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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 7 • Date July 2009

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  • [Front cover]

    Publication Year: 2009 , Page(s): c1
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  • [Inside front cover]

    Publication Year: 2009 , Page(s): c2
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  • Classification Based on Hybridization of Parametric and Nonparametric Classifiers

    Publication Year: 2009 , Page(s): 1153 - 1164
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3218 KB) |  | HTML iconHTML  

    Parametric methods of classification assume specific parametric models for competing population densities (e.g., Gaussian population densities can lead to linear and quadratic discriminant analysis) and they work well when these model assumptions are valid. Violation in one or more of these parametric model assumptions often leads to a poor classifier. On the other hand, nonparametric classifiers (e.g., nearest-neighbor and kernel-based classifiers) are more flexible and free from parametric model assumptions. But, the statistical instability of these classifiers may lead to poor performance when we have small numbers of training sample observations. Nonparametric methods, however, do not use any parametric structure of population densities. Therefore, even when one has some additional information about population densities, that important information is not used to modify the nonparametric classification rule. This paper makes an attempt to overcome these limitations of parametric and nonparametric approaches and combines their strengths to develop some hybrid classification methods. We use some simulated examples and benchmark data sets to examine the performance of these hybrid discriminant analysis tools. Asymptotic results on their misclassification rates have been derived under appropriate regularity conditions. View full abstract»

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  • Combining Slanted-Frame Classifiers for Improved HMM-Based Arabic Handwriting Recognition

    Publication Year: 2009 , Page(s): 1165 - 1177
    Cited by:  Papers (26)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3549 KB) |  | HTML iconHTML  

    The problem addressed in this study is the offline recognition of handwritten Arabic city names. The names are assumed to belong to a fixed lexicon of about 1,000 entries. A state-of-the-art classical right-left hidden Markov model (HMM)-based recognizer (reference system) using the sliding window approach is developed. The feature set includes both baseline-independent and baseline-dependent features. The analysis of the errors made by the recognizer shows that the inclination, overlap, and shifted positions of diacritical marks are major sources of errors. In this paper, we propose coping with these problems. Our approach relies on the combination of three homogeneous HMM-based classifiers. All classifiers have the same topology as the reference system and differ only in the orientation of the sliding window. We compare three combination schemes of these classifiers at the decision level. Our reported results on the benchmark IFN/ENIT database of Arabic Tunisian city names give a recognition rate higher than 90 percent accuracy and demonstrate the superiority of the neural network-based combination. Our results also show that the combination of classifiers performs better than a single classifier dealing with slant-corrected images and that the approach is robust for a wide range of orientation angles. View full abstract»

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  • A Constant-Time Algorithm for Finding Neighbors in Quadtrees

    Publication Year: 2009 , Page(s): 1178 - 1183
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1472 KB) |  | HTML iconHTML  

    Quadtrees and linear quadtrees are well-known hierarchical data structures to represent square images of size 2r times 2r. Finding the neighbors of a specific leaf node is a fundamental operation for many algorithms that manipulate quadtree data structures. In quadtrees, finding neighbors takes O(r) computational time for the worst case, where r is the resolution (or height) of a given quadtree. Schrack [1] proposed a constant-time algorithm for finding equal-sized neighbors in linear quadtrees. His algorithm calculates the location codes of equal-sized neighbors; it says nothing, however, about their existence. To ensure their existence, additional checking of the location codes is needed, which usually takes O(r) computational time. In this paper, a new algorithm to find the neighbors of a given leaf node in a quadtree is proposed which requires just O(1) (i.e., constant) computational time for the worst case. Moreover, the algorithm takes no notice of the existence or nonexistence of neighbors. Thus, no additional checking is needed. The new algorithm will greatly reduce the computational complexities of almost all algorithms based on quadtrees. View full abstract»

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  • Preprocessing of Low-Quality Handwritten Documents Using Markov Random Fields

    Publication Year: 2009 , Page(s): 1184 - 1194
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3253 KB) |  | HTML iconHTML  

    This paper presents a statistical approach to the preprocessing of degraded handwritten forms including the steps of binarization and form line removal. The degraded image is modeled by a Markov random field (MRF) where the hidden-layer prior probability is learned from a training set of high-quality binarized images and the observation probability density is learned on-the-fly from the gray-level histogram of the input image. We have modified the MRF model to drop the preprinted ruling lines from the image. We use the patch-based topology of the MRF and belief propagation (BP) for efficiency in processing. To further improve the processing speed, we prune unlikely solutions from the search space while solving the MRF. Experimental results show higher accuracy on two data sets of degraded handwritten images than previously used methods. View full abstract»

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  • Context-Aware Visual Tracking

    Publication Year: 2009 , Page(s): 1195 - 1209
    Cited by:  Papers (39)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3124 KB) |  | HTML iconHTML  

    Enormous uncertainties in unconstrained environments lead to a fundamental dilemma that many tracking algorithms have to face in practice: Tracking has to be computationally efficient, but verifying whether or not the tracker is following the true target tends to be demanding, especially when the background is cluttered and/or when occlusion occurs. Due to the lack of a good solution to this problem, many existing methods tend to be either effective but computationally intensive by using sophisticated image observation models or efficient but vulnerable to false alarms. This greatly challenges long-duration robust tracking. This paper presents a novel solution to this dilemma by considering the context of the tracking scene. Specifically, we integrate into the tracking process a set of auxiliary objects that are automatically discovered in the video on the fly by data mining. Auxiliary objects have three properties, at least in a short time interval: 1) persistent co-occurrence with the target, 2) consistent motion correlation to the target, and 3) easy to track. Regarding these auxiliary objects as the context of the target, the collaborative tracking of these auxiliary objects leads to efficient computation as well as strong verification. Our extensive experiments have exhibited exciting performance in very challenging real-world testing cases. View full abstract»

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  • A Fast 2D Shape Recovery Approach by Fusing Features and Appearance

    Publication Year: 2009 , Page(s): 1210 - 1224
    Cited by:  Papers (4)
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3804 KB) |  | HTML iconHTML  

    In this paper, we present a fusion approach to solve the nonrigid shape recovery problem, which takes advantage of both the appearance information and the local features. We have two major contributions. First, we propose a novel progressive finite Newton optimization scheme for the feature-based nonrigid surface detection problem, which is reduced to only solving a set of linear equations. The key is to formulate the nonrigid surface detection as an unconstrained quadratic optimization problem that has a closed-form solution for a given set of observations. Second, we propose a deformable Lucas-Kanade algorithm that triangulates the template image into small patches and constrains the deformation through the second-order derivatives of the mesh vertices. We formulate it into a sparse regularized least squares problem, which is able to reduce the computational cost and the memory requirement. The inverse compositional algorithm is applied to efficiently solve the optimization problem. We have conducted extensive experiments for performance evaluation on various environments, whose promising results show that the proposed algorithm is both efficient and effective. View full abstract»

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  • Minimum Distance between Pattern Transformation Manifolds: Algorithm and Applications

    Publication Year: 2009 , Page(s): 1225 - 1238
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1243 KB) |  | HTML iconHTML  

    Transformation invariance is an important property in pattern recognition, where different observations of the same object typically receive the same label. This paper focuses on a transformation-invariant distance measure that represents the minimum distance between the transformation manifolds spanned by patterns of interest. Since these manifolds are typically nonlinear, the computation of the manifold distance (MD) becomes a nonconvex optimization problem. We propose representing a pattern of interest as a linear combination of a few geometric functions extracted from a structured and redundant basis. Transforming the pattern results in the transformation of its constituent parts. We show that, when the transformation is restricted to a synthesis of translations, rotations, and isotropic scalings, such a pattern representation results in a closed-form expression of the manifold equation with respect to the transformation parameters. The MD computation can then be formulated as a minimization problem whose objective function is expressed as the difference of convex functions (DC). This interesting property permits optimally solving the optimization problem with DC programming solvers that are globally convergent. We present experimental evidence which shows that our method is able to find the globally optimal solution, outperforming existing methods that yield suboptimal solutions. View full abstract»

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  • A Novel Algorithm for Detecting Singular Points from Fingerprint Images

    Publication Year: 2009 , Page(s): 1239 - 1250
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5408 KB) |  | HTML iconHTML  

    Fingerprint analysis is typically based on the location and pattern of detected singular points in the images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. In this paper, we propose a novel algorithm for singular points detection. After an initial detection using the conventional poincare index method, a so-called DORIC feature is used to remove spurious singular points. Then, the optimal combination of singular points is selected to minimize the difference between the original orientation field and the model-based orientation field reconstructed using the singular points. A core-delta relation is used as a global constraint for the final selection of singular points. Experimental results show that our algorithm is accurate and robust, giving better results than competing approaches. The proposed detection algorithm can also be used for more general 2D oriented patterns, such as fluid flow motion, and so forth. View full abstract»

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  • An O(N²) Square Root Unscented Kalman Filter for Visual Simultaneous Localization and Mapping

    Publication Year: 2009 , Page(s): 1251 - 1263
    Cited by:  Papers (9)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1277 KB) |  | HTML iconHTML  

    This paper develops a square root unscented Kalman filter (SRUKF) for performing video-rate visual simultaneous localization and mapping (SLAM) using a single camera. The conventional UKF has been proposed previously for SLAM, improving the handling of nonlinearities compared with the more widely used extended Kalman filter (EKF). However, no account was taken of the comparative complexity of the algorithms: In SLAM, the UKF scales as O(N 3) in the state length, compared to the EKF's O(N 2), making it unsuitable for video-rate applications with other than unrealistically few scene points. Here, it is shown that the SRUKF provides the same results as the UKF to within machine accuracy and that it can be reposed with complexity O(N 2) for state estimation in visual SLAM. This paper presents results from video-rate experiments on live imagery. Trials using synthesized data show that the consistency of the SRUKF is routinely better than that of the EKF, but that its overall cost settles at an order of magnitude greater than the EKF for large scenes. View full abstract»

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  • Sign Language Spotting with a Threshold Model Based on Conditional Random Fields

    Publication Year: 2009 , Page(s): 1264 - 1277
    Cited by:  Papers (29)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3523 KB) |  | HTML iconHTML  

    Sign language spotting is the task of detecting and recognizing signs in a signed utterance, in a set vocabulary. The difficulty of sign language spotting is that instances of signs vary in both motion and appearance. Moreover, signs appear within a continuous gesture stream, interspersed with transitional movements between signs in a vocabulary and nonsign patterns (which include out-of-vocabulary signs, epentheses, and other movements that do not correspond to signs). In this paper, a novel method for designing threshold models in a conditional random field (CRF) model is proposed which performs an adaptive threshold for distinguishing between signs in a vocabulary and nonsign patterns. A short-sign detector, a hand appearance-based sign verification method, and a subsign reasoning method are included to further improve sign language spotting accuracy. Experiments demonstrate that our system can spot signs from continuous data with an 87.0 percent spotting rate and can recognize signs from isolated data with a 93.5 percent recognition rate versus 73.5 percent and 85.4 percent, respectively, for CRFs without a threshold model, short-sign detection, subsign reasoning, and hand appearance-based sign verification. Our system can also achieve a 15.0 percent sign error rate (SER) from continuous data and a 6.4 percent SER from isolated data versus 76.2 percent and 14.5 percent, respectively, for conventional CRFs. View full abstract»

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  • A Stochastic Filtering Technique for Fluid Flow Velocity Fields Tracking

    Publication Year: 2009 , Page(s): 1278 - 1293
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5540 KB) |  | HTML iconHTML  

    In this paper, we present a method for the temporal tracking of fluid flow velocity fields. The technique we propose is formalized within a sequential Bayesian filtering framework. The filtering model combines an Ito diffusion process coming from a stochastic formulation of the vorticity-velocity form of the Navier-Stokes equation and discrete measurements extracted from the image sequence. In order to handle a state space of reasonable dimension, the motion field is represented as a combination of adapted basis functions, derived from a discretization of the vorticity map of the fluid flow velocity field. The resulting nonlinear filtering problem is solved with the particle filter algorithm in continuous time. An adaptive dimensional reduction method is applied to the filtering technique, relying on dynamical systems theory. The efficiency of the tracking method is demonstrated on synthetic and real-world sequences. View full abstract»

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  • Supervised Learning of Quantizer Codebooks by Information Loss Minimization

    Publication Year: 2009 , Page(s): 1294 - 1309
    Cited by:  Papers (59)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3973 KB) |  | HTML iconHTML  

    This paper proposes a technique for jointly quantizing continuous features and the posterior distributions of their class labels based on minimizing empirical information loss such that the quantizer index of a given feature vector approximates a sufficient statistic for its class label. Informally, the quantized representation retains as much information as possible for classifying the feature vector correctly. We derive an alternating minimization procedure for simultaneously learning codebooks in the Euclidean feature space and in the simplex of posterior class distributions. The resulting quantizer can be used to encode unlabeled points outside the training set and to predict their posterior class distributions, and has an elegant interpretation in terms of lossless source coding. The proposed method is validated on synthetic and real data sets and is applied to two diverse problems: learning discriminative visual vocabularies for bag-of-features image classification and image segmentation. View full abstract»

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  • 3D Shape Recovery of Smooth Surfaces: Dropping the Fixed-Viewpoint Assumption

    Publication Year: 2009 , Page(s): 1310 - 1324
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2265 KB) |  | HTML iconHTML  

    We present a new method for recovering the 3D shape of a featureless smooth surface from three or more calibrated images illuminated by different light sources (three of them are independent). This method is unique in its ability to handle images taken from unconstrained perspective viewpoints and unconstrained illumination directions. The correspondence between such images is hard to compute and no other known method can handle this problem locally from a small number of images. Our method combines geometric and photometric information in order to recover dense correspondence between the images and accurately computes the 3D shape. Only a single pass starting at one point and local computation are used. This is in contrast to methods that use the occluding contours recovered from many images to initialize and constrain an optimization process. The output of our method can be used to initialize such processes. In the special case of fixed viewpoint, the proposed method becomes a new perspective photometric stereo algorithm. Nevertheless, the introduction of the multiview setup, self-occlusions, and regions close to the occluding boundaries are better handled, and the method is more robust to noise than photometric stereo. Experimental results are presented for simulated and real images. View full abstract»

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  • A New Distance Measure for Model-Based Sequence Clustering

    Publication Year: 2009 , Page(s): 1325 - 1331
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (980 KB) |  | HTML iconHTML  

    We review the existing alternatives for defining model-based distances for clustering sequences and propose a new one based on the Kullback-Leibler divergence. This distance is shown to be especially useful in combination with spectral clustering. For improved performance in real-world scenarios, a model selection scheme is also proposed. View full abstract»

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  • Generalized Risk Zone: Selecting Observations for Classification

    Publication Year: 2009 , Page(s): 1331 - 1337
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3752 KB) |  | HTML iconHTML  

    In this paper, we extend the risk zone concept by creating the Generalized Risk Zone. The Generalized Risk Zone is a model-independent scheme to select key observations in a sample set. The observations belonging to the Generalized Risk Zone have shown comparable, in some experiments even better, classification performance when compared to the use of the whole sample. The main tool that allows this extension is the Cauchy-Schwartz divergence, used as a measure of dissimilarity between probability densities. To overcome the setback concerning pdf's estimation, we used the ideas provided by the Information Theoretic Learning, allowing the calculation to be performed on the available observations only. We used the proposed methodology with Learning Vector Quantization, feedforward Neural Networks, Support Vector Machines, and Nearest Neighbors. View full abstract»

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  • A Novel Feature Selection Methodology for Automated Inspection Systems

    Publication Year: 2009 , Page(s): 1338 - 1344
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1096 KB) |  | HTML iconHTML  

    This paper proposes a new feature selection methodology. The methodology is based on the stepwise variable selection procedure, but, instead of using the traditional discriminant metrics such as Wilks' Lambda, it uses an estimation of the misclassification error as the figure of merit to evaluate the introduction of new features. The expected misclassification error rate (MER) is obtained by using the densities of a constructed function of random variables, which is the stochastic representation of the conditional distribution of the quadratic discriminant function estimate. The application of the proposed methodology results in significant savings of computational time in the estimation of classification error over the traditional simulation and cross-validation methods. One of the main advantages of the proposed method is that it provides a direct estimation of the expected misclassification error at the time of feature selection, which provides an immediate assessment of the benefits of introducing an additional feature into an inspection/classification algorithm. View full abstract»

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  • TPAMI Information for authors

    Publication Year: 2009 , Page(s): c3
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    Freely Available from IEEE
  • [Back cover]

    Publication Year: 2009 , Page(s): c4
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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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David A. Forsyth
University of Illinois