• Journal of Korea Multimedia Society
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• v.9 no.10
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• pp.1266-1275
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• 2006

In this paper, we propose a real-lime face detection and tracking algorithm using AdaBoost(Adaptive Boosting) algorithm. The proposed algorithm consists of two levels such as the face detection and the face tracking. First, the face detection used the eight-wavelet feature models which ate very simple. Each feature model applied to variable size and position, and then create initial feature set. The intial feature set and the training images which were consisted of face images, non-face images used the AdaBoost algorithm. The basic principal of the AdaBoost algorithm is to create final strong classifier joining linearly weak classifiers. In the training of the AdaBoost algorithm, we propose SAT(Summed-Area Table) method. Face tracking becomes accomplished at real-time using the position information and the size information of detected face, and it is extended view region dynamically using the fan-Tilt camera. We are setting to move center of the detected face to center of the Image. The experiment results were amply satisfied with the computational efficiency and the detection rates. In real-time application using Pan-Tilt camera, the detecter runs at about 12 frames per second.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2307-2314
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• 2015

In this paper, a discontinuous conduction mode (DCM) frequency control algorithm is proposed to reduce the input current ripple of a multi-phase interleaved boost converter. Unlike conventional variable duty and constant frequency control, the proposed algorithm controls the switching frequency to regulate the output voltage. By fixing the duty ratio at 1/N in the N-phase interleaved boost converter, the input current ripple can be minimized by ripple cancellation. Furthermore, the negative effects of the diode reverse recovery current are eliminated because of the DCM characteristic. A frequency controller is designed to employ the proposed algorithm considering the magnetic permeability change. The proposed algorithm is analyzed in the frequency domain and verified by a 600 W three-phase boost converter prototype that achieved 57% ripple current reduction.

• Journal of the Semiconductor & Display Technology
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• v.17 no.4
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• pp.105-108
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• 2018

Face Detection is an essential first step of the face recognition, and this is significant effects on face feature extraction and the effects of face recognition. Face detection has extensive research value and significance. In this paper, we present and analysis the principle, merits and demerits of the classic AdaBoost face detection and ASM algorithm based on point distribution model, which ASM solves the problems of face detection based on AdaBoost. First, the implemented scheme uses AdaBoost algorithm to detect original face from input images or video stream. Then, it uses ASM algorithm converges, which fit face region detected by AdaBoost to detect faces more accurately. Finally, it cuts out the specified size of the facial region on the basis of the positioning coordinates of eyes. The experimental result shows that the method can detect face rapidly and precisely, with a strong robustness.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.44-50
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• 2011

We propose a multi-class AdaBoost algorithm for en efficient classification of multi-class data in this paper. Traditional AdaBoost algorithm is basically a binary classifier and it has limitations when applied to multi-class data problems even though multi-class versions are available. In order to overcome the problems on the AdaBoost algorithm for multi-class classification problems, we devise an AdaBoost architecture with a training algorithm that utilizes multi-class classifiers for its weak classifiers instead of series of binary classifiers. Experiments on a image classification problem using collected Caltech Image Database are preformed. The results show that the proposed AdaBoost architecture can reduce its training time while maintaining its classification accuracy competitive when compared to Adaboost.M2.

• Journal of Advanced Navigation Technology
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• v.17 no.2
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• pp.247-254
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• 2013

The task of 3D gesture recognition for controlling equipments is highly challenging due to the propagation of 3D smart TV recently. In this paper, the AdaBoost algorithm is applied to 3D gesture recognition by using Kinect sensor. By tracking time interval trajectory of hand, wrist and arm by Kinect, AdaBoost algorithm is used to train and classify 3D gesture. Experimental results demonstrate that the proposed method can successfully extract trained gestures from continuous hand, wrist and arm motion in real time.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.545-550
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• 2013

In pattern recognition field, data classification is an essential process for extracting meaningful information from data. Adaptive boosting algorithm, known as AdaBoost algorithm, is a kind of improved boosting algorithm for applying to real data analysis. It consists of weak classifiers, such as random guessing or random forest, which performance is slightly more than 50% and weights for combining the classifiers. And a strong classifier is created with the weak classifiers and the weights. In this paper, a research is performed using AdaBoost algorithm for detecting chaff echo which has similar characteristics to precipitation echo and interrupts weather forecasting. The entire process for implementing chaff echo classifier starts spatial and temporal clustering based on similarity with weather radar data. With them, learning data set is prepared that separated chaff echo and non-chaff echo, and the AdaBoost classifier is generated as a result. For verifying the classifier, actual chaff echo appearance case is applied, and it is confirmed that the classifier can distinguish chaff echo efficiently.

• Journal of Korea Multimedia Society
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• v.15 no.5
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• pp.606-614
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• 2012

Face detection using AdaBoost algorithm is one of the fastest and the most robust face detection algorithm so many improvements or extensions of this method have been proposed. However, almost all previous approaches deal with only frontal face and suffer from limited discriminant capability for inclined face because these methods apply the same features for both frontal and inclined face. Also conventional approaches for detecting inclined face which apply frontal face detecting method to inclined input image or make different detectors for each angle require heavy computational complexity and show low detection rate. In order to overcome this problem, a method for detecting inclined face using JointBoost is proposed in this paper. The computational and sample complexity is reduced by finding common features that can be shared across the classes. Simulation results show that the detection rate of the proposed method is at least 2% higher than that of the conventional AdaBoost method under the learning condition with the same iteration number. Also the proposed method not only detects the existence of a face but also gives information about the inclined direction of the detected face.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.166-171
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• 2013

The method proposed in this paper can improve the performance of the Boosting algorithm in machine learning. The proposed Boundary AdaBoost algorithm can make up for the weak points of Normal binary classifier using threshold boundary concepts. The new proposed boundary can be located near the threshold of the binary classifier. The proposed algorithm improves classification in areas where Normal binary classifier is weak. Thus, the optimal boundary final classifier can decrease error rates classified with more reasonable features. Finally, this paper derives the new algorithm's optimal solution, and it demonstrates how classifier accuracy can be improved using the proposed Boundary AdaBoost in a simulation experiment of pedestrian detection using 10-fold cross validation.

• The Transactions of the Korean Institute of Power Electronics
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• v.17 no.6
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• pp.471-477
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• 2012

In this paper, in order to use module integrated converter using cascaded buck-boost converter for a low battery charger in stand-alone system, a charging algorithm which considers photovoltaic and battery status and PWM controllers which are changed according to charging modes are proposed. The proposed algorithm consists of constant current mode, constant voltage mode and maximum power point tracking mode which enables the battery to charge with maximum power rate. This paper also presents design of cascaded buck-boost converter that is the photovoltaic charger system. A 150W prototype system is built according to verify proposed the charger system and the algorithm.

• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.1
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• pp.1-11
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• 2009

This paper proposes an algorithm capable of detecting vehicles in front or in rear using a monocular camera installed in a vehicle. The vehicle detection has been regarded as an important part of intelligent vehicle technologies. The proposed algorithm is mainly composed of two parts: 1)hypothesis generation of vehicles, and 2)hypothesis verification. The hypotheses of vehicles are generated by the analysis of vertical and horizontal edges and the detection of symmetry axis. The hypothesis verification, which determines vehicles among hypotheses, is done by the AdaBoost algorithm. The proposed algorithm is proven to be effective through experiments performed on various images captured on the roads.