The general version of resampling is mixed, however. A new image 
raster is created, having a different density (cell size) and orien- 
tation than the original raster. A specific case is the epipolar 
lay-out of pixels. 
Resampling comprises two basic stages, i.e., calculation of the new 
pixel locations (geometric lay-out) and interpolation of the corres- 
ponding intensity levels from the neighbouring old pixels. Resam-: 
pling is accompanied with some loss of pictorial data; it should 
therefore not be applied cyclically in an iterative process. 
5. Data segmentation and structuring 
Image data segmentation and their structuring are interelated, and 
they strongly reflect the strategy of image matching. Segmentation 
and structuring can refer to the spectral domains (spatial or elec- 
tromagnetic), to spatial entities (i.e., lay-out of patches, seg- 
ments and pixels), or to a combination of both. 
Lay-out of the spatial data entities (patches, segments) can be 
fixed (and homogeneous) or variable, e.g. adapted during pre-proces- 
sing or matching. Data entities can be arranged in a single level 
or in several hierarchical levels (2). 
A homogeneous lay-out of data entities can be single or multiple. In 
the latter case segments can be shifted in x and/or y, (e.g., for 
half interval),changed in size (which changes overlap of segments), 
etc. 
Further variation is attained by interchanging the roles of target 
and search segments (i.e., LH versus RH image). Moreover, segment 
size (of target) can be fixed or variable. In the latter case, it 
can be adapted during pre-processing or during matching. 
6. Image transformations 
Pictorial (intensity) transformations and matching algorithms are 
strongly interelated; a transformation may be regarded as an inte- 
gral part of matching. Examples are Fourier, Hadamard, and exponen- 
tial transforms, which can serve for image analysis and segmenta- 
tion. Transformations are accompanied with some loss of image data. 
7. Filtering and thresholding 
Linear filters are commonly applied in order to compensate for de- 
gradation at imaging (by inverse filters) and to suppress noise (by 
smoothing filters). Filters and thresholds can be aplied in selec- 
tive pre-processing, i.e., to enhance certain image features, for 
segmenting and/or compressing image data, for interpolation, etc. 
Different linear filters can be merged (by convolution) into a com- 
posed filter, which increases efficiency of processing. 
8. Synthesising image data 
The reliability of iamge matching can be increased by creating syn- 
thetic image data from the original (or pre-processed) data.  Exam- 
ples are envelope data (3), compressed second difference data (2), 
etc. 
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