• Speech Sciences
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• v.4 no.2
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• pp.55-68
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• 1998

In this paper, a novel method of noise reduction of speech based on a complex adaptive noise canceler and method of optimal feature extraction are proposed. This complex adaptive noise canceler needs simply the noise detection, and LMS algorithm used to calculate the adaptive filter coefficient. The method of optimal feature extraction requires the variance of noise. The experimental results have shown that the proposed method effectively reduced noise in noisy speech. Optimal feature extraction has shown similar characteristics in noise-free speech.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.8
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• pp.823-829
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• 2003

For high frequency microstrip line modelling, a fast inductance extraction technique using an adaptive PEEC(partial element equivalent circuit) grid is proposed. The grid refinement technique is based on the current distribution depend on the excitation frequencies and the geometry of the microstrip lines. The adaptive ids are refined mainly in the area where heavy currents reside. This technique is applied to the inductance extraction of the microstrip lines. The results show fast convergence, and this adaptive technique is efficient to reduce computing time and the number of grids.

• ETRI Journal
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• v.37 no.5
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• pp.1023-1031
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• 2015

In infrared (IR) images, near targets have a transient distribution at the boundary region, as opposed to a steady one at the inner region. Based on this fact, this paper proposes a novel IR target extraction method that uses both a weighted information entropy (WIE) and an adaptive opening filter to extract near finely shaped targets in IR images. Firstly, the boundary region of a target is detected using a local variance WIE of an original image. Next, a coarse target region is estimated via a labeling process used on the boundary region of the target. From the estimated coarse target region, a fine target shape is extracted by means of an opening filter having an adaptive structure element. The size of the structure element is decided in accordance with the width information of the target boundary and mean WIE values of windows of varying size. Our experimental results show that the proposed method obtains a better extraction performance than existing algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.12
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• pp.1249-1265
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• 1991

In this paper, a fast and adaptive feature extraction algorithm for textured image segmentation is proposed. First, a conventional algorithm to extract the statistical texture features are described and we obtain the recursive equations from that conventional method and it is used for extraction of sevaral texture features. And also we propose the adaptive algorithm which extract the texture features. To evaluate the performance of proposed algorithm, we apply the proposed method to artificial texture images. From the results of computer simulation, the proposed method is superior to the conventional one.

• Korean Journal of Remote Sensing
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• v.17 no.4
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• pp.319-334
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• 2001

Extracting primitives from imagery plays an important task in visual information processing since the primitives provide useful information about characteristics of the objects and patterns. The human visual system utilizes features without difficulty for image interpretation, scene analysis and object recognition. However, to extract and to analyze feature are difficult processing. The ultimate goal of digital image processing is to extract information and reconstruct objects automatically. The objective of this study is to develop robust method to achieve the goal of the image processing. In this study, an adaptive strategy was developed by implementing Gabor filters in order to extract feature information and to segment images. The Gabor filters are conceived as hypothetical structures of the retinal receptive fields in human vision system. Therefore, to develop a method which resembles the performance of human visual perception is possible using the Gabor filters. A method to compute appropriate parameters of the Gabor filters without human visual inspection is proposed. The entire framework is based on the theory of human visual perception. Digital images were used to evaluate the performance of the proposed strategy. The results show that the proposed adaptive approach improves performance of the Gabor filters for feature extraction and segmentation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.1
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• pp.27-32
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• 2003

This paper describes to extend the fast algorithm fur the capacitance extraction of large three-dimensional object. The triangular meshes are used and refined adaptively in the area where the heavy charges reside in each iterative solving. This technique is applied to the capacitance extraction of a 68-pin cerquad package. The results show fast convergence, and this adaptive technique coupled with the fast algorithm is efficient to reduce the number of elements and computing time with least additional computational efforts in large three dimensional problems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1802-1808
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• 2003

In this paper, we propose a method about the extraction of the pedestrian tracking trajectory in the road and we used the method of mosaic background extraction and adaptive neural network for automatic pedestrian tracking system. We used mosaic background extraction to overcome ghost phenomenon. And we detected pedestrian using differential image analysis. We used adaptive neural network for multiple pedestrian tracking that non­rigid form moving. The ART2 network is capable of detecting the mass­centers of moving objects within one frame. The history of neurons positions in the sequential frames approximates the traces of the targets. The experiments done with the network in simulated environment show promising results.

• Current Optics and Photonics
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• v.7 no.4
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• pp.387-397
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• 2023

Tooth surface shape error is an important parameter in gear accuracy evaluation. When tooth surface shape error is measured by laser interferometry, the gear interferogram is highly distorted and the gray level distribution is not uniform. Therefore, it is important for gear interferometry to extract the foreground region from the gear interference fringe image directly and accurately. This paper presents an approach for foreground extraction in gear interference images by leveraging the sinusoidal variation characteristics shown by the interference fringes. A gray level mask with an adaptive threshold is established to capture the relevant features, while a local variance evaluation function is employed to analyze the fluctuation state of the interference image and derive a repair mask. By combining these masks, the foreground region is directly extracted. Comparative evaluations using qualitative and quantitative assessment methods are performed to compare the proposed algorithm with both reference results and traditional approaches. The experimental findings reveal a remarkable degree of matching between the algorithm and the reference results. As a result, this method shows great potential for widespread application in the foreground extraction of gear interference images.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.1
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• pp.49-54
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• 2016

Regarding global and local factors of a set of features, a given single image or multiple images is a common approach in image processing. This paper introduces an application of an adaptive version of non-local filter whose original version searches non-local similarity for removing noise. Since most images involve texture partner in both foreground and background, extraction of signified regions with texture is a challenging task. Aiming to the detection of visual attention regions for images with texture, we present the contrast analysis of image patches located in a whole image but not nearby with assistance of the adaptive filter for estimation of non-local divergence. The method allows extraction of signified regions with texture of images of wild life. Experimental results for a benchmark demonstrate the ability of the proposed method to deal with the mentioned challenge.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2331-2334
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• 2003

Generally, skin color information has been widely used at the face region extraction step of the face region recognition process. But many experimental results show that they are very sensitive to the given threshold range which is used to extract the face regions at the input image. In this paper, we propose a face region extraction algorithm based on an adaptive range decision for skin color. First we extract the pixels which are regarded as the candidate skin color pixels by using the given range for skin color extraction. Then, the ratio between the total pixels and the extracted pixels is calculated. According to the ratio, we adaptively decide the range of the skin color and extract face region. From the experiment results for the various images, the proposed algorithm shows more accurate results than the conventional algorithm.