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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 6 • Date Jun 1997

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Displaying Results 1 - 11 of 11
  • Techniques for assessing polygonal approximations of curves

    Publication Year: 1997 , Page(s): 659 - 666
    Cited by:  Papers (80)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (188 KB)  

    Given the enormous number of available methods for finding polygonal approximations to curves techniques are required to assess different algorithms. Some of the standard approaches are shown to be unsuitable if the approximations contain varying numbers of lines. Instead, we suggest assessing an algorithm's results relative to an optimal polygon, and describe a measure which combines the relative fidelity and efficiency of a curve segmentation. We use this measure to compare the application of 23 algorithms to a curve first used by Teh and Chin (1989); their integral square errors (ISEs) are assessed relative to the optimal ISE. In addition, using an example of pose estimation, it is shown how goal-directed evaluation can be used to select an appropriate assessment criterion View full abstract»

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  • Physically based adaptive preconditioning for early vision

    Publication Year: 1997 , Page(s): 594 - 607
    Cited by:  Papers (8)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1340 KB)  

    Several problems in early vision have been formulated in the past in a regularization framework. These problems, when discretized, lead to large sparse linear systems. In this paper, we present a novel physically based adaptive preconditioning technique which can be used in conjunction with a conjugate gradient algorithm to dramatically improve the speed of convergence for solving the aforementioned linear systems. A preconditioner, based on the membrane spline, or the thin plate spline, or a convex combination of the two, is termed a physically based preconditioner for obvious reasons. The adaptation of the preconditioner to an early vision problem is achieved via the explicit use of the spectral characteristics of the regularization filter in conjunction with the data. This spectral function is used to modulate the frequency characteristics of a chosen wavelet basis, and these modulated values are then used in the construction of our preconditioner. We present the preconditioner construction for three different early vision problems namely, the surface reconstruction, the shape from shading, and the optical flow computation problems. Performance of the preconditioning scheme is demonstrated via experiments on synthetic and real data sets View full abstract»

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  • Ligature modeling for online cursive script recognition

    Publication Year: 1997 , Page(s): 623 - 633
    Cited by:  Papers (12)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (560 KB)  

    Online recognition of cursive words is a difficult task owing to variable shape and ambiguous letter boundaries. The approach proposed is based on hidden Markov modeling of letters and inter-letter patterns called ligatures occurring in cursive script. For each of the letters and the ligatures we create one HMM that models temporal and spatial variability of handwriting. By networking the two kinds of HMMs, we can design a network model for all words or composite characters. The network incorporates the knowledge sources of grammatical and structural constraints so that it can better capture the characteristics of handwriting. Given the network, the problem of recognition is formulated into that of finding the most likely path from the start node to the end node. A dynamic programming-based search for the optimal input-network alignment performs character recognition and letter segmentation simultaneously and efficiently. Experiments on Korean character showed correct recognition of up to 93.3% on unconstrained samples. It has also been compared with several other schemes of HMM-based recognition to characterize the proposed approach View full abstract»

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  • Structural matching by discrete relaxation

    Publication Year: 1997 , Page(s): 634 - 648
    Cited by:  Papers (87)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1344 KB)  

    This paper describes a Bayesian framework for performing relational graph matching by discrete relaxation. Our basic aim is to draw on this framework to provide a comparative evaluation of a number of contrasting approaches to relational matching. Broadly speaking there are two main aspects to this study. Firstly we focus on the issue of how relational inexactness may be quantified. We illustrate that several popular relational distance measures can be recovered as specific limiting cases of the Bayesian consistency measure. The second aspect of our comparison concerns the way in which structural inexactness is controlled. We investigate three different realizations of the matching process which draw on contrasting control models. The main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter View full abstract»

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  • On dimensionality, sample size, and classification error of nonparametric linear classification algorithms

    Publication Year: 1997 , Page(s): 667 - 671
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (76 KB)  

    This paper compares two nonparametric linear classification algorithms $the zero empirical error classifier and the maximum margin classifier - with parametric linear classifiers designed to classify multivariate Gaussian populations. Formulae and a table for the mean expected probability of misclassification MEPN are presented. They show that the classification error is mainly determined by N/p, a learning-set size/dimensionality ratio. However, the influences of learning-set size on the generalization error of parametric and nonparametric linear classifiers are quite different. Under certain conditions the nonparametric approach allows us to obtain reliable rules, even in cases where the number of features is larger than the number of training vectors View full abstract»

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  • Decomposition of gray-scale morphological templates using the rank method

    Publication Year: 1997 , Page(s): 649 - 658
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (260 KB)  

    Convolutions are a fundamental tool in image processing. Nonlinear convolutions are used in such operations as the median filter, the medial axis transform, and erosion and dilation as defined in mathematical morphology. For large convolution masks or structuring elements, the computation cost resulting from implementation can be prohibitive. However, in many instances, this cost can be significantly reduced by decomposing the templates representing the masks or structuring elements into a sequence of smaller templates. In addition, such decomposition can often be made architecture specific and, thus, resulting in optimal transform performance. In this paper we provide methods for decomposing morphological templates which are analogous to decomposition methods used in the linear domain. Specifically, we define the notion of the rank of a morphological template which categorizes separable morphological templates as templates of rank one. We establish a necessary and sufficient condition for the decomposability of rank one templates into 3×3 templates. We then use the invariance of the template rank under certain transformations in order to develop template decomposition techniques for templates of rank two View full abstract»

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  • λτ-space representation of images and generalized edge detector

    Publication Year: 1997 , Page(s): 545 - 563
    Cited by:  Papers (25)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3792 KB)  

    An image and surface representation based on regularization theory is introduced in this paper. This representation is based on a hybrid model derived from the physical membrane and plate models. The representation, called the λτ-representation, has two dimensions; one dimension represents smoothness or scale while the other represents the continuity of the image or surface. It contains images/surfaces sampled both in scale space and the weighted Sobolev space of continuous functions. Thus, this new representation can be viewed as an extension of the well-known scale space representation. We have experimentally shown that the proposed hybrid model results in improved results compared to the two extreme constituent models, i.e., the membrane and the plate models. Based on this hybrid model, a generalized edge detector (GED) which encompasses most of the well-known edge detectors under a common framework is developed. The existing edge detectors can be obtained from the generalized edge detector by simply specifying the values of two parameters, one of which controls the shape of the filter (τ) and the other controls the scale of the filter (λ). By sweeping the values of these two parameters continuously, one can generate an edge representation in the λτ space, which is very useful for developing a goal-directed edge detection scheme for a specific task. The proposed representation and the edge detector have been evaluated qualitatively and quantitatively on several different types of image data such as intensity, range, and stereo images View full abstract»

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  • How easy is matching 2D line models using local search?

    Publication Year: 1997 , Page(s): 564 - 579
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (600 KB)  

    Local search is a well established and highly effective method for solving complex combinatorial optimization problems. Here, local search is adapted to solve difficult geometric matching problems. Matching is posed as the problem of finding the optimal many-to-many correspondence mapping between a line segment model and image line segments. Image data is assumed to be fragmented, noisy, and cluttered. The algorithms presented have been used for robot navigation, photo interpretation, and scene understanding. This paper explores how local search performs as model complexity increases, image clutter increases, and additional model instances are added to the image data. Expected run-times to find optimal matches with 95 percent confidence are determined for 48 distinct problems involving six models. Nonlinear regression is used to estimate run-time growth as a function of problem size. Both polynomial and exponential growth models are fit to the run-time data. For problems with random clutter, the polynomial model fits better and growth is comparable to that for tree search. For problems involving symmetric models and multiple model instances, where tree search is exponential, the polynomial growth model is superior to the exponential growth model for one search algorithm and comparable for another View full abstract»

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  • The K1-map reduction for pattern classifications

    Publication Year: 1997 , Page(s): 616 - 622
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (328 KB)  

    A shortcut hand-reduction method known as the Karnaugh map (K map) is an efficient way of reducing Boolean functions to a minimum form for the purpose of minimizing hardware requirements. In this paper, by applying the prime group and the essential prime group concepts of the K maps to pattern classification problems, the K1-map reduction method is proposed. The K1-map reduction method can be used to design restricted Coulomb energy networks and to determine the number of hidden units problems in a systematic manner View full abstract»

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  • The optimum class-selective rejection rule

    Publication Year: 1997 , Page(s): 608 - 615
    Cited by:  Papers (28)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (300 KB)  

    Class-selective rejection is an extension of simple rejection. That is, when an input pattern cannot be reliably assigned to one of the N classes in an N-class problem, it is assigned to a subset of classes that are most likely to issue the pattern, instead of simply being rejected. By selecting more classes, the risk of making an error can be reduced, at the price of subsequently having a larger remaining number of classes. The optimality of class-selective rejection is therefore defined as the best trade-off between error rate and average number of selected classes. Formally, the trade-off study is embedded in the framework of decision theory. The average expected loss is expressed as a linear combination of error rate and average number of classes. The minimization of the average expected loss, therefore, provides the best trade-off. The complexity of the resulting optimum rule is reduced, via a discrete convex minimization, to be linear in the number of classes. Upper-bounds on error rate and average number of classes are derived. An example is provided to illustrate various aspects of the optimum decision rule. Finally, the implications of the new decision rule are discussed View full abstract»

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  • In defense of the eight-point algorithm

    Publication Year: 1997 , Page(s): 580 - 593
    Cited by:  Papers (395)  |  Patents (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (720 KB)  

    The fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras, and the eight-point algorithm is a frequently cited method for computing the fundamental matrix from a set of eight or more point matches. It has the advantage of simplicity of implementation. The prevailing view is, however, that it is extremely susceptible to noise and hence virtually useless for most purposes. This paper challenges that view, by showing that by preceding the algorithm with a very simple normalization (translation and scaling) of the coordinates of the matched points, results are obtained comparable with the best iterative algorithms. This improved performance is justified by theory and verified by extensive experiments on real images View full abstract»

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Aims & Scope

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