Abstract: A forward interpolation approach is disclosed for enabling a second version of an image to be constructed from a first version of the image. According to one implementation of the forward interpolation approach, pixels from the first version of the image are processed one row at a time. As the pixels in a row of pixels in the first version of the image are processed, they may cause pixel values on different rows of the second version of the image to be determined. Since the pixel values of the second version of the image are stored in output line buffers, this means that, at any particular point in time, there may be multiple partially filled output line buffers. It has been observed that the forward interpolation approach enables significant benefits (such as reduced storage requirements and reduced internal bandwidth and processing) to be achieved over a backward interpolation approach.

Abstract: A forward interpolation approach is disclosed for enabling a second version of an image to be constructed from a first version of the image. According to one implementation, an input pixel from the first version of the image is forward mapped to the second version of the image to determine a set of candidate pixels that may be affected by the input pixel. Each candidate pixel is then backward mapped to the first version of the image to determine whether they are actually affected by the input pixel. For each candidate pixel that is actually affected by the input pixel, a pixel value is determined for that candidate pixel based at least in part upon the pixel value of the input pixel. By using this forward and backward mapping technique, forward interpolation can be implemented quickly and efficiently.

Abstract: A forward interpolation approach is disclosed for enabling a second version of an image to be constructed from a first version of the image. According to one implementation, pixels from the first version of the image are mapped to pixels in the second version of the image, and pixel values are determined for the corresponding pixels in the second version of the image. In one implementation, the pixel mapping is performed using a lookup table and linear approximation. Performing the pixel mapping in this manner enables computations to be simplified and cost and gate count to be reduced.

Abstract: Methods for estimating joint geometric and radiometric deformations relating two observations of the same object provide methods for identifying and matching images (as in face recognition) and for characterizing and determining the relationship between a pair of observations (as in target recognition).

Abstract: An optical system can provides a distorted image of an object within a field of view onto sensing pixels of an image capturing device. The optical system can expand the image in a center of the field of view and compress the image in a periphery or introduce other distortion. The distortion intentionally introduced by the optical system is corrected when the sensing pixels are read to remove some or all of the distortion and thereby produce a “rectified” image. The pixels can be read along a trajectory corresponding to a curvature map of the distorted image to rectify distortions during pixel read out, rather than waiting until all or substantially all of the sensing pixels have been read. Sensor logic and/or algorithms can be used in removing the distortion.