Conference Papers

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    Projection and interaction with ad-hoc interfaces on non-planar surfaces
    (IEEE Computer Society help@computer.org, 2013) Dere, K.S.; Guddeti, G.
    Projector-based display systems have been used in area of computer interaction as Ad-hoc interface in recent time. The mobile hand-held projectors are becoming more popular. Many human centric user interfaces with the human wearable computer are being developed. Most of such system uses daily objects for projection and the interaction. But most of ignores the fact that these object surfaces are not planar. Hence such interfaces suffers from the distortion due to non-planar projection surface. Besides this projection quality also suffers from the radiometric distortion as well. Further more the interaction proposed with such interfaces bound to the planar surface only. Hence this paper is targeted to address the geometric distortion free projection of and interaction with such interfaces on non planar surfaces. Kinect is used as depth sensor for 3D scenario acquisition. We use imageper warping to mesh from Kinect. We use colored fingertip gloves for interaction. Here our system aims any day to day object surface for distortion free projection such as human body, curved wall, room corners, curtain's and many more objects. © 2013 IEEE.
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    Kinect based real-time gesture spotting using HCRF
    (2013) Chikkanna, M.; Guddeti, G.
    The sign language is an effective way of communication for deaf and dumb people. This paper proposes, developing the gesture spotting algorithm for Indian Sign Language that acquires sensory information from Microsoft Kinect Sensor. Our framework consists of three main stages: hand tracking, feature extraction and classification. In the first stage, hand tracking is carried out using frames of Kinect. In second stage, the features of Cartesian system (velocity, angle, location) and hand with respect to body are extracted. K-means is used for extracting the codewords of features for HCRF. In the third stage, Hidden Conditional Random Field is used for classification. The experimental results show that HCRF algorithm gives 95.20% recognition rate for the test data. In real-time, the recognition rate achieves 93.20% recognition rate. © 2013 IEEE.