# HG changeset patch
# User Robert McIntyre <rlm@mit.edu>
# Date 1365661199 0
# Node ID 80cd096682b22dab63022f3b6e22a79f5c1bc9a0
# Parent  057d47fc4789ded634147d8ba06379a23e19c466
reviewing ullman's stuff.

diff -r 057d47fc4789 -r 80cd096682b2 org/literature-review.org
--- a/org/literature-review.org	Thu Apr 11 05:40:23 2013 +0000
+++ b/org/literature-review.org	Thu Apr 11 06:19:59 2013 +0000
@@ -208,6 +208,8 @@
 cross-validation = train the model on two different sets to prevent
 overfitting. 
 
+nifty, relevant, realistic ideas
+He doesn't confine himself to unplasaubile assumptions
 
 
 
@@ -239,15 +241,20 @@
     better with few training points, and the more complex ones do
     better with many training points.
 
+    The final model had intermediate complexity between published
+    extremes. 
+
     The more complex models must be able to be initialized efficiently
     from the less complex models which they replace!
 
 
 ** Non Parametric Models
 
-*** Visual features of intermediate complexity and their use in classification
+*** 2002 Visual features of intermediate complexity and their use in classification
 
-*** The chains model for detecting parts by their context
+    
+
+*** 2010 The chains model for detecting parts by their context
 
     Like the constelation method for rigid objects, but extended to
     non-rigid objects as well.
@@ -257,4 +264,60 @@
     ambiguous in an image, but if you are expecting them at the end of
     an arm, then they become easier to find.
 
-    
\ No newline at end of file
+    They make chains by using spatial proximity of features. That way,
+    a hand can be idntified by chaining back from the head. If there
+    is a good chain to the head, then it is more likely that there is
+    a hand than if there isn't. Since there is some give in the
+    proximity detection, the system can accomodate new poses that it
+    has never seen before.
+
+    Does not use any motion information.
+
+*** 2005 A Hierarchical Non-Parametric Method for Capturing Non-Rigid Deformations
+    
+    (relative dynamic programming [RDP])
+
+    Goal is to match images, as in SIFT, but this time the images can
+    be subject to non rigid transformations. They do this by finding
+    small patches that look the same, then building up bigger
+    patches. They get a tree of patches that describes each image, and
+    find the edit distance between each tree. Editing operations
+    involve a coherent shift of features, so they can accomodate local
+    shifts of patches in any direction. They get some cool results
+    over just straight correlation. Basically, they made an image
+    comparor that is resistant to multiple independent deformations.
+    
+    !important small regions are treated the same as nonimportant
+     small regions
+     
+    !no conception of shape
+    
+    quote:
+    The dynamic programming procedure looks for an optimal
+    transformation that aligns the patches of both images. This
+    transformation is not a global transformation, but a composition
+    of many local transformations of sub-patches at various sizes,
+    performed one on top of the other.
+
+*** 2006 Satellite Features for the Classification of Visually Similar Classes
+    
+    Finds features that can distinguish subclasses of a class, by
+    first finding a rigid set of anghor features that are common to
+    both subclasses, then finding distinguishing features relative to
+    those subfeatures. They keep things rigid because the satellite
+    features don't have much information in and of themselves, and are
+    only informative relative to other features.
+
+*** 2005 Learning a novel class from a single example by cross-generalization.
+
+    Let's you use a vast visual experience to generate a classifier
+    for a novel class by generating synthetic examples by replaceing
+    features from the single example with features from similiar
+    classes.
+
+    quote: feature F is likely to be useful for class C if a similar
+    feature F proved effective for a similar class C in the past.
+
+    Allows you to trasfer the "gestalt" of a similiar class to a new
+    class, by adapting all the features of the learned class that have
+    correspondance to the new class.
\ No newline at end of file