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Also, the methods in the first category are complementary to the second category.
The methods in the first group are independent of the learning algorithm and suggest transformation of MLC learning task into simpler tasks that any classifier can deal with.
The methods in the third category generate more stable gait features and are less sensitive towards noise as compared to the methods in the first category.
The methods in the first category can only work with limited range of view angles, and the accuracy of the methods can be affected by self-occlusion.
The methods in the first category operate by segmenting the input audio into meaningful events, and then attempt to recognise the content of the segments.
The recognition function requires 53 ms and 16 ms, respectively, for the detection and least median of squares methods in the first frame.
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All methods in the second category use a Jacobian-based numerical method.
The methods in the second category are able to provide reliable performance.
The methods in the second category relied on the color images or videos taken by digital cameras or camcorders.
Furthermore, the methods in the third category deploy a simpler camera setup as compared to those in the second category.
Recent research has however revealed the continuing importance of non-parliamentary methods in the eighteenth and nineteenth centuries.
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