Doco upgrade

anaglyph.m

@@ -5,6 +5,8 @@
 % different colors.  By default the left image is red, and the right 
 % image is cyan.
 %
+% ANAGLYPH(LEFT, RIGHT) as above but display the anaglyph.
+%
 % A = ANAGLYPH(LEFT, RIGHT, COLOR) as above but the string COLOR describes
 % the color coding as a string with 2 letters, the first for left, the second 
 % for right, and each is one of:
@@ -19,7 +21,19 @@
 % correction.  If DISP is positive the disparity is increased, if negative it
 % is reduced.  These adjustments are achieved by trimming the images.  Use 
 % this option to make the images more natural/comfortable to view, useful 
-% if the images were captured with a non-human stereo baseline or field of view.
+% if the images were captured with a stereo baseline significantly different
+% the human eye separation (typically 65mm).
+%
+% Example::
+% Load the left and right images
+%         L = iread('rocks2-l.png', 'reduce', 2);
+%         R = iread('rocks2-r.png', 'reduce', 2);
+% then display the anaglyph for viewing with red-cyan glasses
+%         anaglyph(L, R);
+%
+% References::
+%  - Robotics, Vision & Control, Section 14.3,
+%    P. Corke, Springer 2011.
 %
 % See also STDISP.
 

cmfxyz.m

@@ -8,12 +8,16 @@
 % is the color matching function of the corresponding element of LAMBDA. 
 %
 % XYZ = CMFXYZ(LAMBDA, E) is the CIE XYZ color matching (1x3) function for an 
-% illumination spectrum defined by intensity E (Nx1) and wavelength 
-% LAMBDA (Nx1) [m].
+% illumination spectrum E (Nx1) defined at corresponding wavelengths
+% LAMBDA (Nx1).
 %
 % Note::
 % - CIE 1931 2-deg XYZ CMFs from cvrl.ioo.ucl.ac.uk
 %
+% References::
+%  - Robotics, Vision & Control, Section 14.3,
+%    P. Corke, Springer 2011.
+%
 % See also CMFRGB, CCXYZ.
 
 

colorkmeans.m

@@ -32,10 +32,10 @@
 % Notes::
 % - The k-means clustering algorithm used in the first three forms is
 %   computationally expensive and time consuming.
-% - Clustering is performed in rg-chromaticity space.
+% - Clustering is performed in xy-chromaticity space.
 % - The residual is an indication of quality of fit, low is good.
 %
-% See also
+% See also RGB2XYZ, KMEANS.
 
 
 

iblobs.m

@@ -8,9 +8,10 @@
 %  'connect',C	     set connectivity, 4 (default) or 8
 %  'greyscale'	     compute greyscale moments 0 (default) or 1
 %  'boundary'        compute boundary (default off)
-%  'area',[A1,A2]    accept only blobs with area in interval A1 to A2
-%  'shape',[S1,S2]   accept only blobs with shape in interval S1 to S2
-%  'touch'           ignore blobs that touch the edge (default accept)
+%  'area',[A1,A2]    accept only blobs with area in the interval A1 to A2
+%  'shape',[S1,S2]   accept only blobs with shape in the interval S1 to S2
+%  'touch',T         accept only blobs that touch (1) or do not touch (0)
+%                    the edge (default accept all)
 %  'class',C         accept only blobs of pixel value C (default all)
 %
 % The RegionFeature object has many properties including:
@@ -37,11 +38,15 @@
 %  circularity   1 for a circle, less for other shapes
 %  moments       a structure containing moments of order 0 to 2
 %
+% References::
+%  - Robotics, Vision & Control, Section 13.1,
+%    P. Corke, Springer 2011.
+
 % Notes::
-% - the RegionFeature objects are ordered by the raster order of the top most
+% - The RegionFeature objects are ordered by the raster order of the top most
 %   point (smallest v coordinate) in each blob.
 %
-% See also RegionFeature, ILABEL, IMOMENTS.
+% See also RegionFeature, ILABEL, IDISPLABEL, IMOMENTS.
 
 
 % Copyright (C) 1993-2011, by Peter I. Corke

iconcat.m

@@ -1,25 +1,34 @@
 %ICONCAT Concatenate images
 %
-% C = ICONCAT(IM,OPTIONS) concatenates images from the cell array IM.  The
-% images do not have to be of the same size, and smaller images are surrounded
-% by background pixels which can be specified.
+% C = ICONCAT(IM,OPTIONS) concatenates images from the cell array IM.
 %
 % ICONCAT(IM,OPTIONS) as above but displays the concatenated images 
-% using idisp.
+% using IDISP.
 %
 % [C,U] = ICONCAT(IM,OPTIONS) as above but also returns the vector U whose
 % elements are the coordinates of the left (or top in vertical mode) edge of
-% the corresponding image.
+% the corresponding image within the concatenated image.
 %
 % Options::
 % 'dir',D     direction of concatenation: 'horizontal' (default) or 'vertical'.
-% 'bgval',B   value of unset background pixels
+% 'bgval',B   value of padding pixels (default NaN)
+%
+% Examples::
+%
+% Horizontally concatenate three images
+%         c = iconcat({im1, im2, im3}, 'h'); 
+%
+% Find the first column of each of the three images
+%         [c,u] = iconcat({im1, im2, im3}, 'h'); 
+% where u is a 3-vector such that im3 starts in the u(3)'rd column of c.
 %
 % Notes::
-% - Works for color or greyscale images
-% - Direction can be abbreviated to first character, 'h' or 'v'
-% - In vertical mode all images are right justified
-% - In horizontal mode all images are top justified
+% - The images do not have to be of the same size, and smaller images are 
+%   surrounded by background pixels which can be specified.
+% - Works for color or greyscale images.
+% - Direction can be abbreviated to first character, 'h' or 'v'.
+% - In vertical mode all images are right justified.
+% - In horizontal mode all images are top justified.
 %
 % See also IDISP.
 

icorner.m

@@ -1,39 +1,52 @@
 %ICORNER Corner detector
 %
 % F = ICORNER(IM, OPTIONS) is a vector of PointFeature objects describing
-% detected corner features.  This is a non-scale space detector and by
-% default the Harris method is used.  If IM is an image sequence a cell array
-% of PointFeature vectors is returned.
+% corner features detected in the image IM.  This is a non-scale space detector 
+% and by default the Harris method is used but Shi-Tomasi and Noble are also
+% supported.
+%
+% If IM is an image sequence a cell array of PointFeature vectors for the 
+% correspnding frames of IM.
 %
 % The PointFeature object has many properties including:
 %  u            horizontal coordinate
 %  v            vertical coordinate
 %  strength     corner strength
 %  descriptor   corner descriptor (vector)
 %
+% See PointFeature for full details
+%
 % Options::
+% 'detector',D      choose the detector where D is one of 'harris' (default),
+%                   'noble' or 'klt'
+% 'sigma',S         kernel width for smoothing (default 2)
+% 'deriv',D         kernel for gradient (default kdgauss(2))
 % 'cmin',CM         minimum corner strength
 % 'cminthresh',CT   minimum corner strength as a fraction of maximum corner 
 %                   strength
-% 'edgegap',E       don't return features closer than E to the edge of 
+% 'edgegap',E       don't return features closer than E pixels to the edge of 
 %                   image (default 2)
 % 'suppress',R      don't return a feature closer than R pixels to an earlier 
 %                   feature (default 0)
 % 'nfeat',N         return the N strongest corners (default Inf)
-% 'detector',D      choose the detector where D is one of 'harris' (default),
-%                   'noble' or 'klt'
-% 'sigma',S         kernel width for smoothing (default 2)
-% 'deriv',D         kernel for gradient (default kdgauss(2))
-% 'k',K             set the value of k for Harris detector
+% 'k',K             set the value of k for the Harris detector
 % 'patch',P         use a PxP patch of surrounding pixel values as the 
 %                   feature vector.  The vector has zero mean and unit norm.
 % 'color'           specify that IM is a color image not a sequence
 %
+% Example::
+%
+% Compute the 100 strongest Harris features for the image
+%         c = icorner(im, 'nfeat', 100);
+% and overlay them on the image
+%         idisp(im);
+%         c.plot();
+%
 % Notes::
 % - Corners are processed in order from strongest to weakest.
 % - The function stops when:
-%     - the corner strength drops below cmin
-%     - the corner strenght drops below cMinThresh x strongest corner
+%     - the corner strength drops below cmin, or
+%     - the corner strength drops below cMinThresh x strongest corner, or
 %     - the list of corners is exhausted
 % - Features are returned in descending strength order
 % - If IM has more than 2 dimensions it is either a color image or a sequence
@@ -51,15 +64,15 @@
 % - "A combined corner and edge detector", 
 %   C.G. Harris and M.J. Stephens,
 %   Proc. Fourth Alvey Vision Conf., Manchester, pp 147-151, 1988.
-%
 % - "Finding corners", 
 %   J.Noble, 
 %   Image and Vision Computing, vol.6, pp.121-128, May 1988.
-%   
 % - "Good features to track",
 %   J. Shi and C. Tomasi, 
 %   Proc. Computer Vision and Pattern Recognition, pp. 593-593,
 %   IEEE Computer Society, 1994.
+%  - Robotics, Vision & Control, Section 13.3,
+%    P. Corke, Springer 2011.
 %
 % See also PointFeature, ISURF.
 
@@ -87,6 +100,7 @@
 
     % TODO, can handle image sequence, return 3D array of corner_strength if requested 
     % and cell array of corner vectors
+    % handle tiling
 
     % parse options into parameter struct
     opt.k = 0.04;

idouble.m

@@ -1,10 +1,15 @@
 %IDOUBLE Convert integer image to double
 %
-% IMD = IDOUBLE(IM) returns an image with double precision elements in the
-% range 0 to 1. The integer pixels are assumed to span the range 0 to the 
-% maximum value of their integer class.
+% IMD = IDOUBLE(IM) is an image with double precision elements in the
+% range 0 to 1 corresponding to the elements of IM. The integer pixels IM
+% are assumed to span the range 0 to the maximum value of their integer class.
 %
-% See also IINT.
+% Notes::
+% - Works for an image with arbitrary number of dimensions, eg. a color
+%   image or image sequence.
+% - There is a linear mapping (scaling) of the values of IMD to IM.
+%
+% See also IINT, CAST.
 
 
 % Copyright (C) 1993-2011, by Peter I. Corke

ierode.m

@@ -6,12 +6,26 @@
 % OUT = IERODE(IM, SE, N, OPTIONS) as above but the structuring element 
 % SE is applied N times, that is N erosions.
 %
+% Options::
+% 'border'   the border value is replicated (default)
+% 'none'     pixels beyond the border are not included in the window
+% 'trim'     output is not computed for pixels where the structuring element
+%            crosses the image border, hence output image had reduced 
+%            dimensions.
+% 'wrap'     the image is assumed to wrap around, left to right, top to
+%            bottom.
+% 
 % Notes::
 % - Cheaper to apply a smaller structuring element multiple times than
 %   one large one, the effective structuing element is the Minkowski sum
 %   of the structuring element with itself N times.
 % - Windowing options of IMORPH can be passed.
 %
+% Reference::
+%  - Robotics, Vision & Control, Section 12.5,
+%    P. Corke, Springer 2011.
+%
+%
 % See also IDILATE, ICLOSE, IOPEN, IMORPH.
 
 % Copyright (C) 1993-2011, by Peter I. Corke

iisum.m

@@ -1,7 +1,7 @@
 %IISUM Sum of integral image
 %
-% S = IISUM(II, Y1, Y2, X1, X2) is the sum of pixels in the rectangular image
-% region defined by its top-left (X1,Y1) and bottom-right (X2,Y2).  II is
+% S = IISUM(II, U1, V1, U2, V2) is the sum of pixels in the rectangular image
+% region defined by its top-left (U1,V1) and bottom-right (U2,V2).  II is
 % a precomputed integral image.
 %
 % See also INTGIMAGE.
@@ -24,7 +24,7 @@
 % You should have received a copy of the GNU Leser General Public License
 % along with MVTB.  If not, see <http://www.gnu.org/licenses/>.
 
-function s = iisum(ii, r1, r2, c1, c2)
+function s = iisum(ii, c1, r1, c2, r2)
 
     r1 = r1 - 1;
     if r1 < 1

imorph.m

@@ -2,7 +2,7 @@
 %
 % OUT = IMORPH(IM, SE, OP) is the image IM after morphological processing 
 % with the operator OP and structuring element SE.
-
+%
 % The structuring element SE is a small matrix with binary values that indicate
 % which elements of the template window are used in the operation. 
 %

iopen.m

@@ -1,16 +1,20 @@
 %IOPEN Morphological opening
 %
 % OUT = IOPEN(IM, SE, OPTIONS) is the image IM after morphological opening 
-% with the structuring element SE.  This is an erosion followed by dilation.
+% with the structuring element SE.  This is a morphological erosion followed 
+% by dilation.
 %
 % OUT = IOPEN(IM, SE, N, OPTIONS) as above but the structuring element 
 % SE is applied N times, that is N erosions followed by N dilations.
 %
 % Notes::
-% - Cheaper to apply a smaller structuring element multiple times than
-%   one large one, the effective structuing element is the Minkowski sum
+% - For binary image an opening operation can be used to eliminate small white
+%   noise regions.
+% - It is cheaper to apply a smaller structuring element multiple times than
+%   one large one, the effective structuring element is the Minkowski sum
 %   of the structuring element with itself N times.
-% - Windowing options of IMORPH can be passed.
+% - Windowing options of IMORPH can be passed.  By default output image is
+%   same size as input image.
 %
 % See also ICLOSE, IDILATE, IERODE, IMORPH.
 

ipad.m

@@ -1,15 +1,16 @@
 %IPAD Pad an image with constants
 %
 % OUT = IPAD(IM, SIDES, N) is a padded version of the image IM with a block 
-% of NaN values N pixels wide on the sides of IM as specified by SIDES which 
-% is a string containing one or more of the characters:
+% of NaN values N pixels wide on the sides of IM as specified by SIDES.
+%
+% OUT = IPAD(IM, SIDES, N, V) as above but pads with pixels of value V.
+%
+% SIDES is a string containing one or more of the characters:
 % 't'   top
 % 'b'   bottom
 % 'l'   left
 % 'r'   right
 %
-% OUT = IPAD(IM, SIDES, N, V) as above but pads with pixels of value V.
-%
 % Examples::
 %
 % Add a band of zero pixels 20 pixels high across the top of the image: