Matrix computations (3rd ed.)
Introduction to Linear Optimization
Introduction to Linear Optimization
Digital Color Management: Encoding Solutions
Digital Color Management: Encoding Solutions
Convex Optimization
Illumination normalization with time-dependent intrinsic images for video surveillance
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Image restoration in digital photography
IEEE Transactions on Consumer Electronics
IEEE Transactions on Image Processing
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In machine vision applications that involve comparing two images, it is necessary to match the capture conditions, which can affect their graylevels. Illumination and exposure are two important causes for lighting variation that we should compensate for in the resulting images. A standard technique for this purpose is to map one of the images to achieve the smallest mean square error (MSE) between the two. However, applications in defect detection for manufacturing processes are more challenging, because the existence of defects would affect the mapping significantly. In this paper, we present a robust method that is more tolerant to defects, and discuss its formulation as a linear programming to achieve fast implementations. This algorithm is also flexible and capable of incorporating further constraints, such as ensuring non-negativity of the pixel values.