• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.5
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• pp.325-333
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• 2015

Vision based night-time vehicle detection has been an emerging research field in various advanced driver assistance systems(ADAS) and automotive vehicle as well as automatic head-lamp control. In this paper, we propose night-time vehicle detection method based on multi-class support vector machine(SVM) that consists of thresholding, labeling, feature extraction, and multi-class SVM. Vehicle light candidate blobs are extracted by local mean based thresholding following by labeling process. Seven geometric and stochastic features are extracted from each candidate through the feature extraction step. Each candidate blob is classified into vehicle light or not by multi-class SVM. Four different multi-class SVM including one-against-all(OAA), one-against-one(OAO), top-down tree structured and bottom-up tree structured SVM classifiers are implemented and evaluated in terms of vehicle detection performances. Through the simulations tested on road video sequences, we prove that top-down tree structured and bottom-up tree structured SVM have relatively better performances than the others.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1240-1242
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• 1996

In a still mill, the effective surface defect inspection algorithm is necessary. For this purpose, this paper proposed the preprocessing algorithm for surface defect inspection of cold mill strip. This consists of live steps. They are edge detection, binarizing, noise deletion, combining of fragmented defect and selecting the largest defect. Especially, binarizing is a critical problem. Bemuse the performance of the preprocessing is largely depend on the binarized image. So, we develope the adaptive thresholding method, which is multilevel thresholding. The thresholding value is varied according to the mean graylevel value of each test image. To investigate the performance of the proposed algorithm, we classified the detected defect using neural network. The test image is 20 defect images captured at German Sick Co. This algorithm is proved to have good property in cold mill strip surface inspection.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.200-208
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• 2016

Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing in an under-sampled (i.e., under Nyquist rate) representation. A block compressed sensing with projected Landweber (BCS-SPL) framework is most widely known, but, it has high computational complexity at decoder side. In this paper, by introducing adaptive exit criteria instead of fixed exit criteria to SPL framework, an effective fast algorithm is designed in such a way that it can utilize efficiently the sparsity property in DCT coefficients during the iterative thresholding process. Experimental results show that the proposed algorithm results in the significant reduction of the decoding time, while providing better visual qualities than conventional algorithm.

• ETRI Journal
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• v.33 no.6
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• pp.961-964
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• 2011

The cognitive ultra-wideband (UWB) network detects interfering narrowband systems and adapts its configuration accordingly. An inherently adaptive and flexible candidate for cognitive UWB transmission is the wavelet packet multicarrier modulation (WPMCM). In this letter, we use an enhanced forward consecutive mean excision thresholding algorithm to tackle the noise uncertainty in the wavelet-based sensing of WPMCM systems, and mathematical analysis is performed for primary user channel fading. As a benchmark, we compare the proposed system with a conventional fast Fourier transformation-based system, and performance investigation proves significant improvements when primary and secondary links are subjected to multipath fading and noise.

• Journal of computational fluids engineering
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• v.19 no.2
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• pp.58-65
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• 2014

The adaptive wavelet method is studied for the enhancement of computational efficiency of three-dimensional flows. For implementation of the method for three-dimensional Euler equation, wavelet decomposition process is introduced based on the previous two-dimensional adaptive wavelet method. The order of numerical accuracy of an original solver is preserved by applying modified thresholding value. In order to assess the efficiency of the proposed algorithm, the method is applied to the computation of flow field around ONERA-M6 wing in transonic regime with 4th and 6th order interpolating polynomial respectively. Through the application, it is confirmed that the three-dimensional adaptive wavelet method can reduce the computational time while conserving the numerical accuracy of an original solver.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.133-140
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• 2004

Recently, the dental information systems were rapidly developed in order to store and process the data of patients. But, these systems should serve a doctor a good quality information against disease for diagnostic and surgery purpose so as to success in this field. This function of the system it important to persuade patients to undergo proper surgical operation they needed. Hence, 3D teeth model capable of simulating the dental surgery and treatment is necessary Teeth manipulation of dentistry is performed on individual tooth in dental clinic. io, 3D teeth reconstruction system should have the techniques of segmentation and 3D reconstruction adequate for individual tooth. In this paper, we propose the techniques of adaptive optimal segmentation to segment the individual area of tooth, and reconstruction method of tooth based on contour-based method. Each tooth can be segmented from neighboring teeth and alveolar bone in CT images using adaptive optimal threshold computed differently on tooth. Reconstruction of individual tooth using results of segmentation can be manipulated according to user's input and make the simulation of dental surgery and treatment possible.

• Korean Journal of Optics and Photonics
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• v.11 no.6
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• pp.447-451
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• 2000

In this paper, an adaptive equalizer which has been widely used in communication channel and high-density storage system was adopted to mitigate ISI and improve BER generated during storage and retrieval process of the digital holographic memory system. Our experimental results show that the BER performance with an adaptive equalizer is improved about 19.6% on average compared to the binary-decision process by global thresholding. lding.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.1
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• pp.66-74
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• 2008

This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection. Scale invariant feature using the Laplacian scale-space can detect different sizes of targets robustly compared to the conventional spatial filtering methods with fixed kernel size. Additionally, scale-reflected adaptive thresholding can reduce many false alarms. Experimental results with real IR images show the robustness of the proposed target detection in real world.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.245-254
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• 2002

IC package marking inspection system using machine vision locates and identifies the target elements from input image, and decides the quality of marking by comparing the extracted target elements with the standard patterns. This paper proposes an adaptive multi-level thresholding (AMLT) method which is suitable for a series of operations such as locating the target IC package, extracting the characters, and detecting the Pinl dimple. It also proposes a dynamic matching unit selection (DMUS) method which is robust to noises as well as effective to catch out the local marking errors. The main idea of the AMLT method is to restrict the inputs of Otsu's thresholding algorithm within a specified area and a partial range of gray values. Doing so, it can adapt to the specific domain. The DMUS method dynamically selects the matching unit according to the result of character extraction and layout analysis. Therefore, in spite of the various erroneous situation occurred in the process of character extraction and layout analysis, it can select minimal matching unit in any environment. In an experiment with 280 IC package images of eight types, the correct extracting rate of IC package and Pinl dimple was 100% and the correct decision rate of marking quality was 98.8%. This result shows that the proposed methods are effective to IC package marking inspection.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.173-176
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• 2002

This paper describes an implementation of fast face detection algorithm. This algorithm can robustly detect human faces with unknown sizes and positions in complex backgrounds. This paper provides a powerful face detection algorithm using skin color segmenting. Skin Color is modeled by a Gaussian distribution in the HSI color space among different persons within the same race, Oriental. The main feature of the Algorithm is achieved face detection robust to illumination changes and a simple adaptive thresholding technique for skin color segmentation is employed to achieve robust face detection.