The problem of searching the elements of a set that are close to a given query element under some similarity criterion has a vast number of applications in many branches of computer science, from pattern recognition to textual and multimedia information retrieval. We are interested in the rather general case where the similarity criterion defines a metric space, instead of the more restricted case of a vector space. Many solutions have been proposed in different areas, in many cases without cross-knowledge. Because of this, the same ideas have been reconceived several times, and very different presentations have been given for the same approaches. We present some basic results that explain the intrinsic difficulty of the search problem. This includes a quantitative definition of the elusive concept of "intrinsic dimensionality." We also present a unified view of all the known proposals to organize metric spaces, so as to be able to understand them under a common framework. Most approaches turn out to be variations on a few different concepts. We organize those works in a taxonomy that allows us to devise new algorithms from combinations of concepts not noticed before because of the lack of communication between different communities. We present experiments validating our results and comparing the existing approaches. We finish with recommendations for practitioners and open questions for future development.
Anti-tumor mAbs hold promise for cancer therapy, but are relatively inefficient. Therefore, there is a need for agents that might amplify the effectiveness of these mAbs. One such agent is β-glucan, a polysaccharide produced by fungi, yeast, and grains, but not mammalian cells. β-Glucans are bound by C receptor 3 (CR3) and, in concert with target-associated complement fragment iC3b, elicit phagocytosis and killing of yeast. β-Glucans may also promote killing of iC3b-opsonized tumor cells engendered by administration of anti-tumor mAbs. In this study, we report that tumor-bearing mice treated with a combination of β-glucan and an anti-tumor mAb show almost complete cessation of tumor growth. This activity evidently derives from a 25-kDa fragment of β-glucan released by macrophage processing of the parent polysaccharide. This fragment, but not parent β-glucan, binds to neutrophil CR3, induces CBRM 1/5 neoepitope expression, and elicits CR3-dependent cytotoxicity. These events require phosphorylation of the tyrosine kinase, Syk, and consequent PI3K activation because β-glucan-mediated CR3-dependent cytotoxicity is greatly decreased by inhibition of these signaling molecules. Thus, β-glucan enhances tumor killing through a cascade of events, including in vivo macrophage cleavage of the polysaccharide, dual CR3 ligation, and CR3-Syk-PI3K signaling. These results are important inasmuch as β-glucan, an agent without evident toxicity, may be used to amplify tumor cell killing and may open new opportunities in the immunotherapy of cancer.
Parametric image segmentation consists of finding a label field that defines a partition of an image into a set of nonoverlapping regions and the parameters of the models that describe the variation of some property within each region. A new Bayesian formulation for the solution of this problem is presented, based on the key idea of using a doubly stochastic prior model for the label field, which allows one to find exact optimal estimators for both this field and the model parameters by the minimization of a differentiable function. An efficient minimization algorithm and comparisons with existing methods on synthetic images are presented, as well as examples of realistic applications to the segmentation of Magnetic Resonance volumes and to motion segmentation.
Many problems in early vision can be formulated in terms of minimizing a cost function. Examples are shape from shading, edge detection, motion analysis, structure from motion, and surface interpolation. As shown by Poggio and Koch Proc. R. Soc. London, Ser. B 226, 303-323], quadratic variational problems, an important subset of early vision tasks, can be "solved" by linear, analog electrical, or chemical networks. However, in the presence of discontinuities, the cost function is nonquadratic, raising the question of designing efficient algorithms for computing the optimal solution. Recently, Hopfield and Tank [Hopfield, J. J. & Tank, D. W. (1985) Biol. Cylern. 52,[141][142][143][144][145][146][147][148][149][150][151][152] have shown that networks of nonlinear analog "neurons" canibe effective in computing the solution ofoptimization problems. We show how these networks can be generalized to solve the nonconvex energy functionals of early vision. We illustrate this approach by implementing a specific analog network, solving the problem of reconstructing a smooth surface from sparse data while preserving its discontinuities. These results suggest a novel computational strategy for solving early vision problems in both biological and real-time artificial vision systems.This study addresses the use of simple analog networks to implement and solve problems in early vision, such as computing depth from two stereoscopic images, reconstructing and smoothing images from sparsely sampled data, and computing motion. Within the last years, computational studies have provided promising theories ofthe computations necessary for early vision (for partial reviews, see refs. 1-5). A number of early vision tasks can be described within the framework of standard regularization theory (5). Standard regularization analysis can be used to solve these problems in terms of quadratic energy functionals that must be minimized. Previous work by Poggio and Koch (6) showed how to design linear, analog networks for solving regularization problems with quadratic energy functions. The domain of applicability of standard regularization theory is limited, however, by the convexity of the energy functions, which makes it impossible to deal with problems involving true discontinuities. Such problems can be described by nonconvex energy functions involving binary line processes (7-10). More recently Marroquin (11) has proposed an approach to early vision based on the use of Markov random-field models and Bayes estimation theory (11, 33). We will show how these algorithms map naturally onto very simple resistive networks.There has been considerable interest in the computational properties and capabilities of networks of simple, neuroitallike elements (12-15). Recently, flopfield and Tank (16) have shown that analog neuronal networks can provide fast, next-to-optimal solutions to a well-characterized but difficult optimization problem, the "traveling salesman problem."' In this paper we show that networks of simple, analog, or hybrid proce...
Automatic 3D segmentation of the brain from MR scans is a challenging problem that has received enormous amount of attention lately. Of the techniques reported in literature, very few are fully automatic. In this paper, we present an efficient and accurate, fully automatic 3D segmentation procedure for brain MR scans. It has several salient features namely, (1) instead of a single multiplicative bias field that affects all tissue intensities, separate parametric smooth models are used for the intensity of each class. This may be a more realistic model and avoids the need for a logarithmic transformation. (2) A brain atlas is used in conjunction with a robust registration procedure to find a nonrigid transformation that maps the standard brain to the specimen to be segmented. This transformation is then used to: segment the brain from non-brain tissue; compute prior probabilities for each class at each voxel location and find an appropriate automatic initialization. (3) Finally, a novel algorithm is presented which is a variant of the EM procedure, that incorporates a fast and accurate way to find optimal segmentations, given the intensity models along with the spatial coherence assumption. Experimental results with both synthetic and real data are included, as well as comparisons of the performance of our algorithm with that of other published methods.
We present a two-dimensional regularized phase-tracking technique that is capable of demodulating a single fringe pattern with either open or closed fringes. The proposed regularized phase-tracking system gives the detected phase continuously so that no further unwrapping is needed over the detected phase.
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