S. McKenna, S. Gong, and Y. Raja. Modelling facial colour and identity with gaussian mixtures. Pattern Recognition, 31(12):1883--1892, Dec. 1998. Home/Search Document Details and Download
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Visual System for Tracking and Interpreting Selected Human Actions - Kwolek (2003) (Correct)
....WSCG 2003, February 3 7, 2003, Plzen, Czech Republic. Copyright UNION Agency Science Press is located using skin color detection in the HSV color space and color thresholding techniques. Well established methods of color distribution modeling, such as histograms and Gaussian mixture models [Mck98a] have enabled the construction of suitably accurate skin filters. However such techniques are not ideal for use in adaptive real time applications with moving camera. Our Approach In this work we refer to modern interfaces which aid human computer interaction. The main result of this work is a ....
McKenna, S.J., Gong, S., and Raja, Y. Modelling facial colour and identity with Gaussian mixtures, Pattern Recognition 31, No.12, pp.18831892, 1998.
Adaptive Texture and Color Segmentation for Tracking.. - Ozyildiz.. (2002) (2 citations) (Correct)
....Key words. Visual Tracking, Color Segmentation, Texture Segmentation, Cue Fusion Corresponding author. Preprint submitted to Elsevier Preprint 4 December 2001 1 Introduction Some of the most popular methods for real time visual tracking of moving objects are based on color segmentation [1, 2, 3]. The main reason for choosing color based segmentation is that the color cue is relatively invariant to scale, illumination and viewing direction while being computa tionally efficient. Although the different color based tracking approaches reported in the literature demonstrate a certain ....
....For example, 7] and [8] present comparisons of unsupervised and supervised color segmentation al gorithms respectively. 3 For online applications, two color segmentation methods are mostly used. The first method uses Gaussian mixture models to characterize color distribution of an object [1] [3] 4] The second method employs a histogram model [9] 10] The histogram based methods are basically non parametric forms of density estimation in color space. Although color is an efficient cue for computer vision applications, a number of viewing factors, such as light sources, background ....
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S.J McKenna, S. Gong, Y. Raja, Modelling facial colour and identity with Gaussian mixtures, Pattern Recognition, 31(12)(1998), pp. 1883-1892.
Estimation Of The Illuminant Colour Using Highlights.. - Störring, Granum.. (2000) (Correct)
....Model 1. INTRODUCTION In many computer vision applications humans are part of the scenario and may play the main role. For example new human computer interfaces need to detect and track human faces and hands, respectively. The segmentation of skin colour is an often used feature in such systems [1, 2], mostly as an initial segmentation of faces or hands in the camera image. Hence, a reliable output of such a method is imperative for a robust system. However, as for any other material, the same skin area appears as two different colours under two different illuminations, which makes colour ....
Stephen J. McKenna, Shaogang Gong, and Yogesh Raja. Modelling facial colour and identity with Gaussian mixtures. Pattern Recognition, 31(12):1883--1892, December 1998.
Mixture Densities for Video Objects Recognition - Hammoud, Mohr (2000) (Correct)
....of different classification approaches, K nearest neighbor, Gaussian, and Gaussian mixture, using a view based approach for motion representation. According to the results of his experiments on eight human actions, a mixture of Gaussians could be a good model for the data distribution. McKenna [7] use the Gaussian color mixture to track and model face classes in natural scenes (video) This work is the closest to the contribution presented in this paper; it differs mainly by the input data which are tracked objects in our case, and in technical details like Gaussian models and the related ....
....In order to limit dependence on the initial position, the algorithm is run several times (10 times in our experiments) and the best solution is kept. Gaussian models. Gaussian mixtures are sufficiently general to model arbitrarily complex, non linear distribution accurately given enough data [7]. When the data is limited, the method should be constrained to provide better conditioning for the estimation. The various possible constraints on the covariance parameters of a Gaussian mixture (e.g. all classes have the same covariance matrix, an identity covariance matrix, defines 14 ....
S. J. Mckenna, S. Gong, and Y. Raja. Modelling facial colour and identity with gaussian mixtures. Pattern recognition, 31(12):1883--1892, 1998.
Probabilistic Hierarchical Framework for Clustering Tracked.. - Hammoud, Mohr (2000) (Correct)
....(tracked objects; color distributions) and some related criterion related to mixture density estimation (number of Gaussian components is not fixed) Also, the classification technique we use is the AHC. Modeling with Gaussian mixture is now becoming very popular. Rosales [Ros98] McKenna et al. MGR98] and Hammoud at al. HM00a] use the Gaussian mixture model to recognize human actions, face colors and non rigid moving objects in videos, respectively. Then, they use the Gaussian mixture classifier to identify the appropriate class of new entities (action, face or object) The modeling of the ....
S.J. Mckenna, S. Gong, and Y. Raja. Modelling facial colour and identity with gaussian mixtures. Pattern Recognition, 31(12):1883--1892, 1998.
Self-Organized Integration of Adaptive Visual Cues for.. - Triesch, von der Malsburg (2000) (6 citations) (Correct)
....task of face tracking, since they introduce more a priori knowledge about the problem. Often the characteristic shape of the head shoulder contour is used [3, 1] However, these approaches lack an adaptive component. The system by MCKENNA et al. is adaptive, but it relies on a single color cue [6]. It can only adapt to slow continuous changes. In contrast, the system presented here can also cope with successions of sudden changes as long as they affect only a minority of cues at the same time. Related Work on Sensor Fusion. Sensory integration plays an important role in many fields of ....
S. J. McKenna, S. Gong, and Y. Raja. Modelling facial colour and identity with gaussian mixtures. Pattern Recognition, 31(12):1883--1892, 1998.
Support vector machine based multi-view face detection and.. - Shaogang Gong Jamie (2004) Self-citation (Gong) (Correct)
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S. McKenna, S. Gong, Y. Raja, Modelling facial colour and identity with gaussian mixtures, Pattern Recognition 31 (12) (1998) 1883 -- 1892.
Decision Fusion in Identity Verification using Facial Images - Czyz (2003) (Correct)
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S. McKenna, S. Gong, and Y. Raja. Modelling facial colour and identity with gaussian mixtures. Pattern Recognition, 31(12):1883--1892, Dec. 1998.
Skin Detection: A Bayesian Network Approach - Sebe, Cohen, Huang, Gevers (Correct)
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S. McKenna, S.Gong, and Y.Raja. Modelling facial colour and identity with gaussian mixtures. Pattern Recognition, 31:1883--1892, 1998.
A Fast and Accurate Faces Localization Using Gradient Method - Kukharev, Masicz, Masicz (2004) (Correct)
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McKenna, S., Gong, S., Raja, Y. Modelling Facial Colour and Identity with Gaussian Mixtures. Pattern Recognition, vol. 31, no. 12, pp. 1883-1892, 1998.
Self-Organized Integration of Adaptive Visual Cues for Face.. - Triesch (2000) (6 citations) (Correct)
No context found.
S. J. McKenna, S. Gong, and Y. Raja, \Modelling facial colour and identity with gaussian mixtures," Pattern Recognition 31(12), pp. 1883-1892, 1998.
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