The problem of pose estimation arises in many areas of computer vision, including object recognition, object tracking, site inspection and updating, and autonomous navigation when scene models are available. We present a new algorithm, called SoftPOSIT, for determining the pose of a 3D object from a single 2D image when correspondences between object points and image points are not known. The algorithm combines the iterative softassign algorithm (Gold and Rangarajan, 1996; Gold et al., 1998) for computing correspondences and the iterative POSIT algorithm (DeMenthon and Davis, 1995) for computing object pose under a full-perspective camera model. Our algorithm, unlike most previous algorithms for pose determination, does not have to hypothesize small sets of matches and then verify the remaining image points. Instead, all possible matches are treated identically throughout the search for an optimal pose. The performance of the algorithm is extensively evaluated in Monte Carlo simulations on synthetic data under a variety of levels of clutter, occlusion, and image noise. These tests show that the algorithm performs well in a variety of difficult scenarios, and empirical evidence suggests that the algorithm has an asymptotic run-time complexity that is better than previous methods by a factor of the number of image points. The algorithm is being applied to a number of practical autonomous vehicle navigation problems including the registration of 3D architectural models of a city to images, and the docking of small robots onto larger robots.

/pdf/softposit-simultaneous-pose-and-correspondence-determination-36l3bliqs7.pdf

SoftPOSIT: Simultaneous Pose and Correspondence Determination

Considerable progress has been made in handwriting recognition technology over the last few years. Thus far, handwriting recognition systems have been limited to small and medium vocabulary applications, since most of them often rely on a lexicon during the recognition process. The capability of dealing with large lexicons, however, opens up many more applications. This article will discuss the methods and principles that have been proposed to handle large vocabularies and identify the key issues affecting their future deployment. To illustrate some of the points raised, a large vocabulary off-line handwritten word recognition system will be described.

/pdf/large-vocabulary-off-line-handwriting-recognition-a-survey-2rmehscx29.pdf

Large vocabulary off-line handwriting recognition: A survey

The problem of pose estimation arises in many areas of computer vision, including object recognition, object tracking, site inspection and updating, and autonomous navigation using scene models. We present a new algorithm, called SoftPOSIT, for determining the pose of a 3D object from a single 2D image in the case that correspondences between model points and image points are unknown. The algorithm combines Gold's iterative SoftAssign algorithm [19, 20] for computing correspondences and DeMenthon's iterative POSIT algorithm [13] for computing object pose under a full-perspective camera model. Our algorithm, unlike most previous algorithms for this problem, does not have to hypothesize small sets of matches and then verify the remaining image points. Instead, all possible matches are treated identically throughout the search for an optimal pose. The performance of the algorithm is extensively evaluated in Monte Carlo simulations on synthetic data under a variety of levels of clutter, occlusion, and image noise. These tests show that the algorithm performs well in a variety of difficult scenarios, and empirical evidence suggests that the algorithm has a run-time complexity that is better than previous methods by a factor equal to the number of image points. The algorithm is being applied to the practical problem of autonomous vehicle navigation in a city through registration of a 3D architectural models of buildings to images obtained from an on-board camera.

/pdf/softposit-simultaneous-pose-and-correspondence-determination-4f0g2dxxrr.pdf

Image Analysis and Computer Vision

This paper discusses a novel and effective technique for extracting multiple ellipses from an image, using a genetic algorithm with multiple populations (MPGA). MPGA evolves a number of subpopulations in parallel, each of which is clustered around an actual or perceived ellipse in the target image. The technique uses both evolution and clustering to direct the search for ellipses--full or partial. MPGA is explained in detail, and compared with both the widely used randomized Hough transform (RHT) and the sharing genetic algorithm (SGA). In thorough and fair experimental tests, using both synthetic and real-world images, MPGA exhibits solid advantages over RHT and SGA in terms of accuracy of recognition--even in the presence of noise or/and multiple imperfect ellipses in an image--and speed of computation.

/pdf/a-multi-population-genetic-algorithm-for-robust-and-fast-1vvfc9klxn.pdf

A multi-population genetic algorithm for robust and fast ellipse detection

The authors describe a method for the recognition of cursively handwritten words using hidden Markov models (HMMs). The modelling methodology used has previously been successfully applied to the recognition of both degraded machine-printed text and hand-printed numerals. A novel lexicon-driven level building (LDLB) algorithm is proposed, which incorporates a lexicon directly within the search procedure and maintains a list of plausible match sequences at each stage of the search, rather than decoding using only the most likely state sequence. A word recognition rate of 93.4% is achieved using a 713 word lexicon, compared to just 49.8% when the same lexicon is used to post-process the results produced by a standard level building algorithm. Various procedures are described for the normalisation of cursive script. Results are presented on a single-author database of scanned text. It is shown how very high reliability, up to near perfect recognition, can be achieved by using a threshold to reject those word hypotheses to which the system assigns a low confidence. At 19% rejection, 99.2% of accepted words appeared in the top two choices produced by the system, and 100% of the 1645 accepted words were correctly recognised within the top eight choices.

Cursive handwriting recognition using hidden Markov models and a lexicon-driven level building algorithm

We present a new method for the recognition of polyhedral objects from 2D images based on geometric hashing. Rather than the point-based approach of previous geometric hashing implementations, which tend to be rather sensitive to image noise and spurious data, our method is based on triples of connected edges. As well as improving the robustness of the system, the use of higher level feature groupings results in a very efficient specialisation of the geometric hashing paradigm. Theoretical analyses of the ForeSight method show that it is more than ten times as fast as a comparable point-based geometric hashing implementation, while using only one-quarter of the memory. These results were confirmed by practical experiments on a database of 50 real images, in which the recognition rate achieved by ForeSight approached twice that of the conventional method.

ForeSight: fast object recognition using geometric hashing with edge-triple features

We apply an HMM-based text recognition system to the recognition of handwritten digit strings of unknown length. The algorithm is tailored to the input data by controlling the maximum number of levels searched by the level building (LB) search algorithm. We demonstrate that setting this parameter according to the pixel length of the observation sequence, rather than using a fixed value for all input data, results in a faster and more accurate system. Best results were achieved by setting the maximum number of levels to twice the estimated number of characters in the input string. We also describe experiments which show the potential for further improvement by using an adaptive termination criterion in the LB search.

The recognition of handwritten digit strings of unknown length using hidden Markov models

This paper considers an experimental comparison of the Randomised Hough Transform, RHT, and a genetic algorithm, GA, for the difficult problem of extraction of ellipses from binary edge data. The comparison is interesting as the methods share many common features. In both methods minimal subsets which represent the ellipse are selected. The methods differ in the way that these subsets are subsequently treated. In the RHT, subset hypotheses are accumulated and evaluated only after a significant cluster of parameters is found. In the GA method all minimal subsets are evaluated and then recombined to produce better potential solutions. Our experiments show that the methods perform similarly in terms of accuracy of parameter estimation and are mainly distinguished by computational cost. Quantitative results are presented for the cost of the algorithms as a function of their main parameters and the amount of additional noise points present in the image. The results form the basis for making a reasoned choice of which method to use in a specified situation.

A comparison of the randomised hough transform and a genetic algorithm for ellipse extraction

A study of several methods for combining information from two classifiers in a system for the recognition of handwritten text is presented. The system uses two hidden Markov models (HMMs) per character to model columns and rows of pixels in the character image. We show that the best method of combining the results from the vertical and horizontal classifiers is simply to multiply the probabilities produced by the two methods. This approach outperforms more complicated classifier combination strategies such as the behaviour-knowledge space (BKS) method.

S. Procter

Papers

Cursive handwriting recognition using hidden Markov models and a lexicon-driven level building algorithm

ForeSight: fast object recognition using geometric hashing with edge-triple features

The recognition of handwritten digit strings of unknown length using hidden Markov models

A comparison of the randomised hough transform and a genetic algorithm for ellipse extraction

Combining HMM classifiers in a handwritten text recognition system