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Subject: search.filters.subject.Adaboost

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Now showing 1 - 4 of 4
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    Fast Deformable Model for Pedestrian Detection with Haar-like Features
    (2017-01-01)
    Chou, Kuang-Pen
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    Prasad, Mukesh
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    Puthal, Deepak
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    Chen, Ping-Hung
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    Vishwakarma, Dinesh Kumar
    This paper proposes a novel Fast Deformable Model for Pedestrian Detection (FDMPD) to detect the pedestrians efficiently and accurately in the crowded environment. Despite of multiple detection methods available, detection becomes difficult due to variety of human postures and perspectives. The proposed study is divided into two parts. First part trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. Second part uses these six response feature maps with full-body model to produce spatial deep features. The combined deep features are used as an input to SVM to judge the existence of pedestrian. As per the experiments conducted on the INRIA person dataset, the proposed FDMPD approach shows greater than 44.75 % improvement compared to other state-of-the-art methods in terms of efficiency and robustness.
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    A NOVEL CLASSIFICATION PROCESSING BASED ON THE SPATIAL INFORMATION AND THE CONCEPT OF ADABOOST FOR HYPERSPECTRAL IMAGE CLASSIFICATION
    (2010)
    Kuo, Bor-Chen
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    Lin, Shih-Syun
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    Wu, Huey-Min
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    Chuang, Chun-Hsiang
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    電控工程研究所
    In this paper, a novel classification processing based on the spatial information and the concept of Adaboost for hyperspectral image classification is proposed. This classification process is named as adaptive feature extraction with spatial information (AdaFESI). The main idea is adaptive in the sense that subsequent feature spaces are tweaked in favor of those instances misclassified by spectral or spatial classifiers in the previous feature space. All training samples are projected into these feature spaces to train various classifiers and then constitute a multiple classifier system. The experimental results based on two hyperspectral data sets show that the proposed algorithm can generate better classification results.
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    Face Recognition Based on Sparse Representation Applied to Mobile Device
    (2014)
    Chou, Kuan-Yu
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    Huang, Guan-Ming
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    Tseng, Hao-Chien
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    Chen, Yon-Ping
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    電控工程研究所
    This paper develops an Android face recognition application for users on mobile device, and applies it in the face verification system of Samsung Galaxy SII smart phone. The developed face recognition application includes three parts, the face detection using the Viola-Jones face detection program, the feature extraction implemented by the eigenface features, and the face recognition based on the sparse representation of L2 norm minimization. Different to general learning methods, the developed application does not require a tremendous amount of time in data training and can achieve a high recognition rate even higher than 99%, for examples 99.2% for the Sheffield face database, 99.4% for the Cohn-Kanade face database and 96.5% for ORL face database. Finally, the face verification application proposed for the Samsung Galaxy SII smart phone indeed successfully verifies a face just in one second which makes it a real-time application.
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    Efficient face detection method with eye region judgment
    (2013)
    Lin, Chun-Fu
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    Lin, Sheng-Fuu
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    電控工程研究所
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    Institute of Electrical and Control Engineering
    Real-time face detection technology can be applied in many industrial or commercial products. Many face detection applications use the traditional Adaboost face detection system which is proposed by Viola and Jones in 2004. Viola and Jones used the Adaboost training algorithm and the Haar-like features in their proposed traditional Adaboost face detection system, which has a high detection rate but long training time. Many studies have attempted to reduce the training time and retain the high detection rate of the traditional Adaboost face detection system. However, the detection rate of the Adaboost-based face detection system cannot compete with the traditional Adaboost face detection system when the training time is reduced significantly. This study proposes the judging existence of eye region (JEER) method to enhance the detection rate of the previous Adaboost-based face detection systems. The eyes are more salient and representative features than the other facial parts such as the mouth or ears, especially when mask is worn on the human face. Therefore, the proposed face detection system with the JEER method achieves higher detection rate than the Adaboost-based face detection systems. Although the JEER computation results in a slightly longer training time, the training time of the proposed face detection system is still much shorter than the traditional Adaboost face detection system owing to the efficient JEER computation. The experimental results obtained using the gray FERET and CMU databases show that the proposed face detection system is effective in detection and efficient in training.