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Full-Text Articles in Physical Sciences and Mathematics
Building A Classification Cascade For Visual Identification From One Example, Andras Ferencz, Erik G. Learned-Miller, Jitendra Malik
Building A Classification Cascade For Visual Identification From One Example, Andras Ferencz, Erik G. Learned-Miller, Jitendra Malik
Erik G Learned-Miller
Object identification (OID) is specialized recognition where the category is known (e.g. cars) and the algorithm recognizes an object's exact identity (e.g. Bob's BMW). Two special challenges characterize OID. (1) Interclass variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. (2) There may be many classes but few or just one positive "training" examples per class. Due to (1), a solution must locate possibly subtle object-specific salient features (a door handle) while avoiding distracting ones (a specular highlight). However, (2) rules out direct techniques of feature selection. We describe an online algorithm …
Joint Mri Bias Removal Using Entropy Minimization Across Images, Erik G. Learned-Miller, Parvez Ahammad
Joint Mri Bias Removal Using Entropy Minimization Across Images, Erik G. Learned-Miller, Parvez Ahammad
Erik G Learned-Miller
The correction of bias in magnetic resonance images is an important problem in medical image processing. Most previous approaches have used a maximum likelihood method to increase the likelihood of the pixels in a single image by adaptively estimating a correction to the unknown image bias field. The pixel likelihoods are defined either in terms of a pre-existing tissue model, or non-parametrically in terms of the image's own pixel values. In both cases, the specific location of a pixel in the image is not used to calculate the likelihoods. We suggest a new approach in which we simultaneously eliminate the …
A Probabilistic Upper Bound On Differential Entropy, Joseph Destefano, Erik G. Learned-Miller
A Probabilistic Upper Bound On Differential Entropy, Joseph Destefano, Erik G. Learned-Miller
Erik G Learned-Miller
A novel probabilistic upper bound on the entropy of an unknown one-dimensional distribution, given the support of the distribution and a sample from that distribution, is presented. No knowledge beyond the support of the unknown distribution is required. Previous distribution-free bounds on the cumulative distribution function of a random variable given a sample of that variable are used to construct the bound. A simple, fast, and intuitive algorithm for computing the entropy bound from a sample is provided.
Detecting Acromegaly: Screening For Disease With A Morphable Model, Qifeng Lu, Erik G. Learned-Miller, Angela Paisley, Peter Trainer, Volker Blanz, Katrin Dedden, Ralph Miller
Detecting Acromegaly: Screening For Disease With A Morphable Model, Qifeng Lu, Erik G. Learned-Miller, Angela Paisley, Peter Trainer, Volker Blanz, Katrin Dedden, Ralph Miller
Erik G Learned-Miller
Acromegaly is a rare disorder which affects about 50 of every million people. The disease typically causes swelling of the hands, feet, and face, and eventually permanent changes to areas such as the jaw, brow ridge, and cheek bones. The disease is often missed by physicians and progresses beyond where it might if it were identified and treated earlier. We consider a semi-automated approach to detecting acromegaly, using a novel combination of support vector machines (SVMs) and a morphable model. Our training set consists of 24 frontal photographs of acromegalic patients and 25 of disease-free subjects. We modelled each subject's …
Sign Classification For The Visually Impaired, Marwan A. Mattar, Allen R. Hanson, Erik G. Learned-Miller
Sign Classification For The Visually Impaired, Marwan A. Mattar, Allen R. Hanson, Erik G. Learned-Miller
Erik G Learned-Miller
Our world is populated with visual information that a sighted person makes use of daily. Unfortunately, the visually impaired are deprived from such information, which limits their mobility in unconstrained environments. To help alleviate this we are developing a wearable system that is capable of detecting and recognizing signs in natural scenes. The system is composed of two main components, sign detection and recognition. The sign detector, uses a conditional maximum entropy model to find regions in an image that correspond to a sign. The sign recognizer matches the hypothesized sign regions with sign images in a database. The system …
Efficient Population Registration Of 3d Data, Lilla Zöllei, Erik G. Learned-Miller, Eric Grimson, William Wells
Efficient Population Registration Of 3d Data, Lilla Zöllei, Erik G. Learned-Miller, Eric Grimson, William Wells
Erik G Learned-Miller
We present a population registration framework that acts on large collections or populations of data volumes. The data alignment procedure runs in a simultaneous fashion, with every member of the population approaching the central tendency of the collection at the same time. Such a mechanism eliminates the need for selecting a particular reference frame a priori, resulting in a non-biased estimate of a digital atlas. Our algorithm adopts an affine congealing framework with an information theoretic objective function and is optimized via a gradientbased stochastic approximation process embedded in a multi-resolution setting. We present experimental results on both synthetic and …
Sign Classification Using Local And Meta-Features, Marwan A. Mattar,, Allen R. Hanson,, Erik G. Learned-Miller
Sign Classification Using Local And Meta-Features, Marwan A. Mattar,, Allen R. Hanson,, Erik G. Learned-Miller
Erik G Learned-Miller
Our world is populated with visual information that a sighted person makes use of daily. Unfortunately, the visually impaired are deprived from such information, which limits their mobility in unconstrained environments. To help alleviate this we are developing a wearable system [1, 19] that is capable of detecting and recognizing signs in natural scenes. The system is composed of two main components, sign detection and recognition. The sign detector, uses a conditional maximum entropy model to find regions in an image that correspond to a sign. The sign recognizer matches the hypothesized sign regions with sign images in a database. …