Using hidden Markov models (HMM's), an unobstrusive single view camera system is developed that can recognize hand gestures, namely, a subset of American Sign Language (ASL). Previous systems have concentrated on finger spelling or isolated word recognition, often using tethered electronic gloves for input. We achieve high recognition rates for full sentence ASL using only visual cues. A forty word lexicon consisting of personal pronouns, verbs, nouns, and adjectives is used to create 494 randomly constructed five word sentences that are signed by the subject to the computer. The data is separated into a 395 sentence training set and an independent 99 sentence test set. While signing, the 2D position, orientation, and eccentricity of bounding ellipses of the hands are tracked in real time with the assistance of solidly colored gloves. Simultaneous recognition and segmentation of the resultant stream of feature vectors occurs five times faster than real time on an HP 735. With a strong ...

This paper provides a review of shape analysis methods. Shape analysis methods play an important role in systems for object recognition, matching, registration, and analysis. Researchin shape analysis has been motivated, in part, by studies of human visual form perception systems.

Abstract—An overview of statistical and information-theoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discrete-time finite-state homogeneous Markov chain observed through a discrete-time memoryless invariant channel. In recent years, the work of Baum and Petrie on finite-state finite-alphabet HMPs was expanded to HMPs with finite as well as continuous state spaces and a general alphabet. In particular, statistical properties and ergodic theorems for relative entropy densities of HMPs were developed. Consistency and asymptotic normality of the maximum-likelihood (ML) parameter estimator were proved under some mild conditions. Similar results were established for switching autoregressive processes. These processes generalize HMPs. New algorithms were developed for estimating the state, parameter, and order of an HMP, for universal coding and classification of HMPs, and for universal decoding of hidden Markov channels. These and other related topics are reviewed in this paper. Index Terms—Baum–Petrie algorithm, entropy ergodic theorems, finite-state channels, hidden Markov models, identifiability, Kalman filter, maximum-likelihood (ML) estimation, order estimation, recursive parameter estimation, switching autoregressive processes, Ziv inequality. I.

A method for the off-line recognition of cursive handwriting based on Hidden Markov Models (HMMs) is described. The features used in the HMMs are based on the arcs of skeleton graphs of the words to be recognized. An average correct recognition rate of over 98% on the word level has been achieved in experiments with cooperative writers using two dictionaries of 150 words each. CR categories and Subject Description: I.5: Pattern Recognition; I.4: Image Processing General Terms: Algorithms Additional key words: Optical character recognition, cursive script recognition, offline recognition, hidden Markov model, skeleton graphs 1 1 Introduction Optical character recognition (OCR) has been receiving much attention in the past few years although it is one of the oldest and most intensively studied subfields of pattern recognition [1]. The growing interest in this field has been driven by the steadily increasing power of computing machinery and an expanding commercial market. In OCR,...

The real-time, interactive feedback system we developed, Sensei: The T'ai Chi Teacher, is presented. This system provides a good platform on which to build a more sophisticated teaching, training, and feedback tool for gestures or action. In this document, we hope to substantiate that thesis by showing that Sensei does have the components necessary to be a good foundation. First of all, it is capable of performing real-time, multiple user, gesture recognition. Users of the system are free to practice T'ai Chi gestures, starting anywhere in the sequence of moves, and the system recognizes their actions. In addition, a complete teaching system must be able to give both positive and critical feedback to the user. This ability implies a knowledge of the instants in the user's performance of a gesture where the user was both least and most accurate in the movement. Both tasks are accomplished through the use of Hidden Markov Models. Experiments testing these abilities are presented. The wor...

This work describes a statistical approach to deal with learning and recognition problems in the field of computer vision. An abstract theoretical framework is provided, which is suitable for automatic model generation from examples, identification, and localization of objects. Both, the learning and localization stage are formalized as parameter estimation tasks. The statistical learning phase is unsupervised with respect to the matching of model and scene features. The general mathematical description yields algorithms which can even treat parameter estimation problems from projected data. The experiments show that this probabilistic approach is suitable for solving 2D and 3D object recognition problems using grey--level images. The method can also be applied to 3D image processing issues using range images, i.e. 3D input data. 1 Introduction Many object recognition systems are discussed in the literature [7, 8]. The huge variety of approaches are occupied with the development and ...

This thesis presents an integrated approach in modeling, extracting, detecting and classifying deformable contours directly from noisy images. We begin by conducting a case study on regularization, formulation and initialization of the active contour models (snakes). Using minimax principle, we derive a regularization criterion whereby the values can be automatically and implicitly determined along the contour. Furthermore, we formulate a set of energy functionals which yield snakes that contain Hough transform as a special case. Subsequently, we consider the problem of modeling and extracting arbitrary deformable contours from noisy images. We combine a stable, invariant and unique contour model with Markov random field to yield prior distribution that exerts influence over an arbitrary global model while allowing for deformation. Under the Bayesian framework, contour extraction turns into posterior estimation, which is in turn equivalent to energy minimization in a generalized active...

AbstractÐIn this paper, we present a technique using Markov models with spectral features for recognizing 2D shapes. We will analyze the properties of Fourier spectral features derived from closed contours of 2D shapes and use these features for 2D pattern recognition. We develop algorithms for reestimating parameters of hidden Markov models. To demonstrate the effectiveness of our models, we have tested our methods on two image databases: hand-tools and unconstrained handwritten numerals. We are able to achieve high recognition rates of 99.4 percent and 96.7 percent without rejection on these two sets of image data, respectively. Index TermsÐHidden Markov models, spectral features, 2D shape recognition, outer contours, handwritten numeral recognition. 1

ABSTRACT: One of the content based image retrieval techniques is the shape based technique which allows users to ask for objects similar in shape to a query object. We propose a novel method for shape representation and similarity measure which we call the grid based method and evaluate its performance by comparing it with two popular methods, namely, the Fourier descriptors method and the moment invariants method. The comparative study is done by performing the same queries on the same database for all the methods. We also test the methods for robustness to noise by performing the queries on a database of noisy shapes. Experimental results show that the method proposed by us performs favourably compared with the other two methods. 1.

Abstract—This paper proposes a method for detecting object classes that exhibit variable shape structure in heavily cluttered images. The term “variable shape structure ” is used to characterize object classes in which some shape parts can be repeated an arbitrary number of times, some parts can be optional, and some parts can have several alternative appearances. Hidden State Shape Models (HSSMs), a generalization of Hidden Markov Models (HMMs), are introduced to model object classes of variable shape structure using a probabilistic framework. A polynomial inference algorithm automatically determines object location, orientation, scale, and structure by finding the globally optimal registration of model states with the image features, even in the presence of clutter. Experiments with real images demonstrate that the proposed method can localize objects of variable shape structure with high accuracy. For the task of hand shape localization and structure identification, the proposed method is significantly more accurate than previously proposed methods based on chamfer-distance matching. Furthermore, by integrating simple temporal constraints, the proposed method gains speed-ups of more than an order of magnitude and produces highly accurate results in experiments on nonrigid hand motion tracking. Index Terms—Object detection, shape modeling, probabilistic algorithms, dynamic programming. 1