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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 7 • Date July 1996

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Displaying Results 1 - 9 of 9
  • An experimental comparison of range image segmentation algorithms

    Publication Year: 1996 , Page(s): 673 - 689
    Cited by:  Papers (243)  |  Patents (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2316 KB)  

    A methodology for evaluating range image segmentation algorithms is proposed. This methodology involves (1) a common set of 40 laser range finder images and 40 structured light scanner images that have manually specified ground truth and (2) a set of defined performance metrics for instances of correctly segmented, missed, and noise regions, over- and under-segmentation, and accuracy of the recovered geometry. A tool is used to objectively compare a machine generated segmentation against the specified ground truth. Four research groups have contributed to evaluate their own algorithm for segmenting a range image into planar patches. View full abstract»

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  • A survey of methods and strategies in character segmentation

    Publication Year: 1996 , Page(s): 690 - 706
    Cited by:  Papers (209)  |  Patents (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1900 KB)  

    Character segmentation has long been a critical area of the OCR process. The higher recognition rates for isolated characters vs. those obtained for words and connected character strings well illustrate this fact. A good part of recent progress in reading unconstrained printed and written text may be ascribed to more insightful handling of segmentation. This paper provides a review of these advances. The aim is to provide an appreciation for the range of techniques that have been developed, rather than to simply list sources. Segmentation methods are listed under four main headings. What may be termed the “classical” approach consists of methods that partition the input image into subimages, which are then classified. The operation of attempting to decompose the image into classifiable units is called “dissection.” The second class of methods avoids dissection, and segments the image either explicitly, by classification of prespecified windows, or implicitly by classification of subsets of spatial features collected from the image as a whole. The third strategy is a hybrid of the first two, employing dissection together with recombination rules to define potential segments, but using classification to select from the range of admissible segmentation possibilities offered by these subimages. Finally, holistic approaches that avoid segmentation by recognizing entire character strings as units are described View full abstract»

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  • Large vocabulary recognition of on-line handwritten cursive words

    Publication Year: 1996 , Page(s): 757 - 762
    Cited by:  Papers (18)  |  Patents (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (624 KB)  

    This paper presents a writer independent system for large vocabulary recognition of on-line handwritten cursive words. The system first uses a filtering module, based on simple letter features, to quickly reduce a large reference dictionary (lexicon) to a more manageable size; the reduced lexicon is subsequently fed to a recognition module. The recognition module uses a temporal representation of the input, instead of a static two-dimensional image, thereby preserving the sequential nature of the data and enabling the use of a Time-Delay Neural Network (TDNN); such networks have been previously successful in the continuous speech recognition domain. Explicit segmentation of the input words into characters is avoided by sequentially presenting the input word representation to the neural network-based recognizer. The outputs of the recognition module are collected and converted into a string of characters that is matched against the reduced lexicon using an extended Damerau-Levenshtein function. Trained on 2,443 unconstrained word images (11 k characters) from 55 writers and using a 21 k lexicon we reached a 97.9% and 82.4% top-5 word recognition rate on a writer-dependent and writer-independent test, respectively View full abstract»

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  • N-tuple features for OCR revisited

    Publication Year: 1996 , Page(s): 734 - 745
    Cited by:  Papers (12)  |  Patents (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1232 KB)  

    N-tuple features for optical character recognition have received only scattered attention since the 1960s. Our main purpose is to show that advances in computer technology and computer science compel renewed interest. N-tuple features are useful for printed character classification because they indicate the presence or absence of a given rigid configuration of n black and white pixels in a pattern. Desirable n-tuples fit each pattern of a specified (positive) training set of characters in at least p different shift positions, and fail to fit each pattern of a specified (negative) training set by at least n-q pixels in each shift position. We prove that the problem of finding a distinguishing n-tuple is NP-complete, by examining a natural subproblem with binary strings called the missing configuration problem. The NP-completeness result notwithstanding, distinguishing n-tuples are found automatically in a few seconds on contemporary workstations. We exhibit a practical search algorithm for generating, from a small training set, a collection of n-tuples with low class-conditional correlation and with specified design parameters n, p, and q. The generator, which is available on the Internet, is empirically shown to be effective through a comparison with a benchmark generator. We show experimentally that the design parameters provide a useful tradeoff between distinguishing power and generation time, and also between the conditional probabilities for the positive and negative classes. We explore the feature probabilities obtainable for various dichotomies, and show that the design parameters control the feature probabilities View full abstract»

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  • Periodicity, directionality, and randomness: Wold features for image modeling and retrieval

    Publication Year: 1996 , Page(s): 722 - 733
    Cited by:  Papers (144)  |  Patents (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2608 KB)  

    One of the fundamental challenges in pattern recognition is choosing a set of features appropriate to a class of problems. In applications such as database retrieval, it is important that image features used in pattern comparison provide good measures of image perceptual similarities. We present an image model with a new set of features that address the challenge of perceptual similarity. The model is based on the 2D Wold decomposition of homogeneous random fields. The three resulting mutually orthogonal subfields have perceptual properties which can be described as “periodicity,” “directionality,” and “randomness,” approximating what are indicated to be the three most important dimensions of human texture perception. The method presented improves upon earlier Wold-based models in its tolerance to a variety of local inhomogeneities which arise in natural textures and its invariance under image transformation such as rotation. An image retrieval algorithm based on the new texture model is presented. Different types of image features are aggregated for similarity comparison by using a Bayesian probabilistic approach. The, effectiveness of the Wold model at retrieving perceptually similar natural textures is demonstrated in comparison to that of two other well-known pattern recognition methods. The Wold model appears to offer a perceptually more satisfying measure of pattern similarity while exceeding the performance of these other methods by traditional pattern recognition criteria. Examples of natural scene Wold texture modeling are also presented View full abstract»

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  • Automatic finding of main roads in aerial images by using geometric-stochastic models and estimation

    Publication Year: 1996 , Page(s): 707 - 721
    Cited by:  Papers (65)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2744 KB)  

    This paper presents an automated approach to finding main roads in aerial images. The approach is to build geometric-probabilistic models for road image generation. We use Gibbs distributions. Then, given an image, roads are found by MAP (maximum a posteriori probability) estimation. The MAP estimation is handled by partitioning an image into windows, realizing the estimation in each window through the use of dynamic programming, and then, starting with the windows containing high confidence estimates, using dynamic programming again to obtain optimal global estimates of the roads present. The approach is model-based from the outset and is completely different than those appearing in the published literature. It produces two boundaries for each road, or four boundaries when a mid-road barrier is present View full abstract»

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  • Covariance matrix estimation and classification with limited training data

    Publication Year: 1996 , Page(s): 763 - 767
    Cited by:  Papers (121)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (484 KB)  

    A new covariance matrix estimator useful for designing classifiers with limited training data is developed. In experiments, this estimator achieved higher classification accuracy than the sample covariance matrix and common covariance matrix estimates. In about half of the experiments, it achieved higher accuracy than regularized discriminant analysis, but required much less computation View full abstract»

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  • Unbiased estimation of ellipses by bootstrapping

    Publication Year: 1996 , Page(s): 752 - 756
    Cited by:  Papers (23)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (544 KB)  

    A general method for eliminating the bias of nonlinear estimators using bootstrap is presented. Instead of the traditional mean bias we consider the definition of bias based on the median. The method is applied to the problem of fitting ellipse segments to noisy data. No assumption beyond being independent identically distributed is made about the error distribution and experiments with both synthetic and real data prove the effectiveness of the technique View full abstract»

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  • Subpixel precision of straight-edged shapes for registration and measurement

    Publication Year: 1996 , Page(s): 746 - 751
    Cited by:  Papers (10)  |  Patents (21)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (500 KB)  

    The precision by which a region is located or measured on the image plane is limited by the sampling density. In this paper, the worst-case precision errors are determined for calculating the average image location of an edge, line, and straight-edged region. For each case, it is shown how the worst-case error can be minimized as a function of the geometric parameters. These results can be used to determine the worst case error by which the location of a known shape is measured. Another application is to design shapes for use in registration, such as fiducial marks used in electronic assembly. The main conclusion of this paper is that, to achieve better precision, measurement of a straight-edged region should be made at an angle askew to the sampling axis (not 0, 45, or 90 degrees) and this should be at a certain length that is a function of this skew angle View full abstract»

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The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is published monthly. Its editorial board strives to present most important research results in areas within TPAMI's scope.

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Editor-in-Chief
David A. Forsyth
University of Illinois