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A Binary Entropy Measure To Assess Nonrigid Registration Algorithms, Simon K. Warfield, Jan Rexilius, Petra S. Huppi, Terrie E. Inder, Erik G. Learned-Miller, William M. Wells Iii, Gary P. Zientara, Ferenc A. Jolesz, Ron Kikinis
A Binary Entropy Measure To Assess Nonrigid Registration Algorithms, Simon K. Warfield, Jan Rexilius, Petra S. Huppi, Terrie E. Inder, Erik G. Learned-Miller, William M. Wells Iii, Gary P. Zientara, Ferenc A. Jolesz, Ron Kikinis
Erik G Learned-Miller
Assessment of normal and abnormal anatomical variability requires a coordinate system enabling inter-subject comparison. We present a binary minimum entropy criterion to assess affine and nonrigid transformations bringing a group of subject scans into alignment. This measure is a data-driven measure allowing the identification of an intrinsic coordinate system of a particular group of subjects. We assessed two statistical atlases derived from magnetic resonance imaging of newborn infants with gestational age ranging from 24 to 40 weeks. Over this age range major structural changes occur in the human brain and existing atlases are inadequate to capture the resulting anatomical variability. …
Color Eigenflows : Statistical Modeling Of Joint Color Changes, Erik G. Learned-Miller, Kinh Tieu
Color Eigenflows : Statistical Modeling Of Joint Color Changes, Erik G. Learned-Miller, Kinh Tieu
Erik G Learned-Miller
We develop a linear model of commonly observed joint color changes in images due to variation in lighting and certain non-geometric camera parameters. This is done by observing how all of the colors are mapped between two images of the same scene under various “real-world” lighting changes. We represent each instance of such a joint color mapping as a 3-D vector field in RGB color space. We show that the variance in these maps is well represented by a low dimensional linear subspace of these vector fields. We dub the principal components of this space the color eigenflows. When applied …
An Intrinsic Coordinate System Of The Developing Human Brain, Simon K. Warfield, Petra S. Huppi, Terrie E. Inder, Erik G. Learned-Miller, William M. Wells Iii, Gary P. Zientara, Ferenc A. Jolesz, Ron Kikinis
An Intrinsic Coordinate System Of The Developing Human Brain, Simon K. Warfield, Petra S. Huppi, Terrie E. Inder, Erik G. Learned-Miller, William M. Wells Iii, Gary P. Zientara, Ferenc A. Jolesz, Ron Kikinis
Erik G Learned-Miller
No abstract provided.
Lighting Invariance Through Joint Color Change Models, Erik G. Learned-Miller, Kinh Tieu, Eric Grimson
Lighting Invariance Through Joint Color Change Models, Erik G. Learned-Miller, Kinh Tieu, Eric Grimson
Erik G Learned-Miller
In [9], we introduced a linear statistical model of joint color changes in images due to variation in lighting and certain non-geometric camera parameters. We did this by measuring the mappings of colors in one image of a scene to colors in another image of the same scene under different lighting conditions. In this paper, we extend our model in several ways and examine its applicability to several important problems in machine vision. The extensions to our model include incorporating a model of image noise and a prior on the color flows used to explain a particular image difference. In …
Learning Object-Independent Modes Of Variation With Feature Flow Fields, Erik G. Learned-Miller, Kinh Tieu, Chris Stauffer
Learning Object-Independent Modes Of Variation With Feature Flow Fields, Erik G. Learned-Miller, Kinh Tieu, Chris Stauffer
Erik G Learned-Miller
We present a unifying framework in which object-independent modes of variation are learned from continuous-time data such as video sequences. These modes of variation can be used as generators to produce a manifold of images of a new object from a single example of that object. We develop the framework in the context of a well-known example: analyzing the modes of spatial deformations of a scene under camera movement. Our method learns a close approximation to the standard affine deformations that are expected from the geometry of the situation, and does so in a completely unsupervised (i.e. ignorant of the …