Changes between Version 11 and Version 12 of libpipi/research/filters Tweet

Timestamp:

08/22/2008 09:56:08 PM (16 years ago)

Author:

Sam Hocevar

Comment:

--

libpipi/research/filters

@@ -31 +31 @@
 The third way to optimise a median filter is by accounting for the size of neighbourhood changes in raster scan order: usually only a few values are removed from and added to the list of neighbours. This becomes promising with large kernel sizes, and efficient algorithms exist that handle this case in 1D at least ![2].
 
-Today, median filtering methods use O(r) techniques, but O(log(r)) ones exist, too [4].
+Today, median filtering methods use O(r) techniques, but O(log(r)) ones exist, too ![4].
 
 There is even a method in O(1) [3]. However, like many others, it relies on histograms, which only works well when the input data consists of 8-bit integers. Since libpipi does most of its computations using 32-bit floats, such techniques are of limited usefulness (but should be proposed anyway, because we have ways to tell whether an image has fewer than 8 bits per component).
@@ -59 +59 @@
 Erosion and dilation already work, but only for nearest pixels.
 
+There are techniques to make dilate by X O(1) in infinite distance ![1]. Other distances are easily made O(r) with possible optimisations based on kernel symmetry.
+
 {{{
 #!html
@@ -71 +73 @@
 {{{
 #!html
-<p><span style="background: red; color: yellow; padding: 0 3px; font-weight: bold;">TODO</span> implement "dilate by X" (Euclidian distance).
+<p><span style="background: red; color: yellow; padding: 0 3px; font-weight: bold;">TODO</span> implement "dilate by X" (euclidian distance).
+}}}
+
+{{{
+#!html
+<p><span style="background: red; color: yellow; padding: 0 3px; font-weight: bold;">TODO</span> implement "dilate by X" (infinite distance).
 }}}
 
@@ -79 +86 @@
 === Links ===
  * [http://ostermiller.org/dilate_and_erode.html Efficiently Implementing Dilate and Erode Image Functions]
+
+=== References ===
+ * ![1]  Gil, J.   Werman, M., [http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=211471  Computing 2-D min, median, and max filters], IEEE Transactions on Pattern Analysis and Machine Intelligence, 15, 504-507, 1993