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Selected Works

Erik G Learned-Miller

2006

Articles 1 - 7 of 7

Full-Text Articles in Physical Sciences and Mathematics

Cryptogram Decoding For Optical Character Recognition, Gary Huang, Erik G. Learned-Miller, Andrew Mccallum Jul 2006

Cryptogram Decoding For Optical Character Recognition, Gary Huang, Erik G. Learned-Miller, Andrew Mccallum

Erik G Learned-Miller

OCR systems for printed documents typically require large numbers of font styles and character models to work well. When given an unseen font, performance degrades even in the absence of noise. In this paper, we perform OCR in an unsupervised fashion without using any character models by using a cryptogram decoding algorithm. We present results on real and artificial OCR data.


The Umass Mobile Manipulator Uman: An Experimental Platform For Autonomous Mobile Manipulation, Dov Katz, Emily Horrell, Yuandong Yang, Brendan Burns, Thomas Buckley, Anna Grishkan, Volodymyr Zhylkovskyy, Oliver Brock, Erik G. Learned-Miller Jul 2006

The Umass Mobile Manipulator Uman: An Experimental Platform For Autonomous Mobile Manipulation, Dov Katz, Emily Horrell, Yuandong Yang, Brendan Burns, Thomas Buckley, Anna Grishkan, Volodymyr Zhylkovskyy, Oliver Brock, Erik G. Learned-Miller

Erik G Learned-Miller

Object identification is the task of identifying specific objects belonging to the same class such as cars. We often need to recognize an object that we have only seen a few times. In fact, we often observe only one example of a particular object before we need to recognize it again. Thus we are interested in building a system which can learn to extract distinctive markers from a single example and which can then be used to identify the object in another image as “same ” or “different”. Previous work by Ferencz et al. introduced the notion of hyper-features, which …


Detecting Acromegaly: Screening For Disease With A Morphable Model, Erik G. Learned-Miller, Qifeng Lung, Angela Paisley, Peter Trainer, Volker Blanz, Katrin Dedden, Ralph Miller Dec 2005

Detecting Acromegaly: Screening For Disease With A Morphable Model, Erik G. Learned-Miller, Qifeng Lung, 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 …


Data Driven Image Models Through Continuous Joint Alignment, Erik G. Learned-Miller Dec 2005

Data Driven Image Models Through Continuous Joint Alignment, Erik G. Learned-Miller

Erik G Learned-Miller

This paper presents a family of techniques that we call congealing for modeling image classes from data. The idea is to start with a set of images and make them appear as similar as possible by removing variability along the known axes of variation. This technique can be used to eliminate "nuisance” variables such as affine deformations from handwritten digits or unwanted bias fields from magnetic resonance images. In addition to separating and modeling the latent images—i.e., the images without the nuisance variables—we can model the nuisance variables themselves, leading to factorized generative image models. When nuisance variable distributions are …


Joint Feature Selection For Object Detection And Recognition, Jerod J. Weinman, Allen Hanson, Erik G. Learned-Miller Dec 2005

Joint Feature Selection For Object Detection And Recognition, Jerod J. Weinman, Allen Hanson, Erik G. Learned-Miller

Erik G Learned-Miller

Object detection and recognition systems, such as face detectors and face recognizers, are often trained separately and operated in a feed-forward fashion. Selecting a small number of features for these tasks is important to prevent over-fitting and reduce computation. However, when a system has such related or sequential tasks, selecting features for these tasks independently may not be optimal. We propose a framework for choosing features to be shared between object detection and recognition tasks. The result is a system that achieves better performance by joint training and is faster because some features for identification have already been computed for …


Discriminative Training Of Hyper-Feature Models For Object Identification, Vidit Jain, Erik G. Learned-Miller Dec 2005

Discriminative Training Of Hyper-Feature Models For Object Identification, Vidit Jain, Erik G. Learned-Miller

Erik G Learned-Miller

Object identification is the task of identifying specific objects belonging to the same class such as cars. We often need to recognize an object that we have only seen a few times. In fact, we often observe only one example of a particular object before we need to recognize it again. Thus we are interested in building a system which can learn to extract distinctive markers from a single example and which can then be used to identify the object in another image as “same ” or “different”. Previous work by Ferencz et al. introduced the notion of hyper-features, which …


Improving Recognition Of Novel Input With Similarity, Jerod J. Weinman, Erik G. Learned-Miller Dec 2005

Improving Recognition Of Novel Input With Similarity, Jerod J. Weinman, Erik G. Learned-Miller

Erik G Learned-Miller

Many sources of information relevant to computer vision and machine learning tasks are often underused. One example is the similarity between the elements from a novel source, such as a speaker, writer, or printed font. By comparing instances emitted by a source, we help ensure that similar instances are given the same label. Previous approaches have clustered instances prior to recognition. We propose a probabilistic framework that unifies similarity with prior identity and contextual information. By fusing information sources in a single model, we eliminate unrecoverable errors that result from processing the information in separate stages and improve overall accuracy. …