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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 4 • Date April 1996

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Displaying Results 1 - 15 of 15
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  • Evaluation of ridge seeking operators for multimodality medical image matching

    Page(s): 353 - 365
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    Ridge-like structures in digital images may be extracted by convolving the images with derivatives of Gaussians. The choice of the convolution operator and of the parameters involved defines a specific ridge image. In this paper, various ridge measures related to isophote curvature are constructed, reviewed, and evaluated with respect to their usability in CT/MRI matching of human brain scans. Construction is initially done using heuristics in two-dimensional images, and then established firmly in a mathematical framework. Attention is paid to the necessity of operator invariance, scale of the operator, extension to three-dimensional images, and relations to isophote and principal curvature. It is shown that one of the ridge measures appears well suited for the purpose of matching, despite the fact that the measure fails to detect ridges in a number of stylized scenes View full abstract»

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  • Blended deformable models

    Page(s): 443 - 448
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    This paper develops a new class of parameterized models based on the linear interpolation of two parameterized shapes along their main axes, using a blending function. This blending function specifies the relative contribution of each component shape on the resulting blended shape. The resulting blended shape can have aspects of each of the component shapes. Using a small number of additional parameters, blending extends the coverage of shape primitives while also providing abstraction of shape. In particular, it offers the ability to construct shapes whose genus can change. Blended models are incorporated into a physics-based shape estimation framework which uses dynamic deformable models. Finally, we present experiments involving the extraction of complex shapes from range data including examples of dynamic genus change View full abstract»

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  • Compact integrated motion sensor with three-pixel interaction

    Page(s): 455 - 460
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    An integrated circuit with on-chip photoreceptors is described, that computes the bi-directional velocity of a visual stimulus moving along a given axis in the focal plane by measuring the time delay of its detection at two positions. Due to the compactness of the circuit, a dense array of such motion-sensing elements can be monolithically integrated to estimate the velocity field of an image and to extract higher-level image features through local or global interaction View full abstract»

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  • Automatic construction of structural models incorporating discontinuous transformations

    Page(s): 400 - 411
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1176 KB)  

    Presents an approach to automatic construction of structural models incorporating discontinuous transformations, with emphasis on application to unconstrained handwritten character recognition. The author considers this problem as constructing inductively, from the data set, some shape descriptions that tolerate certain types of shape transformations. The approach is based on the exploration of complete, systematic, high-level models on the effects of the transformations, and the generalization process is controlled and supported by the high-level transformation models. An analysis of the a priori effects of commonly occurring discontinuous transformations is carried out completely and systematically, leading to a small, tractable number of distinct cases. Based on this analysis, an algorithm for the inference of super-classes under these transformations is designed. Furthermore, through examples and experiments, the author shows that the proposed algorithm can generalize unconstrained handwritten characters into a small number of classes, and that one class can represent various deformed patterns View full abstract»

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  • The method of normalization to determine invariants

    Page(s): 366 - 376
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    The determination of invariant characteristics is an important problem in pattern recognition. Many invariants are known, which have been obtained either by normalization or by other methods. This paper shows that the method of normalization is much more general and allows one to derive a lot of sets of invariants from the second list as well. To this end, the normalization method is generalized and is presented in such a way that it is easy to apply, thus unifying and simplifying the determination of invariants. Furthermore, this paper discusses the advantages and disadvantages of the invariants obtained by normalization. Their main advantage is that the normalization process provides us with a standard position of the object. Because of the generality of the method, also new invariants are obtained such as normalized moments more stable than known ones, Legendre descriptors and Zernike descriptors to affine transformations, two-dimensional Fourier descriptors and affine moment invariants obtained by combining Hu's moment invariants and normalized moments View full abstract»

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  • Extracting the shape and roughness of specular lobe objects using four light photometric stereo

    Page(s): 449 - 454
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    We propose a noncontact method for the measurement of surface shape and surface roughness. The method, which we call “four light photometric stereo”, uses four lights, which sequentially illuminate the object under inspection, and a video camera for taking images of the object. The method has successfully been applied to a number of real objects View full abstract»

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  • Global word shape processing in off-line recognition of handwriting

    Page(s): 460 - 464
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    Off-line recognition of handwriting may be achieved using simplified profiles of word shapes. These profiles consist of approximations of the word's upper and lower contour. Training and recognition are based on n-gram extraction and identification. The lexicons used extend to 16,000 words View full abstract»

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  • Orthogonal moment features for use with parametric and non-parametric classifiers

    Page(s): 389 - 399
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    This research examines a variety of approaches for using two-dimensional orthogonal polynomials for the recognition of handwritten Arabic numerals. It also makes use of parametric and non-parametric statistical and neural network classifiers. Polynomials, including Legendre, Zernike, and pseudo-Zernike, are used to generate moment-based features which are invariant to location, size, and (optionally) rotation. An efficient method for computing the moments via geometric moments is presented. A side effect of this method also yields scale invariance. A new approach to location invariance using a minimum bounding circle is presented, and a detailed analysis of the rotational properties of the moments is given. Data partitioning tests are performed to evaluate the various feature types and classifiers. For rotational invariant character recognition, the highest percentage of correctly classified characters was 91.7%, and for non-rotational invariant recognition it was 97.6%. This compares with a previous effort, using the same data and test conditions, of 94.8%. The techniques developed here should also be applicable to other areas of shape recognition View full abstract»

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  • Image motion estimation from motion smear-a new computational model

    Page(s): 412 - 425
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    Motion smear is an important visual cue for motion perception by the human vision system (HVS). However, in image analysis research, exploiting motion smear has been largely ignored. Rather, motion smear is usually considered as a degradation of images that needs to be removed. In this paper, the authors establish a computational model that estimates image motion from motion smear information-“motion from smear”. In many real situations, the shutter of the sensing camera must be kept open long enough to produce images of adequate signal-to-noise ratio (SNR), resulting in significant motion smear in images. The authors present a new motion blur model and an algorithm that enables unique estimation of image motion. A prototype sensor system that exploits the new motion blur model has been built to acquire data for “motion-from-smear”. Experimental results on images with both simulated smear and real smear, using the authors' “motion-from-smear” algorithm as well as a conventional motion estimation technique, are provided. The authors also show that temporal aliasing does not affect “motion-from-smear” to the same degree as it does algorithms that use displacement as a cue. “Motion-from-smear” provides an additional tool for motion estimation and effectively complements the existing techniques when apparent motion smear is present View full abstract»

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  • A graduated assignment algorithm for graph matching

    Page(s): 377 - 388
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    A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise. By combining graduated nonconvexity, two-way (assignment) constraints, and sparsity, large improvements in accuracy and speed are achieved. Its low order computational complexity [O(lm), where l and m are the number of links in the two graphs] and robustness in the presence of noise offer advantages over traditional combinatorial approaches. The algorithm, not restricted to any special class of graph, is applied to subgraph isomorphism, weighted graph matching, and attributed relational graph matching. To illustrate the performance of the algorithm, attributed relational graphs derived from objects are matched. Then, results from twenty-five thousand experiments conducted on 100 mode random graphs of varying types (graphs with only zero-one links, weighted graphs, and graphs with node attributes and multiple link types) are reported. No comparable results have been reported by any other graph matching algorithm before in the research literature. Twenty-five hundred control experiments are conducted using a relaxation labeling algorithm and large improvements in accuracy are demonstrated View full abstract»

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  • New prospects in line detection by dynamic programming

    Page(s): 426 - 431
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    The detection of lines in satellite images has drawn a lot of attention within the last 15 years. Problems of resolution, noise, and image understanding are involved, and one of the best methods developed so far is the F* algorithm of Fischler, which achieves robustness, rightness, and rapidity. Like other methods of dynamic programming, it consists of defining a cost which depends on local information; then a summation-minimization process in the image is performed. The authors present herein a mathematical formalization of the F* algorithm, which allows them to extend the cost both to cliques of more than two points (to deal with the contrast), and to neighborhoods of size larger than one (to take into account the curvature). Thus, all the needed information (contrast, grey-level, curvature) is synthesized in a unique cost function defined on the digital original image. This cost is used to detect roads and valleys in satellite images (SPOT) View full abstract»

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  • Distance metric between 3D models and 2D images for recognition and classification

    Page(s): 465 - 479
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    Similarity measurements between 3D objects and 2D images are useful for the tasks of object recognition and classification. The authors distinguish between two types of similarity metrics: metrics computed in image-space (image metrics) and metrics computed in transformation-space (transformation metrics). Existing methods typically use image metrics; namely, metrics that measure the difference in the image between the observed image and the nearest view of the object. Example for such a measure is the Euclidean distance between feature points in the image and their corresponding points in the nearest view. (This measure can be computed by solving the exterior orientation calibration problem.) In this paper the authors introduce a different type of metrics: transformation metrics. These metrics penalize for the deformations applied to the object to produce the observed image. In particular, the authors define a transformation metric that optimally penalizes for “affine deformations” under weak-perspective. A closed-form solution, together with the nearest view according to this metric, are derived. The metric is shown to be equivalent to the Euclidean image metric, in the sense that they bound each other from both above and below. It therefore provides an easy-to-use closed-form approximation for the commonly-used least-squares distance between models and images. The authors demonstrate an image understanding application, where the true dimensions of a photographed battery charger are estimated by minimizing the transformation metric View full abstract»

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  • A deformable template approach to detecting straight edges in radar images

    Page(s): 438 - 443
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    This paper addresses the problem of locating two straight and parallel road edges in images that are acquired from a stationary millimeter-wave radar platform positioned near ground-level. A fast, robust, and completely data-driven Bayesian solution to this problem is developed, and it has applications in automotive vision enhancement. The method employed in this paper makes use of a deformable template model of the expected road edges, a two-parameter log-normal model of the ground-level millimeter-wave (GLEM) radar imaging process, a maximum a posteriori (MAP) formulation of the straight edge detection problem, and a Monte Carlo algorithm to maximize the posterior density. Experimental results are presented by applying the method on GLEM radar images of actual roads. The performance of the method is assessed against ground truth for a variety of road scenes View full abstract»

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  • A new probabilistic relaxation scheme and its application to edge detection

    Page(s): 432 - 437
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    This paper presents a new scheme for probabilistic relaxation labeling that consists of an update function and a dictionary construction method. The nonlinear update function is derived from Markov random field theory and Bayes' formula. The method combines evidence from neighboring label assignments and eliminates label ambiguity efficiently. This result is important for a variety of image processing tasks, such as image restoration, edge enhancement, edge detection, pixel classification, and image segmentation. The authors successfully applied this method to edge detection. The relaxation step of the proposed edge-detection algorithm greatly reduces noise effects, gets better edge localization such as line ends and corners, and plays a crucial role in refining edge outputs. The experiments show that our algorithm converges quickly and is robust in noisy environments 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