Add to ORKGThesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 79-82).by Mohammed Saeed.M.S
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority v...
Finite mixture model (FMM) is being increasingly used for unsupervised image segmentation. In this p...
Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidde...
Segmentation of images has found widespread applications in image recognition systems. Over the last...
Abstractin this paper we describe a modified segmentation method applied to image. An EM algorithm i...
The model parameters of image in real life applications are usually unknown and are necessary for an...
Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov m...
The impressive progress on image segmentation has been witnessed recently. In this paper, an improve...
We introduce in this work the notion of a generalized mixture and propose some methods for estimatin...
Abstract—A new Bayesian model is proposed for image seg-mentation based upon Gaussian mixture models...
In computer vision, image segmentation plays foundational role. Innumerable techniques, such as acti...
This paper considers three crucial issues in processing scaled down image, the representation of par...
Spatially varying mixture models are characterized by the dependence of their mixing proportions on ...
We establish a mixture model with “spurious ” outliers and derive its maxi-mum likelihood estimator,...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority v...
Finite mixture model (FMM) is being increasingly used for unsupervised image segmentation. In this p...
Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidde...
Segmentation of images has found widespread applications in image recognition systems. Over the last...
Abstractin this paper we describe a modified segmentation method applied to image. An EM algorithm i...
The model parameters of image in real life applications are usually unknown and are necessary for an...
Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov m...
The impressive progress on image segmentation has been witnessed recently. In this paper, an improve...
We introduce in this work the notion of a generalized mixture and propose some methods for estimatin...
Abstract—A new Bayesian model is proposed for image seg-mentation based upon Gaussian mixture models...
In computer vision, image segmentation plays foundational role. Innumerable techniques, such as acti...
This paper considers three crucial issues in processing scaled down image, the representation of par...
Spatially varying mixture models are characterized by the dependence of their mixing proportions on ...
We establish a mixture model with “spurious ” outliers and derive its maxi-mum likelihood estimator,...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority v...
Finite mixture model (FMM) is being increasingly used for unsupervised image segmentation. In this p...