By Topic

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 2 • Date Feb. 2000

Filter Results

Displaying Results 1 - 11 of 11
  • 1999 reviewers list

    Page(s): 227 - 229
    Save to Project icon | Request Permissions | PDF file iconPDF (148 KB)  
    Freely Available from IEEE
  • Fundamental limits of Bayesian inference: order parameters and phase transitions for road tracking

    Page(s): 160 - 173
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (628 KB)  

    There is a growing interest in formulating vision problems in terms of Bayesian inference and, in particular, the maximum a posteriori (MAP) estimator. In this paper, we consider the special case of detecting roads from aerial images and demonstrate that analysis of this ensemble enables us to determine fundamental bounds on the performance of the MAP estimate. We demonstrate that there is a phase transition at a critical value of the order parameter; below this phase transition, it is impossible to detect the road by any algorithm. We derive closely related order parameters which determine the time and memory complexity of search and the accuracy of the solution using the n* search strategy. Our approach can be applied to other vision problems, and we briefly summarize the results when the model uses the “wrong prior”. We comment on how our work relates to studies of the complexity of visual search and the critical behaviour in the computational cost of solving NP-complete problems View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Image field categorization and edge/corner detection from gradient covariance

    Page(s): 179 - 190
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1400 KB)  

    Edges, corners, and vertices in an image correspond to 1D (one-dimensional) and 2D discontinuities in the intensity surface of the underlying scene. Ridges and peaks correspond to 1D and 2D extrema in it. All of them can be characterized by the distribution of gradients, particularly by the dimensionality of it. The approach to image field categorization here is to construct a covariance matrix of the gradient vector in each small window and apply the canonical correlation analysis to it. Schwarz's inequality on the matrix determinant and the related differential equation is the key to this analysis. We obtain two operators PEG and QEG to categorize the image field into a unidirectionally varying region (UNIVAR), an omidirectionally varying region (OMNIVAR), and a nonvarying region. We investigate the conditions under which their absolute maximum response, i.e. PEG=1 and QEG=1, occurs in the small window and show that they are, respectively, the desired 1D and 2D discontinuities/extrema and OMNIVAR, is in many cases, a 1D pattern in polar coordinates. This leads to an algorithm to obtain further classification and accurate localization of them into edges, ridges, peaks, corners, and vertices through detailed analysis in the informative (varying) axis of them. We examined and compared the performance of the operators and the localization algorithm on various types of images and various noise levels. The results indicate that the proposed method is superior with respect to stability, localization, and resolution View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Segmentation of color textures

    Page(s): 142 - 159
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4552 KB)  

    This paper describes an approach to perceptual segmentation of color image textures. A multiscale representation of the texture image, generated by a multiband smoothing algorithm based on human psychophysical measurements of color appearance is used as the input. Initial segmentation is achieved by applying a clustering algorithm to the image at the coarsest level of smoothing. The segmented clusters are then restructured in order to isolate core clusters, i.e., patches in which the pixels are definitely associated with the same region. The image pixels representing the core clusters are used to form 3D color histograms which are then used for probabilistic assignment of all other pixels to the core clusters to form larger clusters and categorise the rest of the image. The process of setting up color histograms and probabilistic reassignment of the pixels to the clusters is then propagated through finer levels of smoothing until a full segmentation is achieved at the highest level of resolution View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The frequency structure of one-dimensional occluding image signals

    Page(s): 200 - 206
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1316 KB)  

    We present a theoretical investigation of the frequency structure of 1D occluding image signals. We show that image signal occlusion contains relevant information which is most easily extractable from its representation in the frequency domain. For instance, the occluding and occluded signal velocities may be identified as such and translucency phenomena may be understood in the terms of this theoretical investigation. In addition, it is found that the structure of occluding 1D signals is invariant under constant and linear models of signal velocity. This theoretical framework can be used to describe the exact frequency structure of non-Fourier motion and bridges the gap between such visual phenomena and their understanding in the frequency domain View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Full text access may be available. Click article title to sign in or learn about subscription options.
  • Stereo-motion with stereo and motion in complement

    Page(s): 215 - 220
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (692 KB)  

    This paper presents a new approach of combining stereo vision and dynamic vision with the objective of retaining their advantages and removing their disadvantages. It is shown that, by assuming affine cameras, the stereo correspondences and motion correspondences, if organized in a particular way in a matrix, can be decomposed into: the 3D structure of the scene, the camera parameters, the motion parameters, and the stereo geometry. With this, the approach can infer stereo correspondences from motion correspondences, requiring only a time linear with respect to the size of the available image data. The approach offers the advantages of simpler correspondence, as in dynamic vision, and accurate reconstruction, as in stereo vision, even with short image sequences View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The support cone: a representational tool for the analysis of boundaries and their interactions

    Page(s): 174 - 178
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (412 KB)  

    We present a directional boundary representation which deals locally and consistently with the boundary's “inside”. We show that collision and wave propagation are reduced to addition on the spectrum of directions, and we derive transformation laws for differential geometrical properties such as directed curvature View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Hybrid genetic optimization and statistical model based approach for the classification of shadow shapes in sonar imagery

    Page(s): 129 - 141
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2688 KB)  

    We present an original statistical classification method using a deformable template model to separate natural objects from man-made objects in an image provided by a high resolution sonar. A prior knowledge of the manufactured object shadow shape is captured by a prototype template, along with a set of admissible linear transformations, to take into account the shape variability. Then, the classification problem is defined as a two-step process: 1) the detection problem of a region of interest in the input image is stated as the minimization of a cost function; and 2) the value of this function at convergence allows one to determine whether the desired object is present or not in the sonar image. The energy minimization problem is tackled using relaxation techniques. In this context, we compare the results obtained with a deterministic relaxation technique and two stochastic relaxation methods: simulated annealing and a hybrid genetic algorithm. This latter method has been successfully tested on real and synthetic sonar images, yielding very promising results View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Implicit polynomials, orthogonal distance regression, and the closest point on a curve

    Page(s): 191 - 199
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (553 KB)  

    Implicit polynomials (i.e., multinomials) have a number of properties that make them attractive for modeling curves and surfaces in computer vision. The paper considers the problem of finding the best fitting implicit polynomial (or algebraic curve) to a collection of points in the plane using an orthogonal distance metric. Approximate methods for orthogonal distance regression have been shown by others to be prone to the problem of cusps in the solution and this is confirmed here. Consequently, this work focuses on exact methods for orthogonal distance regression. The most difficult and costly part of exact methods is computing the closest point on the algebraic curve to an arbitrary point in the plane. The paper considers three methods for achieving this in detail. The first is the standard Newton's method, the second is based on resultants which are making a resurgence in computer graphics, and the third is a novel technique based on successive circular approximations to the curve. It is shown that Newton's method is the quickest, but that it can fail sometimes even with a good initial guess. The successive circular approximation algorithm is not as fast, but is robust. The resultant method is the slowest of the three, but does not require an initial guess. The driving application of this work was the fitting of implicit quartics in two variables to thinned oblique ionogram traces. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Optimal linear combination of neural networks for improving classification performance

    Page(s): 207 - 215
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (324 KB)  

    This paper presents a new method for linearly combining multiple neural network classifiers based on the statistical pattern recognition theory. In our approach, several neural networks are first selected based on which works best for each class in terms of minimizing classification errors. Then, they are linearly combined to form an ideal classifier that exploits the strengths of the individual classifiers. In this approach, the minimum classification error criterion is utilized to estimate the optimal linear weights. In this formulation, because the classification decision rule is incorporated into the cost function, a more suitable better combination of weights for the classification objective could be obtained. Experimental results using artificial and real data sets show that the proposed method can construct a better combined classifier that outperforms the best single classifier in terms of overall classification errors for test data View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

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.

Full Aims & Scope

Meet Our Editors

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