A method for recognizing a sequence of sign language words represented in a Japanese sign language sentence

Sagawa, Hirohiko; Takeuchi, Masaru

doi:10.1109/afgr.2000.840671

Cited by 42 publications

(31 citation statements)

References 7 publications

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“…Current systems rely almost exclusively on non-vision techniques to obtain such data, such as CyberGloves [7,18,19,30] and color markers [11]. An automated computer vision technique like ours imposes no restrictions on users.…”

Section: Discussionmentioning

confidence: 99%

3D Hand Pose Reconstruction Using Specialized Mappings

Rosales¹,

Athitsos²,

Sigal³

et al. 2001

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Section: Discussionmentioning

confidence: 99%

3D Hand Pose Reconstruction Using Specialized Mappings

Rosales¹,

Athitsos²,

Sigal³

et al. 2001

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“…Many approaches are not vision-based, but instead use input from magnetic trackers and sensor gloves, e.g., [21,37,45,57,58,66]. Such methods achieve good recognition results on continuous Chinese Sign Language with vocabularies of about 5,000 signs [21,58,66].…”

Section: Related Workmentioning

confidence: 99%

A database-based framework for gesture recognition

Athitsos

Wang

Stefan

2010

Pers Ubiquit Comput

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Gestures are an important modality for human-machine communication. Computer vision modules performing gesture recognition can be important components of intelligent homes, assistive environments, and human-computer interfaces. A key problem in recognizing gestures is that the appearance of a gesture can vary widely depending on variables such as the person performing the gesture, or the position and orientation of the camera. This paper presents a database-based approach for addressing this problem. The large variability in appearance among different examples of the same gesture is addressed by creating large gesture databases, that store enough exemplars from each gesture to capture the variability within that gesture. This database-based approach is applied to two gesture recognition problems: handshape categorization and motion-based recognition of American Sign Language (ASL) signs. A key aspect of our approach is the use of database indexing methods, in order to address the challenge of searching large databases without violating the time constraints of an online interactive system, where system response times of over a few seconds are oftentimes considered unacceptable. Our experiments demonstrate the benefits of the proposed database-based framework, and the feasibility of integrating large gesture databases into online interacting systems.

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“…One proposed solution to movement epenthesis detection is an explicit segmentation model were subsets, of features from gesture data, are used as cues for valid gesture start and end point detection [27,20]. The limitation of this explicit segmentation model arises from the difficulty in creating general rules for sign boundary detection that could be applied to all types of gestures [25].…”

Section: Icmi-mlmi'09mentioning

confidence: 99%

A framework for continuous multimodal sign language recognition

Kelly

Delannoy

Donald

et al. 2009

Proceedings of the 2009 International Conference on Multimodal Interfaces

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We present a multimodal system for the recognition of manual signs and non-manual signals within continuous sign language sentences. In sign language, information is mainly conveyed through hand gestures (Manual Signs). Non-manual signals, such as facial expressions, head movements, body postures and torso movements, are used to express a large part of the grammar and some aspects of the syntax of sign language. In this paper we propose a multichannel HMM based system to recognize manual signs and non-manual signals. We choose a single non-manual signal, head movement, to evaluate our framework when recognizing non-manual signals. Manual signs and non-manual signals are processed independently using continuous multidimensional HMMs and a HMM threshold model. Experiments conducted demonstrate that our system achieved a detection ratio of 0.95 and a reliability measure of 0.93.

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A method for recognizing a sequence of sign language words represented in a Japanese sign language sentence

Cited by 42 publications

References 7 publications

3D Hand Pose Reconstruction Using Specialized Mappings

3D Hand Pose Reconstruction Using Specialized Mappings

A database-based framework for gesture recognition

A framework for continuous multimodal sign language recognition

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