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Recovering 3D Shape and Motion from Image Streams using NonLinear Least Squares
, 1993
"... The simultaneous recovery of 3D shape and motion from image sequences is one of the more difficult problems in computer vision. Classical approaches to the problem rely on using algebraic techniques to solve for these unknowns given two or more images. More recently, a batch analysis of image stream ..."
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Cited by 198 (32 self)
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The simultaneous recovery of 3D shape and motion from image sequences is one of the more difficult problems in computer vision. Classical approaches to the problem rely on using algebraic techniques to solve for these unknowns given two or more images. More recently, a batch analysis of image streams (the temporal tracks of distinguishable image features) under orthography has resulted in highly accurate reconstructions. We generalize this approach to perspective projection and partial or uncertain tracks by using a nonlinear least squares technique. While our approach requires iteration, it quickly converges to the desired solution, even in the absence of a priori knowledge about the shape or motion. Important features of the algorithm include its ability to handle partial point tracks, to use line segment matches and point matches simultaneously, and to use an objectcentered representation for faster and more accurate structure and motion recovery. We also show how a projective (a...
Probability Distributions of Optical Flow
 PROC. CONF. COMP. VISION AND PATT. RECOGNITION
, 1991
"... Gradient methods are widely used in the computation of optical flow. We discuss extensions of these methods which compute probability distributions of optical flow. The use of distributions allows representation of the uncertainties inherent in the optical flow computation, facilitating the combinat ..."
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Cited by 186 (16 self)
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Gradient methods are widely used in the computation of optical flow. We discuss extensions of these methods which compute probability distributions of optical flow. The use of distributions allows representation of the uncertainties inherent in the optical flow computation, facilitating the combination with information from other sources. We compute distributed optical flow for a synthetic image sequence and demonstrate that the probabilistic model accounts for the errors in the flow estimates. We also compute distributed optical flow for a real image sequence. 1 Introduction The recovery of motion information from visual input is an important task for both natural and artificial vision systems. Most models for the analysis of visual motion begin by extracting twodimensional motion information. In particular, computer vision techniques typically compute twodimensional optical flowvectors which describe the motion of each portion of the image in the image plane. Methods for the re...
Motion Segmentation and Tracking Using Normalized Cuts
, 1998
"... We propose a motion segmentation algorithm that aims to break a scene into its most prominent moving groups. A weighted graph is constructed on the ira. age sequence by connecting pixels that arc in the spatiotemporal neighborhood of each other. At each pizel, we define motion profile vectors which ..."
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Cited by 155 (6 self)
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We propose a motion segmentation algorithm that aims to break a scene into its most prominent moving groups. A weighted graph is constructed on the ira. age sequence by connecting pixels that arc in the spatiotemporal neighborhood of each other. At each pizel, we define motion profile vectors which capture the probability distribution of the image veloczty. The distance between motion profiles is used to assign a weight on the graph edges. 5rsmg normalized cuts we find the most salient partitions of the spatiotemporaI graph formed by the image sequence. For swmenting long image sequences,' we have developed a recursire update procedure that incorporates knowledge of segmentation in previous frames for efficiently finding the group correspondence in the new frame.
Toward an AffectSensitive Multimodal HumanComputer Interaction
 Proceedings of the IEEE
, 2003
"... The ability to recognize affective states of a person... This paper argues that nextgeneration humancomputer interaction (HCI) designs need to include the essence of emotional intelligence  the ability to recognize a user's affective states  in order to become more humanlike, more effect ..."
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Cited by 154 (29 self)
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The ability to recognize affective states of a person... This paper argues that nextgeneration humancomputer interaction (HCI) designs need to include the essence of emotional intelligence  the ability to recognize a user's affective states  in order to become more humanlike, more effective, and more efficient. Affective arousal modulates all nonverbal communicative cues (facial expressions, body movements, and vocal and physiological reactions). In a facetoface interaction, humans detect and interpret those interactive signals of their communicator with little or no effort. Yet design and development of an automated system that accomplishes these tasks is rather difficult. This paper surveys the past work in solving these problems by a computer and provides a set of recommendations for developing the first part of an intelligent multimodal HCI  an automatic personalized analyzer of a user's nonverbal affective feedback.
Splinebased image registration
 IN PROC. IEEE CONFERENCE ON COMPUTER VISION PATTERN RECOGNITION
, 1994
"... ..."
A duality based approach for realtime tvl1 optical flow
 In Ann. Symp. German Association Patt. Recogn
, 2007
"... Abstract. Variational methods are among the most successful approaches to calculate the optical flow between two image frames. A particularly appealing formulation is based on total variation (TV) regularization and the robust L 1 norm in the data fidelity term. This formulation can preserve discont ..."
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Cited by 128 (16 self)
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Abstract. Variational methods are among the most successful approaches to calculate the optical flow between two image frames. A particularly appealing formulation is based on total variation (TV) regularization and the robust L 1 norm in the data fidelity term. This formulation can preserve discontinuities in the flow field and offers an increased robustness against illumination changes, occlusions and noise. In this work we present a novel approach to solve the TVL 1 formulation. Our method results in a very efficient numerical scheme, which is based on a dual formulation of the TV energy and employs an efficient pointwise thresholding step. Additionally, our approach can be accelerated by modern graphics processing units. We demonstrate the realtime performance (30 fps) of our approach for video inputs at a resolution of 320 × 240 pixels. 1
MotionBased Background Subtraction Using Adaptive Kernel Density Estimation
, 2004
"... Background modeling is an important component of many vision systems. Existing work in the area has mostly addressed scenes that consist of static or quasistatic structures. When the scene exhibits a persistent dynamic behavior in time, such an assumption is violated and detection performance deter ..."
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Cited by 120 (1 self)
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Background modeling is an important component of many vision systems. Existing work in the area has mostly addressed scenes that consist of static or quasistatic structures. When the scene exhibits a persistent dynamic behavior in time, such an assumption is violated and detection performance deteriorates. In this paper, we propose a new method for the modeling and subtraction of such scenes. Towards the modeling of the dynamic characteristics, optical flow is computed and utilized as a feature in a higher dimensional space. Inherent ambiguities in the computation of features are addressed by using a datadependent bandwidth for density estimation using kernels. Extensive experiments demonstrate the utility and performance of the proposed approach.
Robust dynamic motion estimation over time
 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
, 1991
"... This paper presents a novel approach to incrementally estimating visual motion over a sequence of images. We start by formulating constraints on image motion to account for the possibility of multiple motions. This is achieved by exploiting the notions of weak continuity and robust statistics in the ..."
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Cited by 111 (10 self)
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This paper presents a novel approach to incrementally estimating visual motion over a sequence of images. We start by formulating constraints on image motion to account for the possibility of multiple motions. This is achieved by exploiting the notions of weak continuity and robust statistics in the formulation of a minimization problem. The resulting objective function is non{convex. Traditional stochastic relaxation techniques for minimizing such functions prove inappropriate for the task. We present a highly parallel incremental stochastic minimization algorithm which has a number of advantages over previous approaches. The incremental nature of the scheme makes it truly dynamic and permits the detection of occlusion and disocclusion boundaries. 1
Realtime tracking of image regions with changes in geometry and illumination
, 1996
"... Historically, SSD or correlationbased visual tracking algorithms have been sensitive to changes in illumination and shading across the target region. This paper describes methods for implementing SSD tracking that is both insensitive to illumination variations and computationally e cient. We rst de ..."
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Cited by 111 (8 self)
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Historically, SSD or correlationbased visual tracking algorithms have been sensitive to changes in illumination and shading across the target region. This paper describes methods for implementing SSD tracking that is both insensitive to illumination variations and computationally e cient. We rst describe a vectorspace formulation of the tracking problem, showing how to recover geometric deformations. We then show that the same vector space formulation can be used to account for changes in illumination. We combine geometry and illumination into an algorithm that tracks large image regions on live video sequences using no more computation than would be required to track with no accommodation for illumination changes. We present experimental results which compare theperformance of SSD tracking with and without illumination compensation. 1
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
, 2001
"... In this paper we show that a classic optical ow technique by Nagel and Enkelmann (1986) can be regarded as an early anisotropic diusion method with a diusion tensor. We introduce three improvements into the model formulation that (i) avoid inconsistencies caused by centering the brightness term and ..."
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Cited by 105 (13 self)
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In this paper we show that a classic optical ow technique by Nagel and Enkelmann (1986) can be regarded as an early anisotropic diusion method with a diusion tensor. We introduce three improvements into the model formulation that (i) avoid inconsistencies caused by centering the brightness term and the smoothness term in dierent images, (ii) use a linear scalespace focusing strategy from coarse to ne scales for avoiding convergence to physically irrelevant local minima, and (iii) create an energy functional that is invariant under linear brightness changes. Applying a gradient descent method to the resulting energy functional leads to a system of diusion{reaction equations. We prove that this system has a unique solution under realistic assumptions on the initial data, and we present an ecient linear implicit numerical scheme in detail. Our method creates ow elds with 100 % density over the entire image domain, it is robust under a large range of parameter variations, and it c...