• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.896-902
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• 2021

Rotated face detection is required in many applications but still remains as a challenging task, due to the large variations of face appearances. In this paper, a polar coordinate transform that is not affected by rotation is proposed. In addition, a method for effectively detecting rotated faces using the transformed image has been proposed. The proposed polar coordinate transform maintains spatial information between facial components such as eyes, mouth, etc., since the positions of facial components are always maintained regardless of rotation angle, thereby eliminating rotation effects. Polar coordinate transformed images are trained using AdaBoost, which is used for frontal face detection, and rotated faces are detected. We validate the detected faces using LBP that trained the non-face images. Experiments on 3600 face images obtained by rotating images in the BioID database show a rotating face detection rate of 96.17%. Furthermore, we accurately detected rotated faces in images with a background containing multiple rotated faces.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.65-84
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• 2019

This study attempted to predict citizens' emotions regarding mortgage rates using machine learning algorithms. To accomplish the research purpose, I reviewed the related literature and then set up two research questions. To find the answers to the research questions, I classified emotions according to Akman's classification and then predicted citizens' emotions on mortgage rates using six machine learning algorithms. The results showed that AdaBoost was the best classifier in all evaluation categories. However, the performance level of Naive Bayes was found to be lower than those of other classifiers. Also, this study conducted a ROC analysis to identify which classifier predicts each emotion category well. The results demonstrated that AdaBoost was the best predictor of the residents' emotions on home mortgage rates in all emotion categories. However, in the sadness class, the performance levels of the six algorithms used in this study were much lower than those in the other emotion categories.

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.197-206
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• 2010

In this paper, we propose boosting algorithms when data are very large or coming in batches sequentially over time. In this situation, ordinary boosting algorithm may be inappropriate because it requires the availability of all of the training set at once. To apply to large scale data or data batch stream, we modify the AdaBoost and Arc-x4. These algorithms have good results for both large scale data and data batch stream with or without concept drift on simulated data and real data sets.

• ETRI Journal
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• v.30 no.2
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• pp.338-340
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• 2008

In this letter, we propose a novel approach to detecting and tracking apartment buildings for the development of a video-based navigation system that provides augmented reality representation of guidance information on live video sequences. For this, we propose a building detector and tracker. The detector is based on the AdaBoost classifier followed by hierarchical clustering. The classifier uses modified Haar-like features as the primitives. The tracker is a motion-adjusted tracker based on pyramid implementation of the Lukas-Kanade tracker, which periodically confirms and consistently adjusts the tracking region. Experiments show that the proposed approach yields robust and reliable results and is far superior to conventional approaches.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3E
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• pp.124-132
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• 2003

We propose a new method for speech/non-speech classifiers based on concepts of the adaptive boosting (AdaBoost) algorithm in order to detect speech for robust speech recognition. The method uses a combination of simple base classifiers through the AdaBoost algorithm and a set of optimized speech features combined with spectral subtraction. The key benefits of this method are the simple implementation, low computational complexity and the avoidance of the over-fitting problem. We checked the validity of the method by comparing its performance with the speech/non-speech classifier used in a standard voice activity detector. For speech recognition purpose, additional performance improvements were achieved by the adoption of new features including speech band energies and MFCC-based spectral distortion. For the same false alarm rate, the method reduced 20-50% of miss errors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.2
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• pp.330-337
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• 2005

This paper proposes a method which improves the performance of face detection by using SVM(Support Vector Machine). first, it finds face region candidates by using AdaBoost based object detection method which selects a small number of critical features from a larger set. Next it classifies if the candidate is a face or non-face by using SVM(Support Vector Machine). Experimental results shows that the proposed method improve accuracy of face detection in comparison with existing method.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.743-744
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• 2008

By using DirectX technology, an improved Visual Monitoring System implemented in this paper. The proposed Visual Monitoring System is developed based on the S3C2440 processor. The Windows CE 5.0 is adopted as an operating system, and Visual Monitoring System transfer image 15 frame per second using UDP/IP and by using AdaBoost Algorithm, detect face region and save face image.

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.83-89
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• 2008

This paper introduces a visual object classification algorithm based on statistical information. Objects are characterized through the Histogram of Oriented Gradients (HOG) method and classification is performed using Multiclass AdaBoost. Salient features of an object's appearance are detected by HOG blocks Blocks of different sizes are tested to define the most suitable configuration. To select the most informative blocks for classification a multiclass AdaBoostSVM algorithm is applied. The proposed method has a high speed processing and classification rate. Results of the evaluation based on example of hand gesture recognition are presented.

• Annual Conference of KIPS
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• 2015.04a
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• pp.864-865
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• 2015

웹페이지와 같은 스크린 콘텐츠는 카메라로부터 획득할 수 있는 자연영상과 달리 텍스트, 로고, 아이콘 및 하위 영상과 같은 여러 가지 요소들을 포함하고 있고, 각 요소들은 서로 다른 형식의 정보를 사용자에게 전달한다. 본 논문에서는 윈도우 영상을 지역적인 특성에 따라 다수의 블록으로 분할한 후, 분할된 각 영역을 배경, 텍스트, 하위영상으로 분류하였고, 기계학습 기반의 알고리즘이 하위 영상 검출에도 좋은 접근법이 될 수 있음을 증명하기 위해 AdaBoost를 이용하였다. 실험결과로부터 93.4%의 검출률, 13%의 거짓 긍정률을 보임으로서, 제안방법이 효과적임을 입증하였다.