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[diderot] Diff of /trunk/src/image-info/dnorm-format.txt
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Diff of /trunk/src/image-info/dnorm-format.txt

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revision 108, Tue Jun 15 17:13:55 2010 UTC revision 109, Wed Jun 23 07:25:26 2010 UTC
# Line 7  Line 7 
7  an empty line ('\n' alone).  Some header lines give per-array  an empty line ('\n' alone).  Some header lines give per-array
8  information, some give per-axis information.  information, some give per-axis information.
9    
10    Note that the distinction between scalar and non-scalar images is
11    given by the per-axis "kinds" field, so that is where further
12    information is given about the constraints associated iwth non-scalar
13    image data.
14    
15  ============================================================  ============================================================
16  The basic fields that should be there are:  The basic fields that should be there are:
# Line 92  Line 96 
96  The "space" kind is for a typical axis of the image, with samples that  The "space" kind is for a typical axis of the image, with samples that
97  subtend some region of the spatial domain of the image.  All the other  subtend some region of the spatial domain of the image.  All the other
98  kinds are the sequence of scalar values that compose a non-scalar  kinds are the sequence of scalar values that compose a non-scalar
99  image value (vectors and tensors).  We are currently not allowing the  image value (vectors and tensors).
100  "measurement frame" field that identifies the coordinate system in  
101  which these are measured relative to the image orientation, so nothing  We currently allow there to be *at* *most* *one* axis with a
102  should be assumed about the space in which these coefficients are  non-"space" kind.  We can have non-scalar image values, but we don't
103  measured:  have cartesian products of non-scalar image values.  The non-"space"
104    kind can be anywhere in the axis ordering.  If all the axis kinds are
105    "space", then then image data is for scalars, else it is for
106    non-scalar data.
107    
108    In scalar data, the image "dimension" must equal the "space dimension".
109    In non-scalar data, the image "dimension" must equal *one* plus
110    the "space dimension".  That is, the spatial axes have to form a
111    basis, not necessarily orthonormal, for world space (the vectors
112    are defined by the "space directions" field, below).  Thus, a
113    3-D scalar image can't reside in a 2-D or 4-D world space, nor can
114    a 3-D vector image reside in a 3-D or 5-D world space.
115    
116    We are currently not allowing the "measurement frame" field that
117    identifies the coordinate system in which these are measured relative
118    to the image orientation, so nothing should be assumed about the space
119    in which these coefficients are measured.  The order of coefficients
120    for these non-scalar kinds is:
121    
122  2D-symmetric-matrix: Mxx Mxy Myy  2D-symmetric-matrix: Mxx Mxy Myy
123  2D-matrix: Mxx Mxy Myx Myy  2D-matrix: Mxx Mxy Myx Myy
124  3D-symmetric-matrix: Mxx Mxy Mxz Myy Myz Mzz  3D-symmetric-matrix: Mxx Mxy Mxz Myy Myz Mzz
125  3D-matrix: Mxx Mxy Mxz Myx Myy Myz Mzx Mzy Mzz  3D-matrix: Mxx Mxy Mxz Myx Myy Myz Mzx Mzy Mzz
126    
 We currently allow there to be *at* *most* *one* axis with a  
 non-"space" kind. We can have non-scalar image values, but we don't  
 have cartesian products of non-scalar image values.  The non-"space"  
 kind can be anywhere in the axis ordering.  
   
127  ----------------  ----------------
128  space directions: (<x1>,<x2>,...,<xN>) (<x1>,<x2>,...,<xN>) ...  space directions: (<x1>,<x2>,...,<xN>) (<x1>,<x2>,...,<xN>) ...
129  (and at most one of these vectors is actually "none")  (and at most one of these vectors is actually "none")

Legend:
Removed from v.108  
changed lines
  Added in v.109

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