This paper presents a vision based approach for capturing the hand postures and recognizing them. Authors have suggested the use of PCA for the location of hand in the image frame and then using ICA to obtain the intrinsic features from the hand to identify the hand motions.ICA has been described by the authors as a way to obtain a linear non-orthogonal coordinate system in any multivariate data. The goal of ICA is to perform linear transformation which makes the resulting variable as statistically independent from each other as possible.
They have represented the hand motions by modeling a Hand model in open GL and then using the information about the degrees of freedom of the hand fingers to obtain the various possible combinations available in which fingers touch the palm.
They used the data gloves to obtain the joint information of the possible 31 combinations . They then used that data to obtain the model parameters for various combinations generated by the open GL model over a time span and obtained around 2000 dimensional vector for each posture.
By PCA they reduces the dimensionality of the problem and were able to locate the position of the maximum variance in the image frame. Then by using the ICA model where each basis represents the motion of a particular finger they obtained the hand pose for a given time frame. They used the particle filtering method to track hands in accordance with the bayes theorem.
They employed the edge and silhouette model to match the hand frame with the open GL model and then estimated the closest match between the hand image and open GL models.By superimposing open GL model on hand image, they were able to recognize the posture.
Discussion:
I liked some different approach in this paper though I don't agree the statistically independent nature of fingers exists for all the hand postures. But considering the simplicity of their gestures, it might work. I likes that they used PCA for global hand tracking but PCA requires the bacground to be stable and only hand moving to track the variance. If there is some change in background (like the user moved a bit) PCA may give erroneous global results. Though for limited region, it may be feasible and simplest approach.
I would like to think more about the feasibility of ICA for intrinsic finger tracking, though presently I believe it is not possible to track fingers by this approach for the kind of complex motions we are aiming at,
They have represented the hand motions by modeling a Hand model in open GL and then using the information about the degrees of freedom of the hand fingers to obtain the various possible combinations available in which fingers touch the palm.
They used the data gloves to obtain the joint information of the possible 31 combinations . They then used that data to obtain the model parameters for various combinations generated by the open GL model over a time span and obtained around 2000 dimensional vector for each posture.
By PCA they reduces the dimensionality of the problem and were able to locate the position of the maximum variance in the image frame. Then by using the ICA model where each basis represents the motion of a particular finger they obtained the hand pose for a given time frame. They used the particle filtering method to track hands in accordance with the bayes theorem.
They employed the edge and silhouette model to match the hand frame with the open GL model and then estimated the closest match between the hand image and open GL models.By superimposing open GL model on hand image, they were able to recognize the posture.
Discussion:
I liked some different approach in this paper though I don't agree the statistically independent nature of fingers exists for all the hand postures. But considering the simplicity of their gestures, it might work. I likes that they used PCA for global hand tracking but PCA requires the bacground to be stable and only hand moving to track the variance. If there is some change in background (like the user moved a bit) PCA may give erroneous global results. Though for limited region, it may be feasible and simplest approach.
I would like to think more about the feasibility of ICA for intrinsic finger tracking, though presently I believe it is not possible to track fingers by this approach for the kind of complex motions we are aiming at,
2 comments:
I was optimistic of the potential of this paper, but this might have been partially attributed to my lack of familiarity to the techniques. Judging from the other comments, I guess I might have been too optimistic, heh. You seem to have more faith in their system, but seem to have expressed similar criticisms that the other shared in its limitations. I guess it would be safe to dismiss PCA and ICA, even combined, as a viable technique for haptics.
I think both PCA and ICA are good candidates for feature extraction for use in gesture recognition. I have yet to see anything that makes me want to completely dismiss them. The only thing I disagree with in this paper is, as you've pointed out, finger motions aren't completely independent. I think you could whiten the data to remove some of this correlation, however. But that might screw things up. :( Once you've got the independent components, it might be easy to measure the value of each finger and combine them hierarchically.
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