical images.

• Journal of Industrial Technology
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• v.28 no.A
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• pp.27-34
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• 2008

The purpose of this paper is an adaptive image interpolation using parametric cubic convolution. Proposed method derive parameter of adapting the frequency from adjacent values. The parameter optimize the interpolation kernel of cubic convolution. Simulation results show that the proposed method is superior to the conventional method in terms of the subjective and objective image quality.

• Journal of Mechanical Science and Technology
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• v.14 no.5
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• pp.518-529
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• 2000

A detailed and thorough parametric study of digital image correlation method is presented. A theoretical background and development of the method were introduced and the effects of various parameters on the determination of displacement outputs from the raw original and deformed image information were examined. Use of the normalized correlation coefficient, the use of 20 to 40 pixels for a searching window side, 6 variables searching, bi-cubic spline sub pixel interpolations and the use of coarse-fine search are some of the key choices among the results of parametric studies. The displacement outputs can be further processed with two dimensional curve fitting for the data noise reduction as well as displacement gradient calculation.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.875-876
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• 2008

Parametric stereo coding is a technique to efficiently code a stereo audio signal as a monaural signal plus small amount of parametric overhead to describe the stereo image. The stereo properties are analyzed, encoded, and reinstated in a decoder according to spatial psycho-acoustical principles. However, coding of multichannel audio signal using parametric stereo still requires considerable bit-rate. In this paper, enhanced parametric stereo coding for multichannel audio signal is proposed.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.6
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• pp.231-236
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• 2014

The transit time of contrast agents and the parameters of time-intensity curves in ultrasonography are important factors to diagnose various diseases of a digestive organ. We have implemented an automatic parametric imaging method to overcome the difficulty of the diagnosis by naked eyes. However, the micro-bubble noise and the respiratory motions may degrade the reliability of the parameter images. In this paper, we introduce an optimization technique based on MRF(Markov Random Field) model to enhance the quality of the parameter images, and present an image tracking algorithm to compensate the image distortion by respiratory motions. A method to extract the respiration periods from the ultrasound image sequence has been developed. We have implemented the ROI(Region of Interest) tracking algorithm using the dynamic weights and a momentum factor based on these periods. An energy function is defined for the Gibbs sampler of the image enhancement method. Through the experiments using the data to diagnose liver lesions, we have shown that the proposed method improves the quality of the parametric images.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.8
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• pp.566-573
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• 2019

In order to obtain high-quality images by motion compensation in the airborne synthetic aperture radar (SAR), accurate motion sensing in image acquisition section is necessary. Especially, reducing relative position error and discontinuity in motion sensing is important. To overcome the problem, we propose a pre-parametric error modeling (P-PEM) algorithm which is a real-time motion sensing algorithm for the airborne SAR in this paper. P-PEM is an extended version of parametric error modeling (PEM) method which is a motion sensing algorithm to mitigate the errors in the previous work. PEM estimates polynomial coefficients of INS error which can be assumed as a polynomial in the short term. Otherwise, P-PEM estimates polynomial coefficients in advance and uses at image acquisition section. Simulation results show that the P-PEM reduces relative position error and discontinuity effectively in real-time.