Multi-view hand detection applying viola-jones framework using SAMME AdaBoost

Chouvatut, Varin; Yotsombat, Chanun; Sriwichai, Rapeepat; Jindaluang, Wattana

doi:10.1109/kst.2015.7051476

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…By adjusting the number of weak classifiers, learning rate and algorithm selection to diagnosis system state, the number of weak classifiers is set to 20 and learning rate is set to 0.5. The algorithm chooses SAMME [13]. Through this classifier, fault detection is carried out, and the diagnosis results is shown in the following Fig.…”

Section: Fault Diagnose Resultsmentioning

confidence: 99%

Fault Diagnosis Of Solid Oxide Fuel Cell System Based On Adaboost Algorithms

Wu,

Xu,

Zhong

et al. 2020

Preprint

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Section: Fault Diagnose Resultsmentioning

confidence: 99%

Fault Diagnosis Of Solid Oxide Fuel Cell System Based On Adaboost Algorithms

Wu,

Xu,

Zhong

et al. 2020

Preprint

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“…As Haar-like features and the AdaBoost classifier [22][23][24] have been extensively applied in many different object detection applications with outstanding successes, Mao et al [23] proposed hand detection by improving Haar-like features with the restriction of asymmetric hand patterns. However, their experimental results demonstrated that the improvements might be marginal for complex backgrounds.…”

Section: Hand Detectionmentioning

confidence: 99%

“…However, their experimental results demonstrated that the improvements might be marginal for complex backgrounds. Chouvatut et al [24] applied the use of the SAMME algorithm [25], instead of a decision tree, as an estimator for the degree of orientation angles of the hands, mainly from the perspective of avoiding the overfitting problem. Despite the achievements made, it is generally accepted that Haarlike features are not powerful enough to represent complex objects like hands due to the large variations in their appearance.…”

Section: Hand Detectionmentioning

confidence: 99%

Multiscale Convolutional Neural Networks for Hand Detection

Yan

Xia

Smith

et al. 2017

Applied Computational Intelligence and Soft Computing

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Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper, we propose a multiscale deep learning model for unconstrained hand detection in still images. Deep learning models, and deep convolutional neural networks (CNNs) in particular, have achieved state-of-the-art performances in many vision benchmarks. Developed from the region-based CNN (R-CNN) model, we propose a hand detection scheme based on candidate regions generated by a generic region proposal algorithm, followed by multiscale information fusion from the popular VGG16 model. Two benchmark datasets were applied to validate the proposed method, namely, the Oxford Hand Detection Dataset and the VIVA Hand Detection Challenge. We achieved state-of-the-art results on the Oxford Hand Detection Dataset and had satisfactory performance in the VIVA Hand Detection Challenge.

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“…In Nguyen et al (2012), based on Viola-Jones' work a new approach was addressed for hand detection by detecting the internal region of the hand using its local features without a background. Chouvatut et al (2015) solved the problem of hand detection from various orientation angles of hand positions using the Viola-Jones detector and SAMME classifier. An automatic hand gesture recognition framework was prevented using the steps in the Viola-Jones method for detection and for the recognition phase Hu invariant moments feature vectors of the detected hand gesture are extracted and a Support Vector Machines (SVMs) classifier is trained for final recognition (Yun & Peng, 2009).…”

mentioning

confidence: 99%

Adapting Viola-Jones Method for Online Hand / Glove Identification

2021

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This article proposes a method for hand identification, adapting the method of Viola-Jones for identifying two different objects. The main objective of this work is to solve the problems of hand identification. Thus, our approach based on learning for two objects as one package. Also, the proposed method folds into three parts; the first part is training for both objects, second detection of both objects, and third the identification step to identify if the hand is wearing a glove or not, then labeling each one with a suitable state. Moreover, to test our method, we have proposed a new dataset, which includes a variety of cases with different compositions of hand. As a result, 8 cases were used to test the method. The method was able to detect a human hand successfully. Additionally, it could identify whether the hand was or was not wearhing a glove. The accuracy of detecting a hand without a glove was about 63%, and the accuracy of detecting a hand with a glove on was about 61%. Even though the tests scored different accuracy, as a first step towards solving this problem, it is a big achievement to even reach this level of accuracy.

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Multi-view hand detection applying viola-jones framework using SAMME AdaBoost

Cited by 3 publications

References 19 publications

Fault Diagnosis Of Solid Oxide Fuel Cell System Based On Adaboost Algorithms

Fault Diagnosis Of Solid Oxide Fuel Cell System Based On Adaboost Algorithms

Multiscale Convolutional Neural Networks for Hand Detection

Adapting Viola-Jones Method for Online Hand / Glove Identification

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