Results 1  10
of
39
Snakes, Shapes, and Gradient Vector Flow
 IEEE TRANSACTIONS ON IMAGE PROCESSING
, 1998
"... Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new extern ..."
Abstract

Cited by 744 (16 self)
 Add to MetaCart
Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a graylevel or binary edge map derived from the image. It differs fundamentally from traditional snake external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding snake is formulated directly from a force balance condition rather than a variational formulation. Using several twodimensional (2D) examples and one threedimensional (3D) example, we show that GVF has a large capture range and is able to move snakes into boundary concavities.
Generalized Gradient Vector Flow External Forces for Active Contours
 Signal Processing
, 1998
"... Active contours, or snakes, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. A new type of external force for active contours, called gradient vector flow (GVF) was introduced recently to address problems associated with initializat ..."
Abstract

Cited by 144 (6 self)
 Add to MetaCart
(Show Context)
Active contours, or snakes, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. A new type of external force for active contours, called gradient vector flow (GVF) was introduced recently to address problems associated with initialization and poor convergence to boundary concavities. GVF is computed as a di#usion of the gradient vectors of a graylevel or binary edge map derived from the image. In this paper, we generalize the GVF formulation to include two spatially varying weighting functions. This improves active contour convergence to long, thin boundary indentations, while maintaining other desirable properties of GVF, such as an extended capture range. The original GVF is a special case of this new generalized GVF (GGVF) model. An error analysis for active contour results on simulated test images is also presented. # 1998 Elsevier Science B.V. All rights reserved. Zusammenfassung Aktive Umrisse, oder Schlangen, we...
Gradient Vector Flow Deformable Models
, 2000
"... Deformable models are used extensively in image processing, computer vision, and medical imaging applications, particularly to delineate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This chapter presents ..."
Abstract

Cited by 20 (0 self)
 Add to MetaCart
Deformable models are used extensively in image processing, computer vision, and medical imaging applications, particularly to delineate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This chapter presents an external force for deformable models, largely solving both problems. This external force, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a graylevel or binary edge map derived from the image. It differs fundamentally from traditional deformable model external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding deformable model is formulated directly from a dynamic force equation rather than a energy minimization formulation. Using several twodimensional examples and two threedimensional examples, we show that GVF has a large capture range and is able to move deformable models into ...
Deformable models with application to human cerebral cortex reconstruction in magnetic resonance images
, 2000
"... Constructing a mathematical representation of an object boundary (boundary mapping) from images is an important problem that is of importance to several active research areas such as image analysis, computer vision, and medical imaging. The focus of this dissertation is to investigate deformable mo ..."
Abstract

Cited by 20 (2 self)
 Add to MetaCart
(Show Context)
Constructing a mathematical representation of an object boundary (boundary mapping) from images is an important problem that is of importance to several active research areas such as image analysis, computer vision, and medical imaging. The focus of this dissertation is to investigate deformable models, a boundary mapping technique that incorporates both image information and prior knowledge about the boundary geometry to extract a meaningful boundary description. A key problem with methods reported in the literature is that they have diÆculties in reliably mapping boundaries when the models are not initialized near target boundaries or are applied to reconstruct boundaries with concavities. In this research, we make three main contributions to the area of boundary mapping. First, we developed a method called the gradient vector
ow deformable model that is robust to both model initialization and boundary concavities. Second, we developed a generalization of the rst method that allows for improved performance in converging to narrow boundary indentations and greater accuracy in localizing boundaries. Third, we developed a method for reconstructing the central layer of the human cerebral cortex from magnetic resonance images that uses our proposed deformable model as a core component. Our methods are validated on both simulated images and real magnetic resonance images. This thesis is prepared under the direction of Dr. Jerry L. Prince. ii
Efficient Unsupervised ContentBased Segmentation In Stereoscopic Video Sequences
, 1999
"... This paper presents an e#cient technique for unsupervised contentbased segmentation in stereoscopic video sequences by appropriately combined di#erent content descriptors in a hierarchical framework. Three main modules are involved in the proposed scheme; extraction of reliable depth information ..."
Abstract

Cited by 13 (11 self)
 Add to MetaCart
This paper presents an e#cient technique for unsupervised contentbased segmentation in stereoscopic video sequences by appropriately combined di#erent content descriptors in a hierarchical framework. Three main modules are involved in the proposed scheme; extraction of reliable depth information, image partition into color and depth regions and a constrained fusion algorithm of color segments using information derived from the depth map. In the #rst module, each stereo pair is analyzed and the disparity #eld and depth map are estimated. Occlusion detection and compensation are also applied for improving the depth map estimation. In the following phase, color and depth regions are created using a novel complexityreducingmultiresolution implementation of the Recursive Shortest Spanning Tree algorithm #MRSST#. While depth segments provide a coarse representation of the image content, color regions describe very accurately object boundaries. For this reason, in the #nal phase, a new segmentation fusion algorithm is employed which projects color segments onto depth segments. Experimental results are presented which exhibit the e#ciency of the proposed scheme as contentbased descriptor, even in case of images with complicated visual content
Normalized Gradient Vector Diffusion and Image Segmentation
 In Proceedings of European Conference on Computer Vision
, 2002
"... In this paper, we present an approach for image segmentation, based on the existing Active Snake Model and Watershedbased Region Merging. Our algorithm includes initial segmentation using Normalized Gradient Vector Diffusion ( NG VD) and region merging based on Region Adjacency Graph (RAG). We ..."
Abstract

Cited by 13 (2 self)
 Add to MetaCart
In this paper, we present an approach for image segmentation, based on the existing Active Snake Model and Watershedbased Region Merging. Our algorithm includes initial segmentation using Normalized Gradient Vector Diffusion ( NG VD) and region merging based on Region Adjacency Graph (RAG). We use a set of heat diffusion equations to generate a vector field over the image domain, which provides us with a natural way to define seeds as well as an external force to attract the active snakes. Then an initial segmentation of the original image can be obtained by a similar idea as seen in active snake model. Finally an RAGbased region merging technique is used to find the true segmentation as desired. The experimental results show that our NGVDbased region merging algorithm overcomes some problems as seen in classic active snake model. We will also see that our NGVD has several advantages over the traditional gradient vector diffusion.
Object Classification in 3D Images Using AlphaTrimmed Radial Basis Function Network Mean
, 1999
"... We propose a pattern classification based approach for simultaneous 3D object modeling and segmentation in image volumes. The 3D objects are described as a set of overlapping ellipsoids. ..."
Abstract

Cited by 10 (2 self)
 Add to MetaCart
We propose a pattern classification based approach for simultaneous 3D object modeling and segmentation in image volumes. The 3D objects are described as a set of overlapping ellipsoids.
Adaptive Snakes Using the EM Algorithm
"... Abstract—Deformable models (e.g., snakes) perform poorly in many image analysis problems. The contour model is attracted by edge points detected in the image. However, many edge points do not belong to the object contour, preventing the active contour from converging toward the object boundary. A ne ..."
Abstract

Cited by 6 (3 self)
 Add to MetaCart
(Show Context)
Abstract—Deformable models (e.g., snakes) perform poorly in many image analysis problems. The contour model is attracted by edge points detected in the image. However, many edge points do not belong to the object contour, preventing the active contour from converging toward the object boundary. A new algorithm is proposed in this paper to overcome this difficulty. The algorithm is based on two key ideas. First, edge points are associated in strokes. Second, each stroke is classified as valid (inlier) or invalid (outlier) and a confidence degree is associated to each stroke. The expectation maximization algorithm is used to update the confidence degrees and to estimate the object contour. It is shown that this is equivalent to the use of an adaptive potential function which varies during the optimization process. Valid strokes receive high confidence degrees while confidence degrees of invalid strokes tend to zero during the optimization process. Experimental results are presented to illustrate the performance of the proposed algorithm in the presence of clutter, showing a remarkable robustness. Index Terms—Adaptive potential, contour estimation, deformable models, expectation maximization (EM) algorithm, robust estimation, snakes. I.
Angular mapdriven snakes with application to object shape description in color images
 IEEE Trans. Image Proc
, 2001
"... Abstract—We propose a method for shape description of objects in color images. Our method employs angular maps in order to identify significant changes of color within the image, which are then used to drive snake models. To obtain an angular map, the angle values of the vectors corresponding to col ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
(Show Context)
Abstract—We propose a method for shape description of objects in color images. Our method employs angular maps in order to identify significant changes of color within the image, which are then used to drive snake models. To obtain an angular map, the angle values of the vectors corresponding to color image pixels are first computed with respect to a reference vector, and organized in a twodimensional matrix. To identify significant color changes within the original image, the edges of the angular map are next extracted. The resulting edge map is then presented to a snake model. Distance and gradient vector flow snake models have been employed in this work. Experimental results show, not only that the resulting object shape descriptions are accurate and quite similar, but also that our method is computationally efficient and flexible. Index Terms—Angular color map, gradient vector flow, object shape description, snakes. I.
A Method for Dynamic Clustering of Data
, 1998
"... This paper describes a method for the segmentation of dynamic data. It extends well known algorithms developed in the context of static clustering (e.g., the cmeans algorithm, Kohonen maps, elastic nets and fuzzy cmeans). The work is based on an unified framework for constrained clustering rece ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
This paper describes a method for the segmentation of dynamic data. It extends well known algorithms developed in the context of static clustering (e.g., the cmeans algorithm, Kohonen maps, elastic nets and fuzzy cmeans). The work is based on an unified framework for constrained clustering recently proposed by the authors in [1]. This framework is extended by using a motion model for the clusters which includes global and local evolution of the data centroids. A noise model is also proposed to increase the robustness of the dynamic clustering algorithm with respect to outliers. 1 Introduction Many clustering algorithms try to approximate a set of data vectors # ### # # #### ## # by a smaller set of prototypes # ### # # #### ## #. The main problem consists of computing the number of prototypes and their locations in order to obtain the best fit, according to a specific criterion. This paper addresses dynamic clustering problems in which data is gathered during a time interva...