Results 1  10
of
68
ModelBased Recognition in Robot Vision
 ACM Computing Surveys
, 1986
"... This paper presents a comparative study and survey of modelbased objectrecognition algorithms for robot vision. The goal of these algorithms is to recognize the identity, position, and orientation of randomly oriented industrial parts. In one form this is commonly referred to as the “binpicking ” ..."
Abstract

Cited by 176 (0 self)
 Add to MetaCart
This paper presents a comparative study and survey of modelbased objectrecognition algorithms for robot vision. The goal of these algorithms is to recognize the identity, position, and orientation of randomly oriented industrial parts. In one form this is commonly referred to as the “binpicking ” problem, in which the parts to be recognized are presented in a jumbled bin. The paper is organized according to 2D, 2&D, and 3D object representations, which are used as the basis for the recognition algorithms. Three
R.T.Whitaker, “Partitioning 3D Surface Meshes Using Watershed Segmentation
 IEEE Transactions on Visualization and Computer Graphics
, 1999
"... AbstractÐThis paper describes a method for partitioning 3D surface meshes into useful segments. The proposed method generalizes morphological watersheds, an image segmentation technique, to 3D surfaces. This surface segmentation uses the total curvature of the surface as an indication of region boun ..."
Abstract

Cited by 148 (1 self)
 Add to MetaCart
AbstractÐThis paper describes a method for partitioning 3D surface meshes into useful segments. The proposed method generalizes morphological watersheds, an image segmentation technique, to 3D surfaces. This surface segmentation uses the total curvature of the surface as an indication of region boundaries. The surface is segmented into patches, where each patch has a relatively consistent curvature throughout, and is bounded by areas of higher, or drastically different, curvature. This algorithm has applications for a variety of important problems in visualization and geometrical modeling including 3D feature extraction, mesh reduction, texture mapping 3D surfaces, and computer aided design. Index TermsÐSurfaces, surface segmentation, watershed algorithm, curvaturebased methods. æ 1
Meshless deformations based on shape matching
 ACM TRANS. GRAPH
, 2005
"... We present a new approach for simulating deformable objects. The underlying model is geometrically motivated. It handles pointbased objects and does not need connectivity information. The approach does not require any preprocessing, is simple to compute, and provides unconditionally stable dynamic ..."
Abstract

Cited by 129 (11 self)
 Add to MetaCart
We present a new approach for simulating deformable objects. The underlying model is geometrically motivated. It handles pointbased objects and does not need connectivity information. The approach does not require any preprocessing, is simple to compute, and provides unconditionally stable dynamic simulations. The main idea of our deformable model is to replace energies by geometric constraints and forces by distances of current positions to goal positions. These goal positions are determined via a generalized shape matching of an undeformed rest state with the current deformed state of the point cloud. Since points are always drawn towards welldefined locations, the overshooting problem of explicit integration schemes is eliminated. The versatility of the approach in terms of object representations that can be handled, the efficiency in terms of memory and computational complexity, and the unconditional stability of the dynamic simulation make the approach particularly interesting for games.
Expressioninvariant 3D face recognition
, 2003
"... We present a novel 3D face recognition approach based on geometric invariants introduced by Elad and Kimmel. The key idea of the proposed algorithm is a representation of the facial surface, invariant to isometric deformations, such as those resulting from different expressions and postures of the ..."
Abstract

Cited by 92 (17 self)
 Add to MetaCart
(Show Context)
We present a novel 3D face recognition approach based on geometric invariants introduced by Elad and Kimmel. The key idea of the proposed algorithm is a representation of the facial surface, invariant to isometric deformations, such as those resulting from different expressions and postures of the face. The obtained geometric invariants allow mapping 2D facial texture images into special images that incorporate the 3D geometry of the face. These signature images are then decomposed into their principal components. The result is an efficient and accurate face recognition algorithm that is robust to facial expressions. We demonstrate the results of our method and compare it to existing 2D and 3D face recognition algorithms.
Finding Surface Correspondence for Object Recognition and Registration using Pairwise Geometric Histograms
 in Computer VisionECCV'98
"... . Pairwise geometric histograms have been demonstrated as an effective descriptor of arbitrary 2dimensional shape which enable robust and efficient object recognition in complex scenes. In this paper we describe how the approach can be extended to allow the representation and classification of arbi ..."
Abstract

Cited by 44 (2 self)
 Add to MetaCart
(Show Context)
. Pairwise geometric histograms have been demonstrated as an effective descriptor of arbitrary 2dimensional shape which enable robust and efficient object recognition in complex scenes. In this paper we describe how the approach can be extended to allow the representation and classification of arbitrary 2 1 2  and 3dimensional surface shape. This novel representation can be used in important vision tasks such as the recognition of objects with complex freeform surfaces and the registration of surfaces for building 3dimensional models from multiple views. We apply this new representation to both of these tasks and present some promising results. 1 Introduction Finding a correspondence between two or more surfaces is a frequently encountered problem in many computer vision tasks. When surface based descriptions are used for object recognition, the hypothesis that a particular object is in a scene is confirmed by finding a good correspondence between scene and model surfaces [6]. W...
3D motion estimation, understanding and prediction from noisy image sequences
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 1987
"... AbstractThis paper presents an approach to understanding general 3D motion of a rigid body from image sequences. Based on dynamics, a locally constant angular momentum (LCAM) model is introduced. The model is local in the sense that it is applied to a limited number of image frames at a time. Spec ..."
Abstract

Cited by 41 (2 self)
 Add to MetaCart
(Show Context)
AbstractThis paper presents an approach to understanding general 3D motion of a rigid body from image sequences. Based on dynamics, a locally constant angular momentum (LCAM) model is introduced. The model is local in the sense that it is applied to a limited number of image frames at a time. Specifically, the model constrains the motion, over a local frame subsequence, to be a superposition of precession and translation. Thus, the instantaneous rotation axis of the object is allowed to change through the subsequence. The trajectory of the rotation center is approximated by a vector polynomial. The parameters of the model evolve in time so that they can adapt to long term changes in motion characteristics. The nature and parameters of short term motion can be estimated continuously with the goal of understanding motion through the image sequence. The estimation algorithm presented in this paper is linear, i.e., the algorithm consists of solving simultaneous linear equations. Based on the assumption that the motion is smooth, object positions and motion in the near future can be predicted, and short missing subsequences can be recovered. Noise smoothing is achieved by overdetermination and a leastsquares criterion. The framework is flexible in the sense that it allows both overdetermination in number of feature points and the number of image frames. The number of frames from which the model is derived can be varied according to the complexity of motion and the noise level so as to obtain stable and good estimates of parameters over the entire image sequence. Simulation results are given for noisy synthetic data and images taken of a model airplane. Index TermsComputer vision, dynamic model, image sequence analysis, motion, motion estimation, motion prediction, motion understanding. I.
From Surfaces to Objects: Computer Vision and ThreeDimensional Scene Analysis
, 1989
"... This book was originally published by John Wiley and Sons, ..."
Abstract

Cited by 33 (10 self)
 Add to MetaCart
This book was originally published by John Wiley and Sons,
Highlevel CAD model acquisition from range image
 ComputerAided Design
, 1997
"... Automatic extraction of CAD descriptions which are ultimately intended for human manipulation requires the accurate inference of geometric and topological information. We present a system which applies segmentation techniques from computer vision to automatically extract CADmodels from range images ..."
Abstract

Cited by 33 (6 self)
 Add to MetaCart
Automatic extraction of CAD descriptions which are ultimately intended for human manipulation requires the accurate inference of geometric and topological information. We present a system which applies segmentation techniques from computer vision to automatically extract CADmodels from range images of parts with curved surfaces. The output of the system is a Brep of the object which is suitable for further manipulation in a modelling system. The segmentation process is an improvement upon Besl and Jain's variableorder surface tting 1 extracting general quadric surfaces and planes from the data, with a postprocessing stage to identify surface intersections and to extract a Brep from the segmented image. We present results on a variety of machined objects, which illustrate the highlevel nature of the acquired models, and discuss the numerical accuracy (feature sizes and separations) and the correctness of structural inferences of the system.
Partial Surface and Volume Matching in Three Dimensions
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... In this paper we present a new technique for partial surface and volume matching of images in three dimensions. In this ..."
Abstract

Cited by 30 (1 self)
 Add to MetaCart
In this paper we present a new technique for partial surface and volume matching of images in three dimensions. In this
The Combinatorics Of Local Constraints In ModelBased Recognition And Localization From Sparse Data
, 1986
"... The problem of recognizing what objects are where in the workspace of a robot can be cast as one of searching for a consistent matching between sensory data elements and equivalent model elements. In principle, this search space is enormous and to control the potential combinatorial explosion, const ..."
Abstract

Cited by 27 (3 self)
 Add to MetaCart
The problem of recognizing what objects are where in the workspace of a robot can be cast as one of searching for a consistent matching between sensory data elements and equivalent model elements. In principle, this search space is enormous and to control the potential combinatorial explosion, constraints between the data and model elements are needed. We derive a set of constraints for sparse sensory data that are applicable to a wide variety of sensors and examine their characteristics. We then use known bounds on the complexity of constraint satisfaction problems together with explicit estimates of the effectiveness of the constraints derived for the case of sparse, noisy threedimensional sensory data to obtain general theoretical bounds on the number of interpretations expected to be consistent with the data. We show that these bounds are consistent with empirical results reported previously. The results are used to demonstrate the graceful degradation of the recognition technique with the presence of noise in the data, and to predict the number of data points needed in general to uniquely determine the object being sensed.