The Experts below are selected from a list of 228 Experts worldwide ranked by ideXlab platform
Manuel Ojeda-aciego - One of the best experts on this subject based on the ideXlab platform.
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Towards relational fuzzy adjunctions
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017Co-Authors: Inma P. Cabrera, Pablo Cordero, Manuel Ojeda-aciegoAbstract:The problem of studying the existence of a right adjoint for a mapping defined between sets with different fuzzy structure naturally leads to the search of new notions of adjunction which fit better with the underlying structure of domain and codomain. In this work, we introduce a version of relational fuzzy adjunction between fuzzy preposets which generalizes previous approaches in that its components are fuzzy relations. We also prove that the construction behaves properly with respect to the formation of quotient with respect to the Symmetric Kernel relation.
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Relational fuzzy Galois connections
2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (, 2017Co-Authors: Inma P. Cabrera, Pablo Cordero, Manuel Ojeda-aciegoAbstract:We propose a suitable generalization of the notion of Galois connection whose components are fuzzy relations. We prove that the construction embeds Yao's notion of fuzzy Galois connection as a particular case. Although the natural framework for the proposed notion is that of fuzzy preposets, we also prove that it behaves properly with respect to the formation of quotient with respect to the fuzzy Symmetric Kernel relation.
Alper Yilmaz - One of the best experts on this subject based on the ideXlab platform.
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Object tracking by aSymmetric Kernel mean shift with automatic scale and orientation selection
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007Co-Authors: Alper YilmazAbstract:Tracking objects using the mean shift method is performed by iteratively translating a Kernel in the image space such that the past and current object observations are similar. Traditional mean shift method requires a Symmetric Kernel, such as a circle or an ellipse, and assumes constancy of the object scale and orientation during the course of tracking. In a tracking scenario, it is not uncommon to observe objects with complex shapes whose scale and orientation constantly change due to the camera and object motions. In this paper, we present an object tracking method based on the aSymmetric Kernel mean shift, in which the scale and orientation of the Kernel adaptively change depending on the observations at each iteration. Proposed method extends the traditional mean shift tracking, which is performed in the image coordinates, by including the scale and orientation as additional dimensions and simultaneously estimates all the unknowns in a few number of mean shift iterations. The experimental results show that the proposed method is superior to the traditional mean shift tracking in the following aspects: 1) it provides consistent object tracking throughout the video; 2) it is not effected by the scale and orientation changes of the tracked objects; 3) it is less prone to the background clutter.
André Kaup - One of the best experts on this subject based on the ideXlab platform.
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Scale and shape adaptive mean shift object tracking in video sequences
2009 17th European Signal Processing Conference, 2009Co-Authors: Katharina Quast, André KaupAbstract:A new technique for object tracking based on the mean shift method is presented. Instead of using a Symmetric Kernel like in traditional mean shift tracking, the proposed tracking algorithm uses an aSymmetric Kernel which is retrieved from an object mask. During the mean shift iterations not only the new object position is located but also the Kernel scale is altered according to the object scale, providing an initial adaption of the object shape. The final shape of the Kernel is then obtained by segmenting the area inside and around the adapted Kernel and distinguishing the object segments from the non-object segments. Thus, the object shape is tracked very well even if the object is performing out-of-plane rotations.
Inma P. Cabrera - One of the best experts on this subject based on the ideXlab platform.
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Towards relational fuzzy adjunctions
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017Co-Authors: Inma P. Cabrera, Pablo Cordero, Manuel Ojeda-aciegoAbstract:The problem of studying the existence of a right adjoint for a mapping defined between sets with different fuzzy structure naturally leads to the search of new notions of adjunction which fit better with the underlying structure of domain and codomain. In this work, we introduce a version of relational fuzzy adjunction between fuzzy preposets which generalizes previous approaches in that its components are fuzzy relations. We also prove that the construction behaves properly with respect to the formation of quotient with respect to the Symmetric Kernel relation.
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Relational fuzzy Galois connections
2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (, 2017Co-Authors: Inma P. Cabrera, Pablo Cordero, Manuel Ojeda-aciegoAbstract:We propose a suitable generalization of the notion of Galois connection whose components are fuzzy relations. We prove that the construction embeds Yao's notion of fuzzy Galois connection as a particular case. Although the natural framework for the proposed notion is that of fuzzy preposets, we also prove that it behaves properly with respect to the formation of quotient with respect to the fuzzy Symmetric Kernel relation.
Andrew R Webb - One of the best experts on this subject based on the ideXlab platform.
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A loss function approach to model selection in nonlinear principal components
Neural Networks, 1999Co-Authors: Andrew R WebbAbstract:The nonlinear transformation of the input variables that characterises the first nonlinear principal component is modelled as a linear sum of radially-Symmetric Kernel functions. It is shown that the parameters of the variance maximising transformation may be obtained through the minimisation of a loss function measuring departure from homogeneity. An alternating least squares algorithm is given. This is used as the basis of a cross-validation routine for model selection.
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an approach to non linear principal components analysis using radially Symmetric Kernel functions
Statistics and Computing, 1996Co-Authors: Andrew R WebbAbstract:An approach to non-linear principal components using radially Symmetric Kernel basis functions is described. The procedure consists of two steps: a projection of the data set to a reduced dimension using a non-linear transformation whose parameters are determined by the solution of a generalized Symmetric eigenvector equation. This is achieved by demanding a maximum variance transformation subject to a normalization condition (Hotelling's approach) and can be related to the homogeneity analysis approach of Gifi through the minimization of a loss function. The transformed variables are the principal components whose values define contours, or more generally hypersurfaces, in the data space. The second stage of the procedure defines the fitting surface, the principal surface, in the data space (again as a weighted sum of Kernel basis functions) using the definition of self-consistency of Hastie and Stuetzle. The parameters of this principal surface are determined by a singular value decomposition and crossvalidation is used to obtain the Kernel bandwidths. The approach is assessed on four data sets.
