A mobile phone that responds to hand signals rather than the touch of a button may soon be possible thanks to technology developed by A*STAR researchers that efficiently detects three-dimensional human hand movements from two-dimensional images in real time1. Combining this technology with devices such as laptops or mobile phones can facilitate robot control and enable human-computer interactions.
Extracting correct three-dimensional hand poses from a single image, especially in the presence of complex background signals, is challenging for computers. As the computer has to determine the general position of the hand, as well as each finger, the extraction of hand movements requires the analysis of many parameters. Optimizing these parameters at the same time would be extremely computationally demanding.
To simplify these calculations, Li Cheng and colleagues from the A*STAR Bioinformatics Institute developed a model that breaks this process into two steps. First, the general position of the hand and the wrist is determined. As a second step, the palm and individual finger poses are established using the anatomy of the human hand as a guide for the computer to narrow its options. Separating the task into two steps reduces the overall complexity of the algorithm, and accelerates computations.
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A*STAR researchers have developed a program that is able to detect three-dimensional hand gestures from low-resolution depth images in real time. © Michael Blann/DigitalVision/Thinkstock
International Journal of Computer Vision - Estimate Hand Poses Efficiently from Single Depth Images
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Extracting correct three-dimensional hand poses from a single image, especially in the presence of complex background signals, is challenging for computers. As the computer has to determine the general position of the hand, as well as each finger, the extraction of hand movements requires the analysis of many parameters. Optimizing these parameters at the same time would be extremely computationally demanding.
To simplify these calculations, Li Cheng and colleagues from the A*STAR Bioinformatics Institute developed a model that breaks this process into two steps. First, the general position of the hand and the wrist is determined. As a second step, the palm and individual finger poses are established using the anatomy of the human hand as a guide for the computer to narrow its options. Separating the task into two steps reduces the overall complexity of the algorithm, and accelerates computations.
A*STAR researchers have developed a program that is able to detect three-dimensional hand gestures from low-resolution depth images in real time. © Michael Blann/DigitalVision/Thinkstock
International Journal of Computer Vision - Estimate Hand Poses Efficiently from Single Depth Images
Read more »
