Extracts local binary patterns in local histograms and concatenate these histograms
Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.
Parameters allow users to change the configuration of an algorithm when scheduling an experiment
|lbp_neighbours||Number of neighbours considered for the LBP bin computation||uint32||8|
|left-eye-x||The horizontal position of the left eye (subject perspective) in the cropped image||uint32||48|
|left-eye-y||The vertical position of the left eye (subject perspective) in the cropped image||uint32||16|
|crop-height||The height of the cropped image||uint32||80|
|lbp_radius||Radius considered in the LBP bin computation||uint32||1|
|crop-width||The width of the cropped image||uint32||64|
|right-eye-x||The horizontal position of the right eye (subject perspective) in the cropped image||uint32||15|
|right-eye-y||The vertical position of the right eye (subject perspective) in the cropped image||uint32||16|
The code for this algorithm in Python
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This algorithms extracts Local Binary Pattern Histogram Sequence (LBPHS) features as introduced by [Ahonen04]. First, the image is aligned accorsing to the hand-labeled eye locations. Then, uniform circular Local Binary Patterns (LBPs) [Ojala96] are extracted from the aligned image. Afterwards, the image is split into (possibly overlapping) blocks, and a local histogram of LBP features is extracted for each block. Finally, all histograms are concatenated to form the full LBPHS feature vector.