Exact(5)
We make the histogram of the defect image to 40 bins and turn it into a feature vector as the gray-level feature.
In conventionally procedure, the image is divided into many small cells and calculated respectively, but in this paper, we make the histogram on the whole picture.
As Fig. 4 described, to get the ULBP feature, firstly, we convert the defect image to a ULBP image and then we make the histogram of the ULBP image and convert the histogram to a feature vector.
The denominator in Equation 9, (1+λ)I+2γ D T D, in fact corresponding to the term operates as the averaged histogram function to make the histogram smoother.
As the Fig. 3 described, we firstly convert the defect image to the orientation image (gray level range from 0° to 180°), then we make the histogram of the image with 50 channels.
Similar(54)
Only pixels that have saturation values of more than a threshold are used to make the histograms in (5).
However, inclusion of additional degrees of freedom in the free energy landscapes might make the histograms very sparse in individual windows and convergence might be more difficult.
To reduce 'overcounting' artifacts, before making the histogram we thinned the set of sequences using the 'remove redundancy' (threshold = 0.99) feature of Jalview.
This makes the histograms comparable during the classification process.
The decline of the EOH weight makes the color histogram more reliable.
However, to utilize the MapReduce programming framework, some changes have been made to the histogram structure.
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