Jayashree Subrahmonia

Describing complicated objects by implicit polynomials

This paper introduces and focuses on two problems. First is the representation power of closed implicit polynomials of modest degree for curves in 2-D images and surfaces in 3-D range data. Super quadrics are a small subset of object boundaries that are well fitted by these polynomials. The second problem is the stable computationally efficient fitting of noisy data by closed implicit polynomial curves and surfaces. The attractive features of these polynomials for Vision is discussed. >

Practical reliable Bayesian recognition of 2D and 3D objects using implicit polynomials and algebraic invariants

We treat the use of more complex higher degree polynomial curves and surfaces of degree higher than 2, which have many desirable properties for object recognition and position estimation, and attack the instability problem arising in their use with partial and noisy data. The scenario discussed in this paper is one where we have a set of objects that are modeled as implicit polynomial functions, or a set of representations of classes of objects with each object in a class modeled as an implicit polynomial function, stored in the database. Then, given partial data from one of the objects, we want to recognize the object (or the object class) or collect more data in order to get better parameter estimates for more reliable recognition. Two problems arising in this scenario are discussed: 1) the problem of recognizing these polynomials by comparing them in terms of their coefficients; and 2) the problem of where to collect data so as to improve the parameter estimates as quickly as possible. We use an asymptotic Bayesian approximation for solving the two problems. The intrinsic dimensionality of polynomials and the use of the Mahalanobis distance are discussed.

Pen computing: challenges and applications

Pen computing as a fieId broadly includes computers and applications in which a pen is the main input device. This field continues to draw a lot of attention from researchers because there are a number of applications where the pen is the most convenient form of input. These include: 1. preparing a first draft of a document and concentrating on content creation; 2. a socially acceptable form of capturing information in meetings, that is quieter than typing and creates minimal visual barrier; 3. applications that need privacy; 4. entering letters in ideographic languages like Chinese and Japanese and non-letter entries like graphics, music and gestures; and 5. interaction with multi-modal systems. The advent of electronic tablets in the late 1950s precipitated considerable activity in the area of pen computing. This interest ebbed in the 1970s, and was renewed in the 1980s, primarily due to advances in pen hardware technology and improvement in user-interfaces and handwriting recognition algorithms. There are still however, a number of challenges that need to be addressed before pen computing can address the needs listed above to a acceptable level of user satisfaction. In the paper, an overview of three aspects of pen computing are presented: pen input hardware, handwriting recognition and pen computer applications.

Writer dependent recognition of on-line unconstrained handwriting

In this paper, we present a framework for adapting a writer independent system to a user from samples of the users writing. The writer independent system is modeled using hidden Markov models. Training for a writer involves recomputing the topology and parameters of the hidden Markov models using the writers data. The framework uses the writer independent system to get an initial alignment of the writers data. The system described reduces the error rate by an average of 65%. For the results presented, no language model was used.

Initialization of hidden Markov models for unconstrained on-line handwriting recognition

In a hidden Markov model system, the initialization of the model parameters is critical to the performance of the model after retraining. This paper proposes a number of new approaches to the problem of initialization, and demonstrates that a method of smooth alignment results in the best performance.

HMM topology optimization for handwriting recognition

This paper addresses the problem of hidden Markov model (HMM) topology estimation in the context of on-line handwriting recognition. HMM have been widely used in applications related to speech and handwriting recognition with great success. One major drawback with these approaches, however, is that the techniques that they use for estimating the topology of the models (number of states, connectivity between the states and the number of Gaussians), are usually heuristically derived, without optimal certainty. This paper addresses this problem, by comparing a couple of commonly used heuristically derived methods to an approach that uses the Bayesian information criterion (BIC) for computing the optimal topology. Experimental results on discretely written letters show that using BIC gives comparable results to heuristic approaches with a model that has nearly 10% fewer parameters.

A Bayesian model selection criterion for HMM topology optimization

This paper addresses the problem of estimating the optimal Hidden Markov Model (HMM) topology. The optimal topology is defined as the one that gives the smallest error-rate with the minimal number of parameters. The paper introduces a Bayesian model selection criterion that is suitable for Continuous Hidden Markov Models topology optimization. The criterion is derived from the Laplacian approximation of the posterior of a model structure, and shares the algorithmic simplicity of conventional Bayesian selection criteria, such as Schwarzs Bayesian Information Criterion (BIC). Unlike, BIC, which uses a multivariate Normal distribution assumption for the prior of all parameters of the model, the proposed HMM-oriented Bayesian Information Criterion (HBIC), models each parameter by a different distribution, one more appropriate for that parameter The results on an handwriting recognition task shows that the HBIC realizes a much smaller and efficient system than a system generated through the BIC.

Model-based segmentation and estimation of 3D surfaces from two or more intensity images using Markov random fields

An approach and algorithm for 3D primitive model recognition, parameter estimation, and segmentation from a sequence of images taken by one or more calibrated cameras are presented. Though the approach and algorithm are applicable to more general models, the experiments described are for primitive objects that are 3D planes. Given two or more images taken by one or more calibrated cameras, the algorithm simultaneously segments the images and 3D space into regions, each region associated with a single planar patch, and estimates the parameters of the 3D plane associated with each segmented region. The algorithm is suitable for parallel processing and should function at close to the best possible accuracy. Markov random fields are used to provide very coarse prior knowledge of the regions occupied by the planar patches, resulting in markedly enhanced accuracy.<<ETX>>

Confidence modeling for handwriting recognition: algorithms and applications

Confidence scoring can assist in determining how to use imperfect handwriting-recognition output. We explore a confidence-scoring framework for post-processing recognition for two purposes: deciding when to reject the recognizers output, and detecting when to change recognition parameters e.g., to relax a word-set constraint. Varied confidence scores, including likelihood ratios and posterior probabilities, are applied to an Hidden-Markov-Model (HMM) based on-line recognizer. Receiver-operating characteristic curves reveal that we successfully reject 90% of word recognition errors while rejecting only 33% of correctly-recognized words. For isolated digit recognition, we achieve 90% correct rejection while limiting false rejection to 13%.

Size normalization in on-line unconstrained handwriting recognition

In an on-line handwriting recognition system, the motion of the tip of the stylus (pen) is sampled at equal time intervals using a digitizer tablet and the sampled points are passed to a computer which performs the handwriting recognition. In most cases, the basic recognition algorithm performs best for a nominal size of writing as well as a standard orientation (normally horizontal) and a nominal slant (normally fully upright). We discuss and provide solutions to these normalization problems in the context of on-line handwriting recognition. Most of the results presented are also valid for optical character recognition (OCR). Error rate reductions of 54.3% and 35.8% were obtained for the writer-dependent and writer-independent samples through using the proposed normalization scheme.<<ETX>>