T. Cootes and C. Taylor. Modelling object appearance using the greylevel surface. In Proceedings of the British Machine Vision Conference, 1994.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....specular reflection cause the most difficulties in the context of computer vision. Highly sophisticated approaches use for example an approximate 3 dimensional representation of the scene and the position of the light source(s) 6] 8] a combined PCA model of shape and intensity on landmark points [4], respectively active shapes [5] 7] a model for the object under multiple illumination situations (Eigenfaces [13] a direct model of the illumination variation and specular reflections [2] or 3 dimensional models and neural networks to estimate the position of the light sources [3] Global ....

T.F. Cootes and C.J. Taylor. Modelling object appearance using the grey-level surface. In Proceedings of the 5th British Machine Vision Conference, pages 479--488, York, 1994.

....the joints of the human body have been marked (e.g. Chen and Lee [6] Bulpitt [7] In section 2.6, the non representational eigenimage model of Murphy et al. [8] is summarised. This approach is related to the #eigenface# approach of Turk and Pentland [9] and the grey level extensions to the PDM [10, 11] and has many similarities with the approach taken in this thesis. However, one of the drawbacks of #image# based representations is the computational cost involved in operating on relatively large windows of image pixels (e.g. in calculating optical #ow) Furthermore the dimensionality of the ....

....the general pedestrian tracking problem in a noisy environment (e.g. for a crowded scene) that has motivated the work in this thesis. A similar approach is taken by Turk and Pentland for face representation [9] Cootes et al. have combined an eigenimage approach with the shape model of the LPDM [10, 11]. 2.7 The WALKER model Articulated, primitive based 3D models have been used successfully in a variety of applications (e.g. DigitEyes [40] Lowe re#nement [22] Much of this work is based on the work of Hogg [3] in which a representational model of a walking person (based on the Marr and ....

T F Cootes and C J Taylor. Modelling object appearance using the grey-level surface. In British Machine Vision Conference, volume 2, pages 479#488, 1994.

....Finally, when pedestrians change direction, their profile will also change, making it difficult for a purely shape based tracker to know that it is the same person. One possible approach to overcome these problems is to use both intensity and shape information to track pedestrians. Cootes et al.[5] have used intensity and shape information to model object appearance. Landmark points on an object are hand picked, and a triangulation from these points is used to generate a dense network of vertices. The coordinates of these vertices are combined with the intensity values at the vertices to ....

Cootes, T.F., Taylor, C.J. Modelling Object Appearance Using The Grey-Level Surface. Draft Paper, University of Manchester. November 1993.

.... related problems (see [3] 8] 10] for other topics) Highly sophisticated approaches use for example an approximate 3 dimensional representation of the scene and try to estimate shape, reflection coefficient, and illumination [7] 10] a combined PCA model of shape and intensity on landmark points [5], respectively active shapes [6] 9] a model for the object under multiple illumination situations [15] Eigenfaces) 2] a model of the illumination variation and specular reflections [3] or 3 dimensional models and neural networks to estimate the position of the light sources [4] Global ....

T.F. Cootes and C.J. Taylor. Modelling object appearance using the grey-level surface. In Proceedings of the 5th British Machine Vision Conference, pages 479--488, York, 1994.

.... image attributes, since a single attribute may simply not be present or lack sufficient discriminatory power for a number of real world applications [24, 9, 26, 12] In this framework, we have previously [21, 19, 20] modeled an image as a greylevel intensity surface (XYI representation) see also [3]) and we have used that for matching and retrieval in a face database. On the other hand, Dorai and Jain [5, 6] use Koenderink s definition of the shape index [13] for characterizing a surface, with an application to matching and recognition of free form surfaces in range images. In this paper, ....

T.F. Cootes and C.J.Taylor. Modelling object appearance using the greylevel surface. In Proceedings of the British Machine Vision Conference, 1994.

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T. F. Cootes and C. J. Taylor. Modelling object appearance using the grey-level surface. In E. Hancock, editor, 5 British Machine Vison Conference, pages 479--488, York, England, Sept. 1994. BMVA Press.

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Cootes, T., and Taylor, C: Modelling object appearance using the grey-level surface. In: British Machine Vision Conference, (1994) 479--488

....and co workers [8] 9] synthesise new views of an object from a set of example views. They fit the model to an unseen view by a stochastic optimisation procedure. This 682 British Machine Vision Conference is slow, but can be robust because of the quality of the synthesised images. Cootes et al. [4] describe a 3D model of the grey level surface, allowing full synthesis of shape and appearance. However, they do not suggest a plausible search algorithm to match the model to a new image. Nastar at al [12] describe a related model of the 3D grey level surface, combining physical and statistical ....

T.F. Cootes and C.J. Taylor. Modelling object appearance using the grey-level surface. In E Hancock, editor, # ## British Machine Vison Conference, pages 479--488, York, England, September 1994. BMVA Press.

....8.3. OVERVIEW OF AAM SEARCH 42 Poggio and co workers [24] 38] synthesise new views of an object from a set of example views. They t the model to an unseen view by a stochastic optimisation procedure. This is slow, but can be robust because of the quality of the synthesised images. Cootes et al. [14] describe a 3D model of the grey level surface, allowing full synthesis of shape and appearance. However, they do not suggest a plausible search algorithm to match the model to a new image. Nastar at al [54] describe a related model of the 3D grey level surface, combining physical and statistical ....

T. F. Cootes and C. J. Taylor. Modelling object appearance using the grey-level surface. In E. Hancock, editor, 5 th British Machine Vison Conference, pages 479-488, York, England, Sept. 1994. BMVA Press.

....correlation based methods. Poggio and co workers [17] 30] synthesise new views of an object from a set of example views. They t the model to an unseen view by a stochastic optimisation procedure. This is slow, but can be robust because of the quality of the synthesised images. Cootes et al. [9] describe a 3D model of the grey level surface, allowing full synthesis of shape and appearance. However, they do not suggest a plausible search algorithm to match the model to a new image. Nastar at al [48] describe a related model of the 3D grey level surface, combining physical and statistical ....

T. F. Cootes and C. J. Taylor. Modelling object appearance using the grey-level surface. In E. Hancock, editor, 5 th British Machine Vison Conference, pages 479-488, York, England, Sept. 1994. BMVA Press.

....in the class of images to be analysed. The Active Appearance Model (AAM) approach that we describe also draws on other previous work. Cootes et al. describe a model of the position intensity surface, allowing full synthesis of the appearance of objects that are variable in shape and intensity [11]. They do not, however, describe a plausible matching algorithm. Nastar et al. describe a related model combining physical and statistical modes of deformation [20] Although they describe a matching algorithm it requires very good initialisation. Jones and Poggio use a model capable of ....

Cootes, T., and Taylor, C: Modelling object appearance using the grey-level surface. In: British Machine Vision Conference, (1994) 479-488

....1 Introduction Model based approaches to the interpretation and coding of face images have proved very successful. Methods described so far include: Modelling grey level variation using eigenfaces [1, 2] models based on class specific linear projection [3] combined shape and grey level models[4, 5], models based on the physical and anatomical structure of faces [6] 3D models [7] hand crafted shape models [8] local non linear shape manifolds[9] and models based on elastic meshes coubled with local intensity pattern descriptions[10] Compehensive literature reviews of these techniques and ....

T.F.Cootes and C.J.Taylor. Modelling Object Appearance Using the Grey-Level Surface, Procs of BMVC94 (ed E. Hancock) , 2, pp 479-488, BMVA Press, 1994.

....using correlation based methods. Ezzat and Poggio [9] synthesise new views of a face from a set of example views. They fit the model to an unseen view by a stochastic optimisation procedure. This is extremely slow, but can be robust because of the quality of the synthesised images. Cootes et al. [3] describe a 3D model of the grey level surface, allowing full synthesis of shape and appearance. However, they do not suggest a plausible search algorithm to match the model to a new image. Nastar at al [15] describe a related model of the 3D grey level surface, combining physical and statistical ....

T. Cootes and C. Taylor. Modelling object appearance using the grey-level surface. In E. Hancock, editor, 5 th British Machine Vison Conference, pages 479--488, York, England, September 1994. BMVA Press.

....correlation based methods. Poggio and co workers [10] 12] synthesise new views of an object from a set of example views. They fit the model to an unseen view by a stochastic optimisation procedure. This is slow, but can be robust because of the quality of the synthesised images. Cootes et al. [5] describe a 3D model of the grey level surface, allowing full synthesis of shape and appearance. However, they do not suggest a plausible search algorithm to match the model to a new image. Nastar at al [16] describe a related model of the 3D grey level surface, combining physical and ....

T. Cootes and C. Taylor. Modelling object appearance using the grey-level surface. In E. Hancock, editor, 5 th British Machine Vison Conference, pages 479--488, York, England, September 1994. BMVA Press.

....and co workers [8] 9] synthesise new views of an object from a set of example views. They fit the model to an unseen view by a stochastic optimisation procedure. This British Machine Vision Conference 3 is slow, but can be robust because of the quality of the synthesised images. Cootes et al. [4] describe a 3D model of the grey level surface, allowing full synthesis of shape and appearance. However, they do not suggest a plausible search algorithm to match the model to a new image. Nastar at al [12] describe a related model of the 3D grey level surface, combining physical and statistical ....

T. Cootes and C. Taylor. Modelling object appearance using the grey-level surface. In E. Hancock, editor, 5 th British Machine Vison Conference, pages 479--488, York, England, September 1994. BMVA Press.

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T. Cootes and C. Taylor. Modelling object appearance using the greylevel surface. In Proceedings of the British Machine Vision Conference, 1994.

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