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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 12 • Date Dec. 1993

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Displaying Results 1 - 11 of 11
  • Comments on "Design of fiducials for accurate registration using machine vision"

    Page(s): 1330 - 1332
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (306 KB)  

    The commenters point out that the subpixel registration accuracy improves by one order of magnitude if images are not binarized as indicated in the above said paper by Bose and Amir (ibid. vol.12, p.1196-1200 (1990), but gray scale information is fully exploited in calculating the centroid position.<> View full abstract»

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  • A prototype filter design approach to pyramid generation

    Page(s): 1233 - 1240
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (696 KB)  

    This paper presents a technique for image pyramid generation, in which the reduction (expansion) factor between layers is any rational number M/L. The image pyramid generation is modeled as an interpolation and filtering followed by a decimation. The model enables frequency domain analysis of the image pyramid, as well as convenient design of the generating kernels. L(M) generating kernels are necessary to produce an image pyramid with reduction (expansion) factor M/L(L/M). A polyphase filter network scheme is used where the L(M) generating kernels can be produced by sampling one prototype low-pass filter with cutoff frequency at ω=π/max[M,L]. Using these polyphase filters, the frequency content of pyramid image decompositions can be adjusted with great flexibility. A systematic procedure is presented here for specifying the relative positions of spatial samples in successive pyramid levels-a complication that arises when generalizing from integer reduction factors to rational factors. Two types of low-pass filters are employed in this work for the prototype filter design: a binomial filter and an FIR linear phase filter. Illustrative examples are presented View full abstract»

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  • Vector quantization technique for nonparametric classifier design

    Page(s): 1326 - 1330
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (504 KB)  

    An effective data reduction technique based on vector quantization is introduced for nonparametric classifier design. Two new nonparametric classifiers are developed, and their performance is evaluated using various examples. The new methods maintain a classification accuracy that is competitive with that of classical methods but, at the same time, yields very high data reduction rates View full abstract»

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  • Color space analysis of mutual illumination

    Page(s): 1319 - 1326
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (804 KB)  

    Mutual illumination occurs when light reflected from one surface impinges on a second one. The resulting additional illumination incident on the second surface affects both the color and intensity of the light reflected from it. As a consequence, the image of a surface in the presence of mutual illumination differs from what it otherwise would have been in the absence of mutual illumination. Unaccounted for mutual illumination can easily confuse methods that rely on intensity or color such as shape-from-shading or color-based object recognition. In this correspondence, we introduce an algorithm that removes mutual illumination effects from images. The domain is that of previously-segmented images of convex surfaces of uniform color and diffuse reflectance where for each surface the interreflection occurs mainly from one other surface and can be accurately accounted for within a one-bounce model. The algorithm is based on a singular value decomposition of the colors coming from each surface. Geometrical information about where on the surface the colors emanate from is not required. The RGB triples from a single convex surface experiencing interreflection fall in a plane; intersecting the planes generated from two interreflecting surfaces results in a unique interreflection color. Each pixel can then be factored into its interreflection and no-interreflection components so that a complete no-interreflection image is produced View full abstract»

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  • A metric for line segments

    Page(s): 1312 - 1318
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (600 KB)  

    This correspondence presents a metric for describing line segments. This metric measures how well two line segments can be replaced by a single longer one. This depends for example on collinearity and nearness of the line segments. The metric is constructed using a new technique using so-called neighborhood functions. The behavior of the metric depends on the neighborhood function chosen. In this correspondence, an appropriate choice for the case of line segments is presented. The quality of the metric is verified by using it in a simple clustering algorithm that groups line segments found by an edge detection algorithm in an image. The fact that the clustering algorithm can detect long linear structures in an image shows that the metric is a good measure for the groupability of line segments View full abstract»

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  • Map learning and clustering in autonomous systems

    Page(s): 1286 - 1297
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    Building autonomous systems, self-learning while moving in an unknown environment, finds a variety of challenging applications. This paper presents a new approach, called clustering by discovery, for identification of clusters in a map which is being learned by exploration. The concomitance of exploration and clustering, we argue, is a mandatory feature for an autonomous system, hence the clustering technique we propose is an incremental process performed while the system is learning the map. Clusters supply an abstract description of the environment and can be used to decrease the complexity of the navigational tasks. The environment is viewed as a map of distinctive places which we assume to be sensed and recognized by the system. The presence of distinctive places and the environment scale are the only facts which we assume known apriori to the system. Clustering by discovery is based on a heuristic indicator called scattering, whose increment is minimized at each exploration step compatibly with a connectivity constraint imposed on clusters. Scattering is defined according to a number of functional and structural requirements. Two variants are presented, and their performance is discussed on a sample of maps including a real urban map and some randomly generated ones. In particular, one of the variants shows robust behaviour in terms of independence of the exploration strategy adopted View full abstract»

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  • Stability of phase information

    Page(s): 1253 - 1268
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    This paper concerns the robustness of local phase information for measuring image velocity and binocular disparity. It addresses the dependence of phase behavior on the initial filters as well as the image variations that exist between different views of a 3D scene. We are particularly interested in the stability of phase with respect to geometric deformations, and its linearity as a function of spatial position. These properties are important to the use of phase information, and are shown to depend on the form of the filters as well as their frequency bandwidths. Phase instabilities are also discussed using the model of phase singularities described by Jepson and Fleet. In addition to phase-based methods, these results are directly relevant to differential optical flow methods and zero-crossing tracking View full abstract»

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  • An algebraic approach to automatic construction of structural models

    Page(s): 1298 - 1311
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    We present algebraic approach to the inductive learning of structural models and automatic construction of shape prototypes for character recognition on the basis of the algebraic description of curve structure proposed by Nishida and Mori (1991, 1992). A class in the structural models is a set of shapes that can be transformed continuously to each other. We consider an algebraic representation of continuous transformation of components of the shape, and give specific properties satisfied by each component in the class. The generalization rules in the inductive learning are specified from the viewpoints of continuous transformation of components and relational structure among the components. The learning procedure generalizes a pair of classes into one class incrementally and hierarchically in terms of the generalization rules. We show experimental results on handwritten numerals View full abstract»

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  • The integration of image segmentation maps using region and edge information

    Page(s): 1241 - 1252
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1208 KB)  

    We present an algorithm that integrates multiple region segmentation maps and edge maps. It operates independently of image sources and specific region-segmentation or edge-detection techniques. User-specified weights and the arbitrary mixing of region/edge maps are allowed. The integration algorithm enables multiple edge detection/region segmentation modules to work in parallel as front ends. The solution procedure consists of three steps. A maximum likelihood estimator provides initial solutions to the positions of edge pixels from various inputs. An iterative procedure using only local information (without edge tracing) then minimizes the contour curvature. Finally, regions are merged to guarantee that each region is large and compact. The channel-resolution width controls the spatial scope of the initial estimation and contour smoothing to facilitate multiscale processing. Experimental results are demonstrated using data from different types of sensors and processing techniques. The results show an improvement over individual inputs and a strong resemblance to human-generated segmentation View full abstract»

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  • Spatial reasoning for the automatic recognition of machinable features in solid models

    Page(s): 1269 - 1285
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    Discusses an automatic feature recognizer that decomposes the total volume to be machined into volumetric features that satisfy stringent conditions for manufacturability, and correspond to operations typically performed in 3-axis machining centers. Unlike most of the previous research, the approach is based on general techniques for dealing with features with intersecting volumes. Feature interactions are represented explicitly in the recognizer's output, to facilitate spatial reasoning in subsequent planning stages. A generate-and-test strategy is used. OPS-5 production rules generate hints or clues for the existence of features, and post them on a blackboard. The clues are assessed, and those judged promising are processed to ensure that they correspond to actual features, and to gather information for process planning. Computational geometry techniques are used to produce the largest volumetric feature compatible with the available data. The feature's accessibility, and its interactions with others are analyzed. The validity tests ensure that the proposed features are accessible, do not intrude into the desired part, and satisfy other machinability conditions. The process continues until it produces a complete decomposition of the volume to be machined into fully-specified features View full abstract»

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  • Multimodal estimation of discontinuous optical flow using Markov random fields

    Page(s): 1217 - 1232
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1544 KB)  

    The estimation of dense velocity fields from image sequences is basically an ill-posed problem, primarily because the data only partially constrain the solution. It is rendered especially difficult by the presence of motion boundaries and occlusion regions which are not taken into account by standard regularization approaches. In this paper, the authors present a multimodal approach to the problem of motion estimation in which the computation of visual motion is based on several complementary constraints. It is shown that multiple constraints can provide more accurate flow estimation in a wide range of circumstances. The theoretical framework relies on Bayesian estimation associated with global statistical models, namely, Markov random fields. The constraints introduced here aim to address the following issues: optical flow estimation while preserving motion boundaries, processing of occlusion regions, fusion between gradient and feature-based motion constraint equations. Deterministic relaxation algorithms are used to merge information and to provide a solution to the maximum a posteriori estimation of the unknown dense motion field. The algorithm is well suited to a multiresolution implementation which brings an appreciable speed-up as well as a significant improvement of estimation when large displacements are present in the scene. Experiments on synthetic and real world image sequences are reported 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