This paper presents a generalized form of the
mechanistic deconvolution technique (GMD) to modeling image
sensors applicable in various pan–tilt planes of view. The
mechanistic deconvolution technique (UMD) is modified with the
given angles of a pan–tilt plane of view to formulate constraint
parameters and characterize distortion effects, and thereby, determine
the corrected image data. This, as a result, does not require
experimental setup or calibration. Due to the mechanistic nature of
the sensor model, the necessity for the sensor image plane to be
orthogonal to its z-axis is eliminated, and it reduces the dependency
on image data. An experiment was constructed to evaluate the
accuracy of a model created by GMD and its insensitivity to changes
in sensor properties and in pan and tilt angles. This was compared
with a pre-calibrated model and a model created by UMD using two
sensors with different specifications. It achieved similar accuracy
with one-seventh the number of iterations and attained lower mean
error by a factor of 2.4 when compared to the pre-calibrated and
UMD model respectively. The model has also shown itself to be
robust and, in comparison to pre-calibrated and UMD model,
improved the accuracy significantly.I>/I>>