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Cryptogram Decoding For Optical Character Recognition, Gary Huang, Erik G. Learned-Miller, Andrew Mccallum
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
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 …
Combinatorial Markov Random Fields, Ron Bekkerman, Erik Learned-Miller
Combinatorial Markov Random Fields, Ron Bekkerman, Erik Learned-Miller
Erik G Learned-Miller
A combinatorial random variable is a discrete random variable defined over a combinatorial set (e.g., a power set of a given set). In this paper we introduce combinatorial Markov random fields (Comrafs), which are Markov random fields where some of the nodes are combinatorial random variables. We argue that Comrafs are powerful models for unsupervised and semi-supervised learning. We put Comrafs in perspective by showing their relationship with several existing models. Since it can be problematic to apply existing inference techniques for graphical models to Comrafs, we design two simple and efficient inference algorithms specific for Comrafs, which are based …
Improved Generative Models For Continuous Image Features Through Tree-Structured Non-Parametric Distributions, Marwan Mattar, Erik Learned-Miller
Improved Generative Models For Continuous Image Features Through Tree-Structured Non-Parametric Distributions, Marwan Mattar, Erik Learned-Miller
Erik G Learned-Miller
Density estimation arises in a wide range of vision problems and methods which can deal with high dimensional image features are of great importance. While in principle a non- parametric distribution can be estimated for the full featu re distribution using Parzen win- dows technique, the amount of data to make these estimates accurate is usually either unattainable or unmanageable. Consequently, most modelers resort to parametric models such as mixtures of Gaussians (or other more complicated parametric forms) or make in- dependence assumptions about the features. Such assumptions could be detrimental to the performance of vision systems since realistically, image …
The Processing And Analysis Of In Situ Gene Expression Images Of The Mouse Brain, Manjunatha Jagalur1,, Chris Pal, Erik Learned-Miller, R Zoeller, David Kulp
The Processing And Analysis Of In Situ Gene Expression Images Of The Mouse Brain, Manjunatha Jagalur1,, Chris Pal, Erik Learned-Miller, R Zoeller, David Kulp
Erik G Learned-Miller
Many important high throughput projects use in situ gene expression detection technology and require the analysis of images of spatial cross sections of organisms taken at cellular level resolution. Projects creating gene expression atlases at unprecedented scales for the embryonic fruit fly as well as the embryonic and adult mouse already involve the analysis of hundreds of thousands of high resolution experimental images. We present an end-to-end approach for processing raw in situ expression imagery and performing subsequent analysis. We use a nonlinear image registration technique specifically adapted for mapping expression images to anatomical annotations and a method for extracting …
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
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
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
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
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
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. …