Visual Recognition of Continuous Hand Postures  

Oleh:

Ritter, H. ; Nolker, C.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 13 no. 4 (2002), page 983-994.
Topik: recognition; visual recognition; hand postures

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.7A Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

This paper describes GREFIT (Gesture REcognition based on FInger Tips), a neural network - based system which recognizes continuous hand postures from gray - level video images (posture capturing). Our approach yields a full identification of all finger joint angles (making, however, some assumptions about joint couplings to simplify computations). This allows a full reconstruction of the three - dimensional (3 - D) hand shape, using an articulated hand model with 16 segments and 20 joint angles. GREFIT uses a two - stage approach to solve this task. In the first stage, a hierarchical system of artificial neural networks (ANN s) combined with a priori knowledge locates the two - dimensional (2 - D) positions of the finger tips in the image. In the second stage, the 2 - D position information is transformed by an ANN into an estimate of the 3 - D configuration of an articulated hand model, which is also used for visualization. This model is designed according to the dimensions and movement possibilities of a natural human hand. The virtual hand imitates the user's hand to an remarkable accuracy and can follow postures from gray scale images at a frame rate of 10 Hz.

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