Bowden, R.[Richard],
Sarhadi, M.[Mansoor],
A non-linear model of shape and motion for tracking finger spelt
American sign language,
IVC(20), No. 9-10, August 2002, pp. 597-607.
Elsevier DOI
0208
BibRef
Earlier:
Building Temporal Models for Gesture Recognition,
BMVC00(xx-yy).
PDF File.
0009
BibRef
Tsechpenakis, G.[Gabriel],
Metaxas, D.N.[Dimitris N.],
Neidle, C.[Carol],
Learning-based dynamic coupling of discrete and continuous trackers,
CVIU(103), No. 2-3, November-December 2006, pp. 140-156.
Elsevier DOI
0611
Model-based continuous tracking; Appearance-based discrete tracking;
Coupling trackers; Data-driven tracking error; 3D hand tracking;
Fingerspelling segmentation
BibRef
Michael, N.[Nicholas],
Yang, P.[Peng],
Liu, Q.S.[Qing-Shan],
Metaxas, D.N.[Dimitris N.],
Neidle, C.[Carol],
A Framework for the Recognition of Nonmanual Markers in Segmented
Sequences of American Sign Language,
BMVC11(xx-yy).
HTML Version.
1110
BibRef
Yang, H.D.[Hee-Deok],
Sclaroff, S.[Stan],
Lee, S.W.[Seong-Whan],
Sign Language Spotting with a Threshold Model Based on Conditional
Random Fields,
PAMI(31), No. 7, July 2009, pp. 1264-1277.
IEEE DOI
0905
Signs appear in a continuous stream with other gestures.
BibRef
Yang, H.D.[Hee-Deok],
Lee, S.W.[Seong-Whan],
Simultaneous spotting of signs and fingerspellings based on
hierarchical conditional random fields and boostmap embeddings,
PR(43), No. 8, August 2010, pp. 2858-2870.
Elsevier DOI
1006
Sign language spotting; Fingerspelling spotting; Conditional random field
See also Sign Language Spotting with a Threshold Model Based on Conditional Random Fields.
BibRef
Yang, H.D.[Hee-Deok],
Lee, S.W.[Seong-Whan],
Robust sign language recognition by combining manual and non-manual
features based on conditional random field and support vector machine,
PRL(34), No. 16, 2013, pp. 2051-2056.
Elsevier DOI
1310
BibRef
Earlier:
Robust Sign Language Recognition with Hierarchical Conditional Random
Fields,
ICPR10(2202-2205).
IEEE DOI
1008
Sign language recognition
BibRef
Cho, S.S.[Seong-Sik],
Yang, H.D.[Hee-Deok],
Lee, S.W.[Seong-Whan],
Sign language spotting based on semi-Markov Conditional Random Field,
WACV09(1-6).
IEEE DOI
0912
BibRef
Suraj, M.G.,
Guru, D.S.,
Manjunath, S.,
Recognition Of Postal Codes From Fingerspelling Video Sequence,
IJIG(11), No. 1, January 2011, pp. 21-41.
DOI Link
1103
BibRef
Altun, O.[Oguz],
Albayrak, S.[Songül],
Turkish fingerspelling recognition system using Generalized Hough
Transform, interest regions, and local descriptors,
PRL(32), No. 13, 1 October 2011, pp. 1626-1632.
Elsevier DOI
1109
BibRef
Earlier:
Increasing the Effect of Fingers in Fingerspelling Hand Shapes by Thick
Edge Detection and Correlation with Penalization,
PSIVT06(1133-1141).
Springer DOI
0612
Generalized Hough Transform; DoG; SIFT; Interest regions; Local
descriptors; Fingerspelling recognition
BibRef
Dahmani, D.[Djamila],
Larabi, S.[Slimane],
User-independent system for sign language finger spelling recognition,
JVCIR(25), No. 5, 2014, pp. 1240-1250.
Elsevier DOI
1406
Hand posture
BibRef
Kane, L.[Lalit],
Khanna, P.[Pritee],
A framework for live and cross platform fingerspelling recognition
using modified shape matrix variants on depth silhouettes,
CVIU(141), No. 1, 2015, pp. 138-151.
Elsevier DOI
1512
Depth sensing
BibRef
Pattanaworapan, K.[Kanjana],
Chamnongthai, K.[Kosin],
Guo, J.M.[Jing-Ming],
Signer-independence finger alphabet recognition using discrete
wavelet transform and area level run lengths,
JVCIR(38), No. 1, 2016, pp. 658-677.
Elsevier DOI
1605
Sign language recognition
BibRef
Chang, H.J.[Hyung Jin],
Garcia-Hernando, G.[Guillermo],
Tang, D.H.[Dan-Hang],
Kim, T.K.[Tae-Kyun],
Spatio-Temporal Hough Forest for efficient
detection-localisation-recognition of fingerwriting in egocentric
camera,
CVIU(148), No. 1, 2016, pp. 87-96.
Elsevier DOI
1606
Spatio-Temporal Hough forest
BibRef
Chen, M.,
Al Regib, G.,
Juang, B.H.,
Feature Processing and Modeling for 6D Motion Gesture Recognition,
MultMed(15), No. 3, 2013, pp. 561-571.
IEEE DOI
1303
BibRef
Kim, P.,
Kim, K.S.,
Kim, S.,
Modified Nonnegative Matrix Factorization Using the Hadamard Product
to Estimate Real-Time Continuous Finger-Motion Intentions,
HMS(47), No. 6, December 2017, pp. 1089-1099.
IEEE DOI
1712
Adaptation models, Data models, Estimation, Feature extraction,
Fingers, Real-time systems, Robots, Finger-intention estimation,
surface electromyogram (sEMG)
BibRef
Li, R.,
Nguyen, M.,
Yan, W.Q.,
Morse Codes Enter Using Finger Gesture Recognition,
DICTA17(1-8)
IEEE DOI
1804
gesture recognition, image sequences, mobile communication,
mobile computing, Morse codes enter, Morse codes input,
Training
BibRef
Gao, B.,
Kim, H.,
Lee, H.,
Lee, J.,
Kim, J.,
Effects of Continuous Auditory Feedback on Drawing Trajectory-Based
Finger Gestures,
HMS(48), No. 6, December 2018, pp. 658-669.
IEEE DOI
1812
feedback, gesture recognition, human computer interaction,
touch sensitive screens, finger-attached pen, fat finger issue,
visual occlusion
BibRef
Chao, F.,
Huang, Y.,
Lin, C.,
Yang, L.,
Hu, H.,
Zhou, C.,
Use of Automatic Chinese Character Decomposition and Human Gestures
for Chinese Calligraphy Robots,
HMS(49), No. 1, February 2019, pp. 47-58.
IEEE DOI
1901
Writing, Trajectory, Manipulators, Databases, Service robots, Hardware,
Chinese character decomposition, human-robot interactions, robotic calligraphy
BibRef
Shi, B.,
Rio, A.M.D.,
Keane, J.,
Brentari, D.,
Shakhnarovich, G.,
Livescu, K.,
Fingerspelling Recognition in the Wild With Iterative Visual
Attention,
ICCV19(5399-5408)
IEEE DOI
2004
feature extraction, handicapped aids, human computer interaction,
iterative methods, sign language recognition,
Hidden Markov models
BibRef
Alam, M.S.[Md. Shahinur],
Kwon, K.C.[Ki-Chul],
Kim, N.[Nam],
Implementation of a Character Recognition System Based on
Finger-Joint Tracking Using a Depth Camera,
HMS(51), No. 3, June 2021, pp. 229-241.
IEEE DOI
2106
Writing, Gesture recognition, Cameras, Character recognition,
Tracking, Keyboards, Thumb, Character recognition,
human-computer interaction (HCI)
BibRef
Benitez-Garcia, G.[Gibran],
Haris, M.[Muhammad],
Tsuda, Y.[Yoshiyuki],
Ukita, N.[Norimichi],
Continuous Finger Gesture Spotting and Recognition Based on
Similarities Between Start and End Frames,
ITS(23), No. 1, January 2022, pp. 296-307.
IEEE DOI
2201
Gesture recognition, Feature extraction, Real-time systems,
Proposals, Safety, Videos, Hand gesture recognition,
automotive user interfaces
BibRef
Guo, Z.H.[Zi-Hui],
Hou, Y.H.[Yong-Hong],
Hou, C.P.[Chun-Ping],
Yin, W.J.[Wen-Jie],
Locality-Aware Transformer for Video-Based Sign Language Translation,
SPLetters(30), 2023, pp. 364-368.
IEEE DOI
2305
Videos, Assistive technologies, Gesture recognition, Transformers,
Encoding, Visualization, Task analysis, sign language translation
BibRef
Muroi, M.[Masanori],
Sogi, N.[Naoya],
Kato, N.[Nobuko],
Fukui, K.[Kazuhiro],
Fingerspelling Recognition with Two-steps Cascade Process of Spotting
and Classification,
MPRSS20(728-743).
Springer DOI
2103
BibRef
Kwolek, B.[Bogdan],
Sako, S.[Shinji],
Learning Siamese Features for Finger Spelling Recognition,
ACIVS17(225-236).
Springer DOI
1712
BibRef
Hosoe, H.,
Sako, S.,
Kwolek, B.,
Recognition of JSL finger spelling using convolutional neural
networks,
MVA17(85-88)
DOI Link
1708
Assistive technology, Cameras, Gesture recognition, Solid modeling,
Thumb
BibRef
Artyukhin, S.G.,
Mestetskiy, L.M.,
Dactyl Alphabet Gesture Recognition in a Video Sequence Using Microsoft
Kinect,
PTVSBB15(83-86).
DOI Link
1508
BibRef
Hameed, M.Z.[Muhammad Zaid],
Garcia-Hernando, G.[Guillermo],
Novel spatio-temporal features for fingertip writing recognition in
egocentric viewpoint,
MVA15(484-488)
IEEE DOI
1507
Cameras
BibRef
Silva, S.[Samira],
Schwartz, W.R.[William Robson],
Cámara-Chávez, G.[Guillermo],
Spatial Pyramid Matching for Finger Spelling Recognition in Intensity
Images,
CIARP14(629-636).
Springer DOI
1411
BibRef
Rioux-Maldague, L.[Lucas],
Giguere, P.[Philippe],
Sign Language Fingerspelling Classification from Depth and Color
Images Using a Deep Belief Network,
CRV14(92-97)
IEEE DOI
1406
Accuracy
BibRef
Weerasekera, C.S.,
Jaward, M.H.,
Kamrani, N.,
Robust ASL Fingerspelling Recognition Using Local Binary Patterns and
Geometric Features,
DICTA13(1-8)
IEEE DOI
1402
computer vision
BibRef
Kim, T.[Taehwan],
Shakhnarovich, G.[Greg],
Livescu, K.[Karen],
Fingerspelling Recognition with Semi-Markov Conditional Random Fields,
ICCV13(1521-1528)
IEEE DOI
1403
BibRef
Lahamy, H.,
Lichti, D.D.[Derek D.],
Robust Real-time And Rotation-invariant American Sign Language Alphabet
Recognition Using Range Camera,
ISPRS12(XXXIX-B5:217-222).
DOI Link
1209
BibRef
Saldivar-Piñon, L.[Leonardo],
Chacon-Murguia, M.I.[Mario I.],
Sandoval-Rodriguez, R.[Rafael],
Vega-Pineda, J.[Javier],
Human Sign Recognition for Robot Manipulation,
MCPR12(107-116).
Springer DOI
1208
BibRef
Rios Soria, D.J.[David J.],
Schaeffer, S.E.[Satu Elisa],
A Tool for Hand-Sign Recognition,
MCPR12(137-146).
Springer DOI
1208
BibRef
Lilha, H.[Himanshu],
Shivmurthy, D.[Devashish],
Evaluation of features for automated transcription of dual-handed sign
language alphabets,
ICIIP11(1-5).
IEEE DOI
1112
BibRef
Pugeault, N.[Nicolas],
Bowden, R.[Richard],
Spelling it out: Real-time ASL fingerspelling recognition,
ConDepth11(1114-1119).
IEEE DOI
1201
BibRef
Rashid, O.[Omer],
Al-Hamadi, A.[Ayoub],
Utilizing the Bezier descriptors for hand gesture recognition,
ICIP15(3525-3529)
IEEE DOI
1512
Bezier Curves; Gesture Recognition; HMM
BibRef
Rashid, O.[Omer],
Al-Hamadi, A.[Ayoub],
Michaelis, B.[Bernd],
Utilizing Invariant Descriptors for Finger Spelling American Sign
Language Using SVM,
ISVC10(I: 253-263).
Springer DOI
1011
BibRef
Ricco, S.[Susanna],
Tomasi, C.[Carlo],
Fingerspelling Recognition through Classification of Letter-to-Letter
Transitions,
ACCV09(III: 214-225).
Springer DOI
0909
BibRef
Jin, L.W.[Lian-Wen],
Yang, D.[Duanduan],
Zhen, L.X.[Li-Xin],
Huang, J.C.[Jian-Cheng],
A Novel Vision based Finger-writing Character Recognition System,
ICPR06(I: 1104-1107).
IEEE DOI
0609
BibRef
Chen, Z.W.[Zhi-Wei],
Lin, Y.C.[Yu-Cheng],
Chiang, C.C.[Cheng-Chin],
In this paper, an approach for deaf-people,
ICPR06(II: 104-107).
IEEE DOI
0609
Finger writing interface.
BibRef
Goh, P.,
Holden, E.J.[Eun-Jung],
Dynamic Fingerspelling Recognition using Geometric and Motion Features,
ICIP06(2741-2744).
IEEE DOI
0610
See also Australian sign language recognition.
BibRef
Feris, R.S.[Rogerio S.],
Turk, M.A.[Matthew A.],
Raskar, R.[Ramesh],
Tan, K.[Karhan],
Ohashi, G.[Gosuke],
Exploiting Depth Discontinuities for Vision-Based Fingerspelling
Recognition,
RealTimeHCI04(155).
IEEE DOI
0502
BibRef
Birk, H.[Henrik],
Moeslund, T.B.[Thomas B.], and
Madsen, C.B.[Claus B.],
Real-Time Recognition of Hand Alphabet Gestures Using
Principal Component Analysis,
SCIA97(xx-yy)
HTML Version.
9705
BibRef
Uras, C.,
Verri, A.,
Sign Language Recognition: an Application of the Theory of Size
Functions,
BMVC95(xx-yy).
PDF File.
9509
BibRef
Earlier:
On the recognition of the alphabet of the sign language through size
functions,
ICPR94(B:334-338).
IEEE DOI
9410
BibRef
Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Sign Language Generation, Sign Language Synthesis .