Local Standard Deviation Image Processing


Local Standard Deviation Image Processing

Local Standard Deviation Image Processing

The method described in the previous section was quite handy but in practice, it was not suitable for all kind of lemons. A lemon turning from green to yellow has yellow patches of green and possesses strong color contrast even if it does not have any defect.

To overcome this difficulty another method was proposed. The whole image was divided into 16×16 patches. The standard deviation was calculated for each patch thus the name, local standard deviation. The method calculated standard deviation in each 16×16 patch locally, independent of global context. The patches having higher standard deviation was categorized as defective because, in a local neighborhood, pixel values should not spread significantly for a defect-free surface. The only defective region has high standard deviation. The computed standard deviation was stored in a matrix having elements equal to the number of 16×16 patched in the image.

It was determined experimentally that the border of fruit had high standard deviation even for good fruits because the patches at the border have wider pixel value distribution beyond mean. The morphological operation was performed to remove some border values. Most patches in the fruit region had values of standard deviation in the range of 0 to 1 even for smoother skin because of little bumps on the lemon surface. So the patch comprising of vale 1 was set to zero. Remaining patches, where a value of standard deviation was non-zero were added together and were used as feature M.Features from both center-surround and local 8×8 patches standard deviation was used for defective fruit detection.


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