Summary:
This paper by LaViola presented a summary of key gesture recognition techniques. Hand posture and gesture recognition was divided up into several categories: feature extraction, statistics, models, and learning approaches. Some approaches, such as template matching, are more suited for postures, whereas HMMs are used solely for gestures. Feature extraction is used for both, but the feature set can be computationally heavy for the large dimension spaces.
Possible applications for gestures and postures include sign language, presentation assistance, 3D modeling, and virtual environments.
Discussion:
This paper is a good summary of current techniques and their strengths and weaknesses. There's not much to summarize in the paper since the summarizing an 80 page summary is rather dull and pointless, but I will be referring back to this paper for any future work.
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1 comment:
Yeah, I agree that this paper is a nice reference. Seems like a lot of gesture recognition papers already do so. Gives one's own research paper some street cred or something. West side! I'd reference it myself to see some other viable approaches worth checking out.
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