Baumberg, A. and Hogg, D. 1994. Learning flexible models from image sequences. In Proc. of the European Conference on Computer Vision, pp. 299--308.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....parts and merge them into a single component. This makes it difficult to distinguish the covered parts. The second effect is that the clothes may generate some spurious body parts along the silhouette that distract from the locations of the real body parts. Although the global deformable templates [32, 41] can model some shape deformations, they have difficulty dealing with large articulated motions and partial occlusion. To handle these effects, I first introduce merged body parts to model the merging of multiple body parts as shown in Fig. 1.7. Using the merged body parts, various shape ....

....20 representation is stable in the presence of noise and sensitive to fine level features, but is impractical because it contains few actual constraints to support human detection. Contour based representations have been used to model the 2D human shape. Baumberg et al. and Sullivan et al. [32, 33] employed a deformable template to handle shape deformation, where the shape model is derived from a set of training shapes. The orthogonal shape parameters are estimated using Principle Component Analysis(PCA) One drawback with this approach is that the model and the extracted contour should be ....

A. Baumberg, D. Hogg, "Learning Flexible Models from Image Sequences," Proc. European Conf. on Computer Vision, pp. 299-308, 1994.

....simple generic volume model described above with the parameters for spatial extent, position on the ground plane and motion refined over time by the visual evidence. A promising scheme of this kind using the point distribution model has been tested for tracking human figures in image sequences [6]. 3.4 Tracking objects for dynamic scene interpretation For reasons of accuracy and robustness, model based object recognition has been adopted as one of the key components in surveillance systems [56, 28, 17] However, it is recognised that the temporal correlations between objects over time ....

A. Baumberg and D. Hogg. "Learning flexible models from image sequences". Technical report, Division of Artificial Intelligence, School of Computer Science, University of Leeds, England, 1993. Research report series 93.36.

....of shape can result, leading to difficulty in defining shape constraints. In practice, correspondence has often been established using manually defined landmarks a time consuming, subjective and error prone process. Several previous attempts have been made to automate model building [1, 2, 6, 7, 8, 9, 10, 13] . The problem of establishing dense correspondence over a set of training boundaries can be posed as that of defining a parameterisation for each of the training shapes, leading to an implicit correspondence between equivalently parameterised points. Different arbitrary parameterisations of the ....

....correspondence over a set of training boundaries can be posed as that of defining a parameterisation for each of the training shapes, leading to an implicit correspondence between equivalently parameterised points. Different arbitrary parameterisations of the training boundaries have been proposed [1, 9] , but do not address the issue of optimality. Shape features (e.g. regions of high curvature) have been used to establish point correspondences, 2, 8, 13] but, although this approach corresponds with human intuition, it is still not clear that it is in any sense optimal. A third approach, and ....

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Baumberg, A. and D. Hogg, Learning Flexible Models from Image Sequences, in European Conference on Computer Vision, Stockholm, Sweden. 1994. p. 299-308.

.... propagated among others by Menet et al. 129] and Cipolla and Blake [39] Increasingly more elab orate models of shape and appearance have been proposed by Cootes, Taylor and co workers under the names of point distribution model, active shape and active appearance model [44] Baumberg and Hogg [10] presented a method for automatic shape acquisition using background subtraction and a spline based shape analysis. More recently, a number of nonlinear models of shape variation were presented, i.e. models where the permissible shape variation is not constructed by a linear combination of ....

....in the training set. This has been done for example in [44] However, this is a supervised approach and the manual interaction can be cumbersome if the number of training shapes is large. An application to large training sets is therefore infeasible. Therefore, we have chosen a different approach [10], where the training images are preprocessed in order to adapt them to an automatic spline fit. In practice, we binarize the training images by applying a threshold and if necessary a median filter to remove noise. In more difficult background conditions one can preprocess the input images ....

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A. Baumberg and D. Hogg. Learning flexible models from image se- quences. In J.O. Eklundh, editor, Proc. of the Europ. Conf. on Comp. Vis., volume 801 of LNCS, pages 316-327. Springer-Verlag, 1994.

....statisical shape models that can be directly matched to observed shape contours. Models which capture the 2D distribution of feature point locations have been shown to be able to describe a wide range of flexible shapes, and they can be directly matched to input images [4] The authors of [1] developed a single view model of pedestrian contours, and showed how a linear subspace model formed from principal components analysis could represent and track a wide range of motion [2] A model appropriate for feature point locations sampled from a contour is also given in [2] This ....

A. Baumberg and D. Hogg. Learning flexible models from image sequences. In Proceedings of European Conference on Computer Vision, 1994.

.... This arrangement parallels the situation in animal vision in which slow head movements can be compensated by good proprioception, via the vestibulo ocular reflex [1] Background intensity variations A number of researchers have used image differencing to increase the robustness of tracking [23, 4, 19]. This uses a simple model of the background in which its mean intensity is represented as an image. Off line estimation of this mean can be made robust to occasional moving objects by using a suitable filter the median filter for instance. Once the mean image is obtained it is stored for ....

Adam Baumberg and David Hogg. Learning flexible models from image sequences. In Jan-Olof Eklundh, editor, Computer Vision - ECCV '94, volume Volume I, pages 299 -- 308. Springer-Verlag, 1994.

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Baumberg, A., Hogg, D.: Learning flexible models from image sequences. In: European Conference on Computer Vision, Springer Verlag (1994) 299 308

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Baumberg, A. and Hogg, D. 1994. Learning flexible models from image sequences. In Proc. of the European Conference on Computer Vision, pp. 299--308.

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A. Baumberg and D. Hogg. Learning flexible models from image sequences. 3rd European Conference on Computer Vision, 1:299--308, 1994.

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A. Baumberg and D. C. Hogg. Learning flexible models from image sequences. In Proc. European Conference on Computer Vision, 1994.

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A. Baumberg and D. Hogg. Learning flexible models from image sequences. In J. Eklundh, editor, ECCV-94, vol. 800 of LNCS-Series, pp. 299--308, Stockholm, 1994.

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A. Baumberg and D. Hogg. Learning flexible models from image sequences. ECCV94, pp. 299--308, Stockholm, 1994. 920

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A. Baumberg and D. Hogg. Learning flexible models from image sequences. Lecture Notes in Computer Science, 800:299--308, 1994.

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A. Baumberg and D. Hogg. Learning flexible models from image sequences. ECCV, 1994.

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A. Baumberg and D. Hogg. Learning flexible models from image sequences. In J.-O. Eklundh, European Conference on Computer Vision, volume 1, pages 299--308. Springer-Verlag, Berlin, 1994.

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A. Baumberg and D. Hogg. Learning flexible models from image sequences. In J. Eklundh, editor, European Conf. on Computer Vision, ECCV-94, volume 800 of LNCS-Series, pages 299--308, Stockholm, Sweden, 1994. Springer-Verlag.

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A. Baumberg and D. Hogg, "Learning flexible models from image sequences," in Proc. of European Conference on Computer Vision, vol. 1, pp. 229-308, May 1994.

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A. Baumberg and D. Hogg. Learning flexible models from image sequences. In Proc of ECCV, pages 299--308, 1994.

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A. Baumberg, D.C. Hogg, Learning Flexible Models from Image Sequences, Proc. of the European Conference on Computer Vision: ECCV'94, Springer LNCS 800, pp.299-308, 1994.

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A. Baumberg and D. Hogg. Learning flexible models from image sequences. In Proceedings of European Conference on Computer Vision, 1994.

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A. Baumberg and D. Hogg, "Learning flexible models from image sequences, " in Proc. Eur. Conf. Computer Vision, vol. 1, Berlin, Germany, May 1994, pp. 229--308.

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A. Baumberg and D. Hogg. Learning flexible models from image sequences. In J.-O. Eklundh, editor, 3 Conference on Computer Vision, volume 1, pages 299--308. Springer-Verlag, Berlin, 1994. 1

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A. Baumberg, D. Hogg, "Learning Flexible Models from Image Sequences," Proc. European Conf. on Computer Vision, pp. 299-308, 1994.

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A Baumberg, DC Hogg , Learning Flexible Models from Image Sequences, Proc. of the European Conference on Computer Vision (ECCV), 1994.

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A. Baumberg and D. Hogg, "Learning flexible models from image sequences," in Proc. of European Conference on Computer Vision, vol. 1, pp. 229-308, May 1994.

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