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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 2 • Date Feb 1998

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Displaying Results 1 - 12 of 12
  • Decomposition of arbitrarily shaped binary morphological structuring elements using genetic algorithms

    Publication Year: 1998 , Page(s): 217 - 224
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (240 KB)  

    A number of different algorithms have been described in the literature for the decomposition of both convex binary morphological structuring elements and a specific subset of nonconvex ones. Nevertheless, up to now no deterministic solutions have been found to the problem of decomposing arbitrarily shaped structuring elements. This work presents a new stochastic approach based on genetic algorithms, in which no constraints are imposed on the shape of the initial structuring element nor assumptions are made on the elementary factors, which are selected within a given set View full abstract»

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  • Linear programming fitting of implicit polynomials

    Publication Year: 1998 , Page(s): 212 - 217
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (264 KB)  

    A new implicit polynomial (IP) fitting method is presented. It provides a different way of viewing the IP fitting problem from those of the nonlinear optimization approaches. It requires less computation, and can be done automatically or interactively. Linear programming is used to do the fitting. The approach can incorporate a variety of distance measures and global geometric constraints View full abstract»

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  • Characterization of neuropathological shape deformations

    Publication Year: 1998 , Page(s): 97 - 112
    Cited by:  Papers (24)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1500 KB)  

    We present a framework for analyzing the shape deformation of structures within the human brain. A mathematical model is developed describing the deformation of any brain structure whose shape is affected by both gross and detailed physical processes. Using our technique, the total shape deformation is decomposed into analytic modes of variation obtained from finite element modeling, and statistical modes of variation obtained from sample data. Our method is general, and can be applied to many problems where the goal is to separate out important from unimportant shape variation across a class of objects. In this paper, we focus on the analysis of diseases that affect the shape of brain structures. Because the shape of these structures is affected not only by pathology but also by overall brain shape, disease discrimination is difficult. By modeling the brain's elastic properties, we are able to compensate for some of the nonpathological modes of shape variation. This allows us to experimentally characterize modes of variation that are indicative of disease processes. We apply our technique to magnetic resonance images of the brains of individuals with schizophrenia, Alzheimer's disease, and normal-pressure hydrocephalus, as well as to healthy volunteers. Classification results are presented View full abstract»

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  • Extracting 3D vortices in turbulent fluid flow

    Publication Year: 1998 , Page(s): 193 - 199
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (656 KB)  

    This paper presents a computational framework to extract salient patterns, called vortex structures, from 3D turbulent fluid flows. These structures can be characterized as regions of dominating rotational motion in the velocity fields and intensity concentrations in the corresponding vorticity fields. A pointwise linear representation is employed to approximate the kinematics of the flow field, and the fluid motion is classified according to motion analysis or topological patterns. The regions of vortex structures are identified as those dominated by rotational motion or those of focus-type singularity. The 2D vortices, as a special case of 3D vortices, are detected by searching for regions of vorticity concentrations View full abstract»

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  • Robust adaptive segmentation of range images

    Publication Year: 1998 , Page(s): 200 - 205
    Cited by:  Papers (46)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (356 KB)  

    We propose a novel image segmentation technique using the robust, adaptive least kth order squares (ALKS) estimator which minimizes the kth order statistics of the squares of residuals. The optimal value of k is determined from the data, and the procedure detects the homogeneous surface patch representing the relative majority of the pixels. The ALKS shows a better tolerance to structured outliers than other recently proposed similar techniques. The performance of the new, fully autonomous, range image segmentation algorithm is compared to several other methods View full abstract»

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  • An unbiased detector of curvilinear structures

    Publication Year: 1998 , Page(s): 113 - 125
    Cited by:  Papers (198)  |  Patents (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2032 KB)  

    The extraction of curvilinear structures is an important low-level operation in computer vision that has many applications. Most existing operators use a simple model for the line that is to be extracted, i.e., they do not take into account the surroundings of a line. This leads to the undesired consequence that the line will be extracted in the wrong position whenever a line with different lateral contrast is extracted. In contrast, the algorithm proposed in this paper uses an explicit model for lines and their surroundings. By analyzing the scale-space behavior of a model line profile, it is shown how the bias that is induced by asymmetrical lines can be removed. Furthermore, the algorithm not only returns the precise subpixel line position, but also the width of the line for each line point, also with subpixel accuracy View full abstract»

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  • Grouping-based nonadditive verification

    Publication Year: 1998 , Page(s): 186 - 192
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1156 KB)  

    Verification is the final decision stage in many object recognition processes. It is carried out by evaluating a score for every hypothesis and choosing the hypotheses associated with the highest score. This paper suggests a grouping-based verification paradigm, relying on the observation that a group of data features belonging to a hypothesized object instance should be a “good group”. Therefore, it should support perceptual grouping information available from the image by grouping relations. The proposed score, which is the joint likelihood of these grouping cues, quantifies this observation in a probabilistic framework. Experiments with synthetic and real images show that the proposed method performs better in difficult cases View full abstract»

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  • Task-oriented generation of visual sensing strategies in assembly tasks

    Publication Year: 1998 , Page(s): 126 - 138
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1988 KB)  

    This paper describes a method of systematically generating visual sensing strategies based on knowledge of the assembly task to be performed. Since visual sensing is usually performed with limited resources, visual sensing strategies should be planned so that only necessary information is obtained efficiently. The generation of the appropriate visual sensing strategy entails knowing what information to extract, where to get it, and how to get it. This is facilitated by the knowledge of the task, which describes what objects are involved in the operation, and how they are assembled. In the proposed method, using the task analysis based on face contact relations between objects, necessary information for the current operation is first extracted. Then, visual features to be observed are determined using the knowledge of the sensor, which describes the relationship between a visual feature and information to be obtained. Finally, feasible visual sensing strategies are evaluated based on the predicted success probability, and the best strategy is selected. Our method has been implemented using a laser range finder as the sensor. Experimental results show the feasibility of the method, and point out the importance of task-oriented evaluation of visual sensing strategies View full abstract»

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  • A generic grouping algorithm and its quantitative analysis

    Publication Year: 1998 , Page(s): 168 - 185
    Cited by:  Papers (40)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1716 KB)  

    This paper presents a generic method for perceptual grouping and an analysis of its expected grouping quality. The grouping method is fairly general: It may be used for the grouping of various types of data features, and to incorporate different grouping cues operating over feature sets of different sizes. The proposed method is divided into two parts: constructing a graph representation of the available perceptual grouping evidence, and then finding the “best” partition of the graph into groups. The first stage includes a cue enhancement procedure, which integrates the information available from multifeature cues into very reliable bifeature cues. Both stages are implemented using known statistical tools such as Wald's SPRT algorithm (1952) and the maximum likelihood criterion. The accompanying theoretical analysis of this grouping criterion quantifies intuitive expectations and predicts that the expected grouping quality increases with cue reliability. It also shows that investing more computational effort in the grouping algorithm leads to better grouping results. This analysis, which quantifies the grouping power of the maximum likelihood criterion, is independent of the grouping domain. To our best knowledge, such an analysis of a grouping process is given here for the first time. Three grouping algorithms, in three different domains, are synthesized as instances of the generic method. They demonstrate the applicability and generality of this grouping method View full abstract»

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  • Analysis of photometric properties of occluding edges by the reversed projection blurring model

    Publication Year: 1998 , Page(s): 155 - 167
    Cited by:  Papers (23)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (516 KB)  

    This paper analyzes photometric properties of occluding edges and proves that an object surface behind a nearer object is partially observable beyond the occluding edges. We first discuss a limitation of the image blurring model using the convolution, and then present an optical flux based blurring model named the reversed projection blurring (RPB) model. Unlike the multicomponent blurring model proposed by Nguyen et al., the RPB model enables us to explore the optical phenomena caused by a shift-variant point spread function that appears at a depth discontinuity. Using the RPB model, theoretical analysis of occluding edge properties are given and two characteristic phenomena are shown: (1) a blurred occluding edge produces the same brightness profiles as would be predicted for a surface edge on the occluding object when the occluded surface radiance is uniform and (2) a nonmonotonic brightness transition would be observed in blurred occluding edge profiles when the occluded object has a surface edge. Experimental results using real images have demonstrated the validity of the RPB model as well as the observability of the characteristic phenomena of blurred occluding edges View full abstract»

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  • Closed-loop object recognition using reinforcement learning

    Publication Year: 1998 , Page(s): 139 - 154
    Cited by:  Papers (39)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3380 KB)  

    Current computer vision systems whose basic methodology is open-loop or filter type typically use image segmentation followed by object recognition algorithms. These systems are not robust for most real-world applications. In contrast, the system presented here achieves robust performance by using reinforcement learning to induce a mapping from input images to corresponding segmentation parameters. This is accomplished by using the confidence level of model matching as a reinforcement signal for a team of learning automata to search for segmentation parameters during training. The use of the recognition algorithm as part of the evaluation function for image segmentation gives rise to significant improvement of the system performance by automatic generation of recognition strategies. The system is verified through experiments on sequences of indoor and outdoor color images with varying external conditions View full abstract»

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  • Segmentation and factorization-based motion and structure estimation for long image sequences

    Publication Year: 1998 , Page(s): 206 - 211
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1412 KB)  

    This paper presents a computer algorithm which, given a dense temporal sequence of intensity images of multiple moving objects, can separate the images into regions showing distinct objects and, for those objects which are rotating, calculate the three-dimensional structure and motion 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