T.F.Cootes and C.J.Taylor. On representing edge structure for model matching. volume 1, pages 1114--1119, 2001. Home/Search Document Details and Download
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Face Alignment Using Texture-Constrained Active Shape.. - Yan, Liu, Li, Zhang.. (2003) (Correct)
....statistics around each landmark, efficiently finds the best candidate point for each landmark in searching the image. The solution space is constrained by the properly trained global shape model. By means of modeling of the local features, ASM obtains nice results in shape localization. AAM [2,3,10] combines constraints on both shape and texture in its characterization of face appearance. In the context of this paper, texture means the intensity patch contained in the shape after warping to the mean shape [4] There are two linear mappings assumed for optimization: from appearance variation ....
T. Cootes, C. Taylor, On representing edge structure for model matching, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1 (2001) 1114 -- 1119.
Texture-Constrained Active Shape Models - Yan, Liu, Li, Zhang, Shum, Cheng (2002) (2 citations) (Correct)
....each landmark, e#ciently finds the best candidate point for each landmark in searching the image. The solution space is constrained by the properly trained global shape model. Based on the accurate modeling of the local features, ASM obtains nice results in shape localization. AAM [6] 7] 8] [9] combines constraints on both shape and texture in its characterization of face appearance. In the context of this paper, texture means the intensity patch contained in the shape after warping to the mean shape [1] There are two linear mappings assumed for optimization: from appearance variation ....
T.F.Cootes and C.J.Taylor, "On representing edge structure for model matching," Proc. CVPR 2001, vol. 1, pp. 114--1119.
Multi-band Modelling of Appearance - Stegmann, Larsen (2002) (2 citations) (Correct)
....between unseen input images and synthesised images and subsequently drive these to equality. In this paper, we investigate a generative model that has proven widely applicable. The Active Appearance Models (AAMs) 12] 13] have been applied to most of the examples given above. As Cootes et al. [14] the appearance of edge strength is modelled, but in contrast this is augmented with colour information and conventional raw intensities. We show that a considerable gain in accuracy can be achieved, merely by selecting a more appropriate representation of the particular object class being ....
....a representation less sensitive to these. First, we notice that lighting e ects have less in uence on the hue band in the Hue, Saturation and Value (HSV) colour space. By modelling hue, we aim at obtaining the speci city of colour models without the sensitivity to e ects of lighting. Second, as [14] we notice that edge estimators per se are less sensitive to lighting effects than raw intensities. Since edge estimators are implemented as numeric di erential operators (e.g. Sobel lters) these are unfortunately inherently sensitive to noise, which calls out for some degree of regularisation. ....
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T. F. Cootes and C. J. Taylor, \On representing edge structure for model matching," in Proc. IEEE Computer Vision and Pattern Recognition { CVPR. 2001, vol. 1, pp. 1114-1119, IEEE.
Analysis of Features for Rigid Structure Vehicle Type.. - Petrovic, Cootes Self-citation (Cootes) (Correct)
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T.F.Cootes and C.J.Taylor. On representing edge structure for model matching. volume 1, pages 1114--1119, 2001.
Comparing Variations on the Active Appearance Model Algorithm - Cootes, Kittipanya-ngam (2002) (14 citations) Self-citation (Cootes) (Correct)
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T.F.Cootes and C.J.Taylor. On representing edge structure for model matching. In Computer Vision and Pattern Recognition Conference 2001.
Comparing Variations on the Active Appearance Model Algorithm - Cootes, Kittipanya-ngam (2002) (14 citations) Self-citation (Cootes) (Correct)
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T.F.Cootes and C.J.Taylor. On representing edge structure for model matching. In Computer Vision and Pattern Recognition Conference 2001.
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