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73
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 ..."
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Cited by 144 (6 self)
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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...
Autonomous Exploration: Driven by Uncertainty
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1995
"... Passively accepting measurements of the world is not enough, as the data we obtain is always incomplete, and the inferences made from it uncertain to a degree which is often unacceptable. If we are to build machines that operate autonomously they will always be faced with this dilemma, and can only ..."
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Cited by 105 (8 self)
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Passively accepting measurements of the world is not enough, as the data we obtain is always incomplete, and the inferences made from it uncertain to a degree which is often unacceptable. If we are to build machines that operate autonomously they will always be faced with this dilemma, and can only be successful if they play a much more active role. This paper presents such a machine. It deliberately seeks out those parts of the world which maximize the fidelity of its internal representations, and keeps searching until those representations are acceptable. We call this paradigm autonomous exploration, and the machine an autonomous explorer. This paper has two major contributions. The first is a theory that tells us how to explore, and which confirms the intuitive ideas we have put forward previously. The second is an implementation of that theory. In our laboratory we have constructed a working autonomous explorer and here for the first time show it in action. The system is entirely bottomup and does not depend on any a priori knowledge of the environment. To our knowledge it is the first to have successfully closed the loop between gaze planning and the inference of complex 3D models.
Superquadrics for Segmenting and Modeling Range Data
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1997
"... We present a novel approach to reliable and efficient recovery of partdescriptions in terms of superquadric models from range data. We show that superquadrics can directly be recovered from unsegmented data, thus avoiding any presegmentation steps (e.g., in terms of surfaces). The approach is b ..."
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Cited by 67 (4 self)
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We present a novel approach to reliable and efficient recovery of partdescriptions in terms of superquadric models from range data. We show that superquadrics can directly be recovered from unsegmented data, thus avoiding any presegmentation steps (e.g., in terms of surfaces). The approach is based on the recoverandselect paradigm [10]. We present several experiments on real and synthetic range images, where we demonstrate the stability of the results with respect to viewpoint and noise.
Representation and Recognition of FreeForm Surfaces
, 1992
"... We introduce a new surface representation for recognizing curved objects. Our approach begins by representing an object by a discrete mesh of points built from range data or from a geometric model of the object. The mesh is computed from the data by deforming a standard shaped mesh, for example, an ..."
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Cited by 62 (7 self)
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We introduce a new surface representation for recognizing curved objects. Our approach begins by representing an object by a discrete mesh of points built from range data or from a geometric model of the object. The mesh is computed from the data by deforming a standard shaped mesh, for example, an ellipsoid, until it fits the surface of the object. We define local regularity constraints that the mesh must satisfy. We then define a canonical mapping between the mesh describing the object and a standard spherical mesh. A surface curvature index that is poseinvariant is stored at every node of the mesh. We use this object representation for recognition by comparing the spherical model of a reference object with the model extracted from a new observed scene. We show how the similarity between reference model and observed data can be evaluated and we show how the pose of the reference object in the observed scene can be easily computed using this representation. We present results on real range images which show that this approach to modelling and recognizing threedimensional objects has three main advantages: First, it is applicable to complex curved surfaces that cannot be handled by conventional techniques. Second, it reduces the recognition problem to the computation of similarity between spherical distributions; in particular, the recognition algorithm does not require any combinatorial search. Finally, even though it is based on a spherical mapping, the approach can handle occlusions and partial views.
Generic model abstraction from examples
 IEEE Trans. on Pattern Analysis and Machine Intelligence
"... The recognition community has long avoided bridging the representational gap between traditional, lowlevel image features and generic models. Instead, the gap has been artificially eliminated by either bringing the image closer to the models, using simple scenes containing idealized, textureless ob ..."
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Cited by 58 (8 self)
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The recognition community has long avoided bridging the representational gap between traditional, lowlevel image features and generic models. Instead, the gap has been artificially eliminated by either bringing the image closer to the models, using simple scenes containing idealized, textureless objects, or by bringing the models closer to the images, using 3D CAD model templates or 2D appearance model templates. In this paper, we attempt to bridge the representational gap for the domain of model acquisition. Specifically, we address the problem of automatically acquiring a generic 2D viewbased class model from a set of images, each containing an exemplar object belonging to that class. We introduce a novel graphtheoretical formulation of the problem, and demonstrate the approach on real imagery.
A parametric deformable model to fit unstructured 3D data
, 1995
"... Recovery of unstructured 3D data with deformable models has been the subject of many studies over the last ten years. In particular, in medical image understanding, deformable models are useful to get a precise representation of anatomical structures. However, general deformable models involve large ..."
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Cited by 53 (1 self)
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Recovery of unstructured 3D data with deformable models has been the subject of many studies over the last ten years. In particular, in medical image understanding, deformable models are useful to get a precise representation of anatomical structures. However, general deformable models involve large linear systems to solve when dealing with high resolution 3D images. The advantage of parametric deformable models like superquadrics is their small number of parameters to describe a shape combined with a better robustness in the presence of noise or sparse data. Also, at the expense of a reasonable number of additional parameters, free form deformations provide a much closer fit and a volumetric deformation field. This article introduces such a model to fit unstructured 3D points with a parametric deformable surface based on a superquadric fit followed by a free form deformation to describe the cardiac left ventricle. We present the mathematical and algorithmic details of the method, as wel...
3D Part Segmentation Using Simulated Electrical Charge Distributions
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1996
"... A novel approach to 3D part segmentation is presented It is a wellknown physical fact that electrical charge on the surface of a conductor tends to accumulate at a sharp convexity and vanish at a sharp concavity. Thus object part boundaries, which are usually denoted by a sharp surface concavity, c ..."
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Cited by 45 (0 self)
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A novel approach to 3D part segmentation is presented It is a wellknown physical fact that electrical charge on the surface of a conductor tends to accumulate at a sharp convexity and vanish at a sharp concavity. Thus object part boundaries, which are usually denoted by a sharp surface concavity, can be detected by locating surface points exhibiting local charge density minima. Beginning with single or multiview range data of a 3D object, we simulate the charge density distribution over an object's surface which has been tessellated by a triangular mesh. We detect the deep surface concavities by tracing local charge density minima and then decompose the object into parts at these points. The charge density computation does not require an assumption on surface smoothness and uses weighted global data to produce robust local surface features for part segmentation.
Normal Vector Voting: Crease Detection and Curvature Estimation on Large, Noisy Meshes
, 2002
"... ..."
The role of modelbased segmentation in the recovery of volumetric parts from range data
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... We present a method for segmenting and estimating the shape of 3D objects from range data. The technique uses model views, or aspects, to constrain the fitting of deformable models to range data. Based on an initial region segmentation of a range image, regions are grouped into aspects corresponding ..."
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Cited by 27 (4 self)
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We present a method for segmenting and estimating the shape of 3D objects from range data. The technique uses model views, or aspects, to constrain the fitting of deformable models to range data. Based on an initial region segmentation of a range image, regions are grouped into aspects corresponding to the volumetric parts that make up an object. The qualitative segmentation of the range image into a set of volumetric parts not only captures the coarse shape of the parts, but qualitatively encodes the orientation of each part through its aspect. Knowledge of a partâ€™s coarse shape, its orientation, as well as the mapping between the faces in its aspect and the surfaces on the part provides strong constraints on the fitting of a deformable model (supporting both global and local deformations) to the data. Unlike previous work in physicsbased deformable model recovery from range data, the technique does not require presegmented data. Furthermore, occlusion is handled at segmentation time and does not complicate the fitting process, as only 3D points known to belong to a part participate in the fitting of a model to the part. We present the approach in detail and apply it to the recovery of objects from range data.
Fitting of IsoSurfaces Using Superquadrics and FreeForm Deformations
 IN PROCEEDINGS IEEE WORKSHOP ON BIOMEDICAL IMAGE ANALYSIS (WBIA
, 1994
"... Recovery of 3D data with simple parametric models has been the subject of many studies over the last ten years. Many have used the notion of superquadrics introduced for graphics in [Bar81]. Different improvements were introduced to make the model a better representation of the data [BG87, FLW89, S ..."
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Cited by 26 (8 self)
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Recovery of 3D data with simple parametric models has been the subject of many studies over the last ten years. Many have used the notion of superquadrics introduced for graphics in [Bar81]. Different improvements were introduced to make the model a better representation of the data [BG87, FLW89, SB90, TM91]. This paper describes a twosteps method to fit a parametric deformable surface to 3D points. We suppose that a 3D image has been segmented to get a set of 3D points. The first step consists in our version of a superquadric fit with global tapering, similar to the method proposed in [BG87]. We then make use of the technique of freeform deformations, as introduced by [SP86] in computer graphics. We present experimental results with synthetic and real 3D medical images.