Results 1 - 10 of 899,356

Multi-class image segmentation using Conditional Random Fields and Global Classification

by Nils Plath, Marc Toussaint, Shinichi Nakajima
"... A key aspect of semantic image segmentation is to integrate local and global features for the prediction of local segment labels. We present an approach to multi-class segmentation which combines two methods for this integration: a Conditional Random Field (CRF) which couples to local image features ..."
Abstract - Cited by 19 (0 self) - Add to MetaCart
A key aspect of semantic image segmentation is to integrate local and global features for the prediction of local segment labels. We present an approach to multi-class segmentation which combines two methods for this integration: a Conditional Random Field (CRF) which couples to local image

Multi-Class Segmentation with Relative Location Prior

by Stephen Gould, Jim Rodgers, David Cohen, Gal Elidan, Daphne Koller - INTERNATIONAL JOURNAL OF COMPUTER VISION , 2008
"... Multi-class image segmentation has made significant advances in recent years through the combination of local and global features. One important type of global feature is that of inter-class spatial relationships. For example, identifying “tree” pixels indicates that pixels above and to the sides ar ..."
Abstract - Cited by 101 (4 self) - Add to MetaCart
Multi-class image segmentation has made significant advances in recent years through the combination of local and global features. One important type of global feature is that of inter-class spatial relationships. For example, identifying “tree” pixels indicates that pixels above and to the sides

Background

by unknown authors , 2008
"... To print higher-resolution math symbols, click the Hi-Res Fonts for Printing button on the jsMath control panel. How to train and run multi-class image segmentation ..."
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To print higher-resolution math symbols, click the Hi-Res Fonts for Printing button on the jsMath control panel. How to train and run multi-class image segmentation

TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-Class Object . . .

by J. Shotton, J. Winn, C. Rother, A. Criminisi - IN ECCV , 2006
"... This paper proposes a new approach to learning a discriminative model of object classes, incorporating appearance, shape and context information efficiently. The learned model is used for automatic visual recognition and semantic segmentation of photographs. Our discriminative model exploits nov ..."
Abstract - Cited by 427 (19 self) - Add to MetaCart
novel features, based on textons, which jointly model shape and texture. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating

Normalized Cuts and Image Segmentation

by Jianbo Shi, Jitendra Malik - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2000
"... ..."
Abstract - Cited by 3764 (48 self) - Add to MetaCart
Abstract not found

CONTEXT-BASED GLOBAL MULTI-CLASS SEMANTIC SEGMENTATION OF IMAGES INSPIRED BY THE HUMAN VISUAL SYSTEM

by Na Fan
"... Semantic scene understanding is one of the several significant goals of robotics. In this paper, we propose a framework that is able to simultaneously detect and segment objects of different classes using a simple pairwise interactive context term, for the sake of achieving a preliminary milestone o ..."
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of Semantic scene understanding. The context is incorporated as pairwise interactions between pixels, imposing a prior on the labeling. Our model formulates the multi-class image segmentation task as an energy minimization problem and finds a globally optimal solution using belief propagation and neural

Efficient Graph-Based Image Segmentation

by Pedro F. Felzenszwalb, Daniel P. Huttenlocher
"... This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show that althou ..."
Abstract - Cited by 931 (1 self) - Add to MetaCart
This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show

SUPERVISED AND TRANSDUCTIVE MULTI-CLASS SEGMENTATION USING p-LAPLACIANS AND RKHS METHODS

by S. H. Kang, B. Shafei, G. Steidl
"... Abstract. This paper considers supervised multi-class image segmentation: from a labeled set of pixels in one image, we learn the segmentation and apply it to the rest of the image or to other sim-ilar images. We study approaches with p-Laplacians, (vector-valued) Reproducing Kernel Hilbert Spaces ( ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Abstract. This paper considers supervised multi-class image segmentation: from a labeled set of pixels in one image, we learn the segmentation and apply it to the rest of the image or to other sim-ilar images. We study approaches with p-Laplacians, (vector-valued) Reproducing Kernel Hilbert Spaces

Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

by Yongyue Zhang, Michael Brady, Stephen Smith - IEEE TRANSACTIONS ON MEDICAL. IMAGING , 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limi ..."
Abstract - Cited by 619 (14 self) - Add to MetaCart
The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic

Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multi-band Image Segmentation

by Song Chun Zhu, Alan Yuille - IEEE Transactions on Pattern Analysis and Machine Intelligence , 1996
"... We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum and c ..."
Abstract - Cited by 778 (21 self) - Add to MetaCart
We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum
Results 1 - 10 of 899,356