A System to Interact with CAVE Applications Using Hand Gesture Recognition from Depth Data

Leite, Diego Augusto T. Q.; Duarte, Júlio César; Oliveira, Jauvane C. de; Thomaz, Victor de Almeida; Giraldi, Gilson A.

doi:10.1109/svr.2014.13

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…To address this problem, BoF approach has been applied in reducing feature dimensions, redundancy elimination, and extracting global information from local SIFT features [36]. Moreover, the BoF approach has been considered as an efficient method to represent visual contents in hand gesture recognition [37]. The local feature points extracted from SIFT are fed into clustering algorithm to learn visual codebook and then each feature vector is mapped to a visual codeword represented by a sparse histogram.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Symbolic Gestures Using Depth Information

Mahmud

Hasan

Abdullah-Al-Tariq

et al. 2018

Advances in Human-Computer Interaction

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Symbolic gestures are the hand postures with some conventionalized meanings. They are static gestures that one can perform in a very complex environment containing variations in rotation and scale without using voice. The gestures may be produced in different illumination conditions or occluding background scenarios. Any hand gesture recognition system should find enough discriminative features, such as hand-finger contextual information. However, in existing approaches, depth information of hand fingers that represents finger shapes is utilized in limited capacity to extract discriminative features of fingers. Nevertheless, if we consider finger bending information (i.e., a finger that overlaps palm), extracted from depth map, and use them as local features, static gestures varying ever so slightly can become distinguishable. Our work here corroborated this idea and we have generated depth silhouettes with variation in contrast to achieve more discriminative keypoints. This approach, in turn, improved the recognition accuracy up to 96.84%. We have applied Scale-Invariant Feature Transform (SIFT) algorithm which takes the generated depth silhouettes as input and produces robust feature descriptors as output. These features (after converting into unified dimensional feature vectors) are fed into a multiclass Support Vector Machine (SVM) classifier to measure the accuracy. We have tested our results with a standard dataset containing 10 symbolic gesture representing 10 numeric symbols (0-9). After that we have verified and compared our results among depth images, binary images, and images consisting of the hand-finger edge information generated from the same dataset. Our results show higher accuracy while applying SIFT features on depth images. Recognizing numeric symbols accurately performed through hand gestures has a huge impact on different Human-Computer Interaction (HCI) applications including augmented reality, virtual reality, and other fields.

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Section: Related Workmentioning

confidence: 99%

Recognition of Symbolic Gestures Using Depth Information

Mahmud

Hasan

Abdullah-Al-Tariq

et al. 2018

Advances in Human-Computer Interaction

View full text Add to dashboard Cite

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“…Augmented reality applications use a series of artificial intelligence techniques such as: neural networks [14], Vector support machines [9], Machine learning techniques [3], color segmentation [4], to achieve results that increase the user experience in different areas [10] [13], either by mixing reality with the interaction of objects with augmented reality [8] or just by performing projections of images, symbols [7], information or simple lines projections that simulate the possible balls trajectories in the billiard game.…”

Section: Introductionmentioning

confidence: 99%

Billiard game parameters calculation using a depth camera for augmented reality applications

Pinzon¹,

González²,

Gómez³

2017

ces

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The billiard game presents a challenge for players who have to imagine the ball paths, this game raises an issue that requires the application of the classical mechanical physics. The objective of that work is to generate a tool that calculates the ball paths on a billiard table, using an infrared camera as a depth sensor and a projector to indicate the results of the calculated path; using the balls and the cue contours, the principles of mechanical physics are applied dynamically to diagram the possible move in real time. A projected image is obtained over the table, which generates an absolute realism feeling, also the opening angle and use of the depth sensor raise on the surface of the table, which allows to calculate 4 rebounds of the projected path, all this with less than a one second delay, which generates the fluidity feeling for the simulation. A prediction system for the billiard game was obtained, however it does not guarantee the success of the play through external conditions, nevertheless it can give a good idea of the behavior and interaction of the balls. regarding the acquisition of the image, the system due to the changes of environmental light conditions.

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Exploring the Museum with a Handheld Projector in Your Own Room

Yan

Ding²

2017

Transactions on Edutainment XIII

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A System to Interact with CAVE Applications Using Hand Gesture Recognition from Depth Data

Cited by 5 publications

References 16 publications

Recognition of Symbolic Gestures Using Depth Information

Recognition of Symbolic Gestures Using Depth Information

Billiard game parameters calculation using a depth camera for augmented reality applications

Exploring the Museum with a Handheld Projector in Your Own Room

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