The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kertas kerja ini membentangkan rangka kerja teguh interaksi manusia-komputer daripada penglihatan isyarat tangan dengan kehadiran senario yang realistik dan mencabar. Untuk tujuan ini, beberapa komponen novel dicadangkan. Pendekatan hibrid pertama kali dicadangkan untuk membuat kesimpulan secara automatik kedudukan permulaan gerak isyarat tangan yang diminati melalui pengoptimuman bersama kawasan yang diberikan oleh model kulit luar talian yang dilatih daripada model campuran Gaussian dan pengelas gerak isyarat tangan khusus yang dilatih daripada teknik Adaboost. Untuk menjejak tangan secara konsisten dalam konteks penggunaan penjejakan berasaskan kernel, strategi pemilihan ciri separa seliaan dipersembahkan selanjutnya untuk memilih subruang ciri yang sesuai mewakili sifat isyarat kulit tangan luar talian dan isyarat klasifikasi latar depan-latar belakang dalam talian. Mengambil histogram kecerunan berorientasikan sebagai deskriptor untuk mewakili gerak isyarat tangan, pendekatan keputusan lembut akhirnya dicadangkan untuk mengenali gerak isyarat tangan statik di lokasi yang kesamaran teruk berlaku dan gerak isyarat dinamik berasaskan model Markov yang tersembunyi digunakan untuk interaksi. Eksperimen pada pelbagai jujukan video sebenar menunjukkan prestasi unggul komponen yang dicadangkan. Di samping itu, keseluruhan rangka kerja boleh digunakan untuk aplikasi masa nyata pada platform pengkomputeran umum.
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Salinan
Liang SHA, Guijin WANG, Xinggang LIN, Kongqiao WANG, "A Framework of Real Time Hand Gesture Vision Based Human-Computer Interaction" in IEICE TRANSACTIONS on Fundamentals,
vol. E94-A, no. 3, pp. 979-989, March 2011, doi: 10.1587/transfun.E94.A.979.
Abstract: This paper presents a robust framework of human-computer interaction from the hand gesture vision in the presence of realistic and challenging scenarios. To this end, several novel components are proposed. A hybrid approach is first proposed to automatically infer the beginning position of hand gestures of interest via jointly optimizing the regions given by an offline skin model trained from Gaussian mixture models and a specific hand gesture classifier trained from the Adaboost technique. To consistently track the hand in the context of using kernel based tracking, a semi-supervised feature selection strategy is further presented to choose the feature subspaces which appropriately represent the properties of offline hand skin cues and online foreground-background-classification cues. Taking the histogram of oriented gradients as the descriptor to represent hand gestures, a soft-decision approach is finally proposed for recognizing static hand gestures at the locations where severe ambiguity occurs and hidden Markov model based dynamic gestures are employed for interaction. Experiments on various real video sequences show the superior performance of the proposed components. In addition, the whole framework is applicable to real-time applications on general computing platforms.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E94.A.979/_p
Salinan
@ARTICLE{e94-a_3_979,
author={Liang SHA, Guijin WANG, Xinggang LIN, Kongqiao WANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Framework of Real Time Hand Gesture Vision Based Human-Computer Interaction},
year={2011},
volume={E94-A},
number={3},
pages={979-989},
abstract={This paper presents a robust framework of human-computer interaction from the hand gesture vision in the presence of realistic and challenging scenarios. To this end, several novel components are proposed. A hybrid approach is first proposed to automatically infer the beginning position of hand gestures of interest via jointly optimizing the regions given by an offline skin model trained from Gaussian mixture models and a specific hand gesture classifier trained from the Adaboost technique. To consistently track the hand in the context of using kernel based tracking, a semi-supervised feature selection strategy is further presented to choose the feature subspaces which appropriately represent the properties of offline hand skin cues and online foreground-background-classification cues. Taking the histogram of oriented gradients as the descriptor to represent hand gestures, a soft-decision approach is finally proposed for recognizing static hand gestures at the locations where severe ambiguity occurs and hidden Markov model based dynamic gestures are employed for interaction. Experiments on various real video sequences show the superior performance of the proposed components. In addition, the whole framework is applicable to real-time applications on general computing platforms.},
keywords={},
doi={10.1587/transfun.E94.A.979},
ISSN={1745-1337},
month={March},}
Salinan
TY - JOUR
TI - A Framework of Real Time Hand Gesture Vision Based Human-Computer Interaction
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 979
EP - 989
AU - Liang SHA
AU - Guijin WANG
AU - Xinggang LIN
AU - Kongqiao WANG
PY - 2011
DO - 10.1587/transfun.E94.A.979
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E94-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2011
AB - This paper presents a robust framework of human-computer interaction from the hand gesture vision in the presence of realistic and challenging scenarios. To this end, several novel components are proposed. A hybrid approach is first proposed to automatically infer the beginning position of hand gestures of interest via jointly optimizing the regions given by an offline skin model trained from Gaussian mixture models and a specific hand gesture classifier trained from the Adaboost technique. To consistently track the hand in the context of using kernel based tracking, a semi-supervised feature selection strategy is further presented to choose the feature subspaces which appropriately represent the properties of offline hand skin cues and online foreground-background-classification cues. Taking the histogram of oriented gradients as the descriptor to represent hand gestures, a soft-decision approach is finally proposed for recognizing static hand gestures at the locations where severe ambiguity occurs and hidden Markov model based dynamic gestures are employed for interaction. Experiments on various real video sequences show the superior performance of the proposed components. In addition, the whole framework is applicable to real-time applications on general computing platforms.
ER -