D. Beymer and T. Poggio. Image representations for visual learning. Science, 272:1905.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....employed for the onthe fly synthesis [RCB98] WH97] 3. LINEAR COMBINATIONS OF STATIONARY OBJECTS The basic idea of the vector space representation by a linear combination of stationary objects can be described as follows, first proposed by Ulman and Basri [UB91] and followed up for 2D images [BP96][VP97] and 3D geometries [BV99] She00] It is based on a data set of stationary objects in a same class. All of these exemplar objects are assumed in full correspondence, which can be done using techniques based on optic flow algorithms [BP96] BV99] VP97] Given a set of m exemplar objects in full ....

....by Ulman and Basri [UB91] and followed up for 2D images [BP96] VP97] and 3D geometries [BV99] She00] It is based on a data set of stationary objects in a same class. All of these exemplar objects are assumed in full correspondence, which can be done using techniques based on optic flow algorithms [BP96][BV99] VP97] Given a set of m exemplar objects in full correspondence, characterized by feature vectors X 1 , Xm such as pixels for 2D images or vertices for 3D geometries, a linear combination of them produces a new object in the same class: X = w i X i This linear combination is ....

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D. Beymer and T. Poggio. Image representations for visual learning. Science, 272(5270):1905 -- 1909, 1996.

....embedding space. This distinguishes it from other methods which retrieve perceptual manifolds in an unsupervised fashion, such as classical neural network approaches or more recent techniques, e.g. 9, 11] Even most techniques which establish the topology of views in a supervised fashion such as [7, 2, 10, 8] require an embedding space in which distances can be measured. This point is so important, because a space embedding all views of an object requires finding a view representation suitable for the whole view sphere of an object beforehand. This in turn raises di#cult questions regarding the ....

....the whole view sphere of an object beforehand. This in turn raises di#cult questions regarding the missing data problem [8] caused for example by self occlusion, or it requires normalization procedures in order to render the view representation comparable even across large changes in viewpoint [7, 2]. 4.2 Global parameterization So far, a local topology has been established between all views within an aspect, and distances between the single aspects have been defined. What is missing for complete knowledge about the manifold is a global parameterization of all views. To achieve this metric ....

D. Beymer and T. Poggio. Image representations for visual learning. Science, 272:1905 -- 1909, June 1996.

....Previous techniques in the literature also utilized optical As an interesting related side note, these same authors have also recently derived a statistical physics formulation for handling loopy Bayesian Networks. Figure 8.9: Reconstruction of Facial Images with PCA and Variant. flow [18] or variations [143] of it for image matching and also produce better reconstruction than PCA. 0 10 20 30 40 50 60 0 2 4 6 8 10 12 User Image Reconstruction 0 50 100 150 200 0 1 2 3 4 5 6 7 8 9 x 10 World Image Reconstruction (a) Agent Images (b) World Images Figure 8.10: ....

D. Beymer and T. Poggio. Image representation for visual learning. Science, 272, 1996.

.... Applications and related systems were reported in [9] 4] 8] Re rendering under more general assumptions, yet exploiting linearity of light transport, was reported in [12] 17] Work on class based synthesis and recognition of images (mostly with varying viewing positions) was reported in [5], 3] 7] 27] 26] 24] 25] 6] 2] 15] These methods adopt a reconstructionist approach (also see Section 3) in which a necessary condition for the process of synthesis is that the original novel image be generated, reconstructed, from the database of examples. For example, the linear ....

D. Beymer and T. Poggio, Image Representations for Visual Learning, Science, vol. 272, pp. 1905-1909, 1995.

....by interpolating between two existing shapes. Morphing of a face requires precise specifications of the displacements of many points in order to guarantee that the results look like real faces. Most techniques, therefore, rely on a manual specification of the morph parameters [26] Beymer et al. [25] and Bichsel [27] have proposed image analysis methods where the morph parameters are determined automatically, based on optical flow. While this approach gives an elegant solution to generating new views from a set of reference images, one still has to find the proper reference images. Moreover, ....

D. Beymer and T. Poggio, "Image representation for visual learning," Science, vol. 272, no. 28, pp. 1905.

....of style and content are synthesized from a new face image under novel illumination by changing the estimated parameters of the new image. Given a set of parameters describing facial expressions such as degree of happiness or surprise, along with a relative motion field of the face, Beymer et al. [5, 6] use a regression function that estimates a mapping from parameters to motion for synthesizing novel facial expressions from a set of labelled training images. Rather than defining individual filters by hand, Hertzmann et al. 20] automatically studied a mapping of spatially local filters from ....

D. Beymer and T. Poggio. Image representation for visual learning. Science, 272:1905.

....is, how to find the displacements of the image pixels. Warping a face requires precise specifications of the displacements of many points in order to guarantee that the results look like real faces. Most techniques therefore rely on a manual specification of the warp parameters. Poggio et al. [13][15] have proposed an image synthesis analysis framework where the warp parameters are determined automatically, based on optical flow. While this approach gives an elegant solution to generating new views from a set of reference images, one still has to find the proper reference images. Moreover, ....

D.Beymer and T.Poggio, Image Representation for Visual Learning, Science, 28 June 1996, vol 272, pp1905-1909.

....Jones et al. [22] similarly represent an image in terms of a shape vector and a texture vector. Although the texture parameters can be recovered fairly 27 easily, it turns out that the shape parameters introduce a non linearity in the search for parameters. Techniques ranging from optical ow [2] to gradient descent [8, 22] have been suggested to recover the shape. The methods described in this chapter use learning by example. Speci cally, given some example images as training data, hand annotated for shape parameters, the techniques tries to estimate the relationship between the shape ....

D. Beymer and T. Poggio. Image representations for visual learning. Science, June 1996.

....are optimized simultaneously there is extra pressure for local experts to fit the global structure of the manifold. Our work can be viewed as a synthesis of two long lines of research in unsupervised learning. In the first are efforts at learning the global structure of nonlinear manifolds [1, 4, 9, 10]; in the second are efforts at developing probabilistic graphical models for reasoning under uncertainty[5, 6, 7] Our work proposes to model the global coordinates on manifolds as latent variables, thus attempting to combine the representational advantages of both frameworks. It differs from ....

D. Beymer & T. Poggio. Image representations for visual learning. pringerScience 272 (1996).

....split into two major groups: pure feedforward network models and network models with feedback connections. The term feedforward describes the networks where the information signals flow towards one direction. Feedforward models include the wellknown Radial Basis Function (RBF) network (see [1]) that has been used for many visual tasks, as discussed above. In feedback systems information flows both directions. Feedback models include architectures using a generative approach, according which the network tries to model the density function that is assumed to have generated the data. In ....

D. J. Beymer and T. Poggio. Image representations for visual learning. Science, 272:

....an XYI surface at time t to the XYI surface at t 1. This allows for a general class of smooth deformations between frames, including both multiplicative and additive changes to intensity. One variation on the general form of (2) is the the use of object specific models of image brightness [7, 22, 23, 41]. Hager and Belhumeur [22] used principal component analysis to find a set of orthogonal basis images, B j (x) n j =1 , that spanned the ensemble of images of an object under a wide variety of illuminant directions. They constrained deviations from 14 BLACK, FLEET, AND YACOOB brightness ....

....see text. a) I (x, t ) b) I (x, t 1) c) I mean (x) d) flow 1; e) wM 1 (x) f ) w L ,M 1 (x) g) flow 2; h) diff M 1 (x) i) diff M 1 ,L (x) j) wM 2 (x) k) w S (x) l) w O (x) outliers) m) diff M 2 (x) n) diff S (x) o) I (x, t ) I mean (x) diff mean (x) [7, 9, 13, 18, 22, 23, 41], we learn parameterized models of motion and iconic structure from examples. We then use these in our mixture model framework to explain motion and iconic change in human mouths. 6.1. Learned Iconic Model To capture the iconic change in domain specific cases, such as the mouths in Fig. 14, we ....

D. Beymer and T. Poggio, Image representations for visual learning, Science 272,

....when these models are incorporated into a larger (perhaps hierarchical) architecture for probabilistic reasoning. Our work can be viewed as a synthesis of two long lines of research in unsupervised learning. In the first are efforts at learning the global structure of nonlinear manifolds [1, 4, 9, 10]; in the second are efforts at developing probabilistic graphical models for reasoning under uncertainty[5, 6, 7] Our work proposes to model the global coordinates on manifolds as latent variables, thus attempting to combine the representational advantages of both frameworks. It differs from work ....

D. Beymer & T. Poggio. Image representations for visual learning. Science 272 (1996).

....of using learning techniques for computer graphics tasks involved line drawing images of cartoon characters. The metaphor is that the computer learns to draw a cartoon character from a few example drawings provided by an artist [56] We later extended the approach to deal with real images [52]. We are now developing an image based text to visual speech (TTVS) system [57] The TTVS module takes as input unconstrained typed text and produces as output two synchronized streams: the audio stream and the video stream. Fig. 12. Example matches of novel mouths using stochastic gradient ....

D. Beymer and T. Poggio, "Image representation for visual learning," Science, vol. 272, pp. 1905.

....oldest. Typically they involved fitting data in a small number of dimensions [53, 44, 45] More recently, they also included typical learning applications, sometimes with a very high dimensionality. One example is the use of algorithms in computer graphics for synthesizing new images and videos [38, 5, 20]. The inverse problem of estimating facial expression and object pose from an image is another successful application [25] Still another case is the control of mechanical arms. There are also applications in finance, as, for instance, the estimation of the price of derivative securities, such as ....

D. Beymer and T. Poggio. Image representations for visual learning. Science, 272(5270):1905.

....needed to achieve the final goal is automatically learned from examples. Moreover it does not make use of any 3D information or explicit motion or segmentation. Some work on 2D view based models of faces have also been extended to expression recognition (Beymer et al. 3] Beymer and Poggio [2] and Cootes et al. 6] However these view based models are explicitly dependent on correspondence between facial features and therefore repeated estimation of this correspondence during analysis. In this paper, we describe models that do not rely on such explicit correspondence. The analysis of ....

....used to localize the mouths of di#erent persons as they make basic expressions such as surprise, joy and anger. The mouths grabbed are normalized to a size of 32 21 and annotated manually for the degree of openness and smile on a scale of 0 to 1 (see Beymer et al. 3] and Beymer and Poggio [2]) This is done based on the subjective interpretation of the user and no actual image parameters are measured. Figure 4 shows some examples of such a subjective annotation. 4.2 Automatic stomatic of as pars s ubs of Haar Wavelet coe# cients The input space for the regression function is ....

D. Beymer and T. Poggio. Image representations for visual learning. Science, 272(5270):1905.

....to look continuously at all directions but not to move. This is the basis for the QuickTimeVR system [7] The fixed position constraint can be relaxed by computing the optical flowbetween the example images and using it to interpolate between the cylinders constructed at different locations (cf. [4, 5, 6]) originally proposed for views, not mosaics, but the principle is the same) However, interpolation may produce physically invalid images. Seitz and Dyer [22] proposed a physically valid view interpolation method. The method involves recovering the epipolar geometry between the two acquired ....

....objects. In general, wewantto generate from one 2D view Img nov of a 3D object p nov other views, exploiting knowledge of views of other objects of the same class. This idea of generating virtual views of an object by using class specific knowledge has been discussed before (see references in [5]) Suppose that wehavetwo views Img ref and Img p of the prototype. Wetake Img ref to appear in the same pose as Img nov . Img p is a slightly transformed (i.e. rotated) view of Img ref (see diagram in Fig. 7) We can then compute the optical flow S p between these two views. Moreover, since the ....

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D. Beymer, and T. Poggio. Image Representations for Visual Learning. Science, 272, pages 1905.

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D. Beymer and T. Poggio. Image representations for visual learning. Science, 272:1905.

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Beymer D, Poggio T. Image representations for visual learning. Science 1996; 272: 1905--1909

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D. Beymer and T. Poggio. Image representation for visual learning. Science, 272, 1996.

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D. Beymer and T. Poggio. Image representations for visual learning. Science, 272:1905.

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D. Beymer and T. Poggio, "Image representations for visual learning," Science, vol. 272, no. 5250, 1996.

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D. Beymer and T. Poggio. Image representations for visual learning. Science, 272(5250), 1996.

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D. J. Beymer and T. Poggio. Image representations for visual learning. Science, 272:19051909, 1996.

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D. Beymer and T. Poggio. Image representation for visual learning. Science, 272, 1996.

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D. Beymer and T. Poggio, "Image Representations for Visual Learning," Science, Vol. 272, No. 5270, pp. 1905-1909, June 28, 1996.

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