Computer vision and image recognition have been extensively applied in industrial production and traditional manufacture for automatic and refinement purposes. Through such image processing techniques, effective quality control would be attained and thereby improve manufacture processes as well as reduce manufacture costs.
For example, during a wafer manufacturing process, defects could occur on wafer surfaces due to equipments, environment, and human causes. Currently, wafer surface defects are mainly inspected through comparing features within an image of a wafer surface captured by an image capturing device and each block in its reference image, and all defect information on the wafer surface could be thereby obtained. The existing approaches for defect inspection are mainly done by subtracting two images with corresponding blocks to obtain a difference image and determining whether each pixel value of the difference image is greater than a preset threshold. However, when color cast, color shift, uneven brightness, or noise interference occurs in a captured image, false defects would be detected after the captured image is compared with its reference image. This would result in an inaccurate image alignment for comparison.