Recovery of the 3-D location and motion of a rigid object through camera image (An Extended Kalman Filter Approach)

Wu, Jian; Rink, R. E.; Caelli, Terry; Gourishankar, V.

doi:10.1007/bf00133556

Cited by 49 publications

(22 citation statements)

References 15 publications

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“…Fusing, including Kalman filtering, is the second traditional approach to MISFM after optimization [3,5,7,8,56,59,60,73,85,86,88,90,100,101,110]. By fusing, I mean any MISFM technique that computes a final multi-image reconstruction from intermediate reconstructions (and estimates of the uncertainties in these) rather than from the image data directly.…”

Section: Fusing/kalman Filteringmentioning

confidence: 97%

A Critique of Structure-from-Motion Algorithms

Oliensis

2000

Computer Vision and Image Understanding

131

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Section: Fusing/kalman Filteringmentioning

confidence: 97%

A Critique of Structure-from-Motion Algorithms

Oliensis

2000

Computer Vision and Image Understanding

131

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“…The most noticeable examples among them are quaternions of unit norm and rotation matrices. Minimal representations comprise Euler's (or RPY) angles [7,10,11] and rotation vectors [13].…”

Section: Parameterisation Of the Orientation And The Exponential Mapmentioning

confidence: 99%

“…This is usually divided into four steps: prediction, projection, measurement and adjustment. Tracking systems usually use a variant of Kalman filtering to govern this process [3,6,10,11,13].…”

Section: Introductionmentioning

confidence: 99%

Filtering in a unit quaternion space for model-based object tracking

Ude¹

1999

Robotics and Autonomous Systems

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The main idea in object tracking is to support the processing of incoming images by predicting future object's poses and features using the knowledge about the object's previous motion. In this paper we present a new method for the prediction and adjustment of motion parameters to the current measurements using the quaternion representation for the orientation and the Gauss-Newton iteration on the unit sphere S 3 . Unlike other trackers, our tracker searches for estimates directly in the space of rigid body motions (represented by R 3 × S 3 ) and takes into account the differential and geometric properties of this space. We present some results showing the successful tracking in synthetic and real image sequences.

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“…As well, Matthies et al [1989] and Wu et al [1989] have used a Kalman-filter approach to integrate information over time in the computation of 3-D information from a sequence of images. Spetsakis and Aloimonos [1988] also showed the effectiveness of using least squares in image displacement motion techniques.…”

Section: Literature Surveymentioning

confidence: 99%

The feasibility of motion and structure from noisy time-varying image velocity information

Barron

Jepson

Tsotsos

1990

Int J Comput Vision

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This research addresses the problem of noise sensitivity inherent in motion and structure algorithms. The motion and structure paradigm is a two-step process. First, we measure image velocities and, perhaps, their spatial and temporal derivatives, are obtained from time-varying image intensity data and second, we use these data to compute the motion of a moving monocular observer in a stationary environment under perspective projection, relative to a single 3-D planar surface. The first contribution of this article is an algorithm that uses time-varying image velocity information to compute the observer's translation and rotation and the normalized surface gradient of the 3-D planar surface. The use of time-varying image velocity information is an important tool in obtaining a more robust motion and structure calculation. The second contribution of this article is an extensive error analysis of the motion and structure problem. Any motion and structure algorithm that uses image velocity information as its input should exhibit error sensitivity behavior compatible with the results reported here. We perform an average and worst case error analysis for four types of image velocity information: full and normal image velocities and full and normal sets of image velocity and its derivatives. (These derivatives are simply the coefficients of a truncated Taylor series expansion about some point in space and time.) The main issues we address here are: just how sensitive is a motion and structure computation in the presence of noisy input, or alternately, how accurate must our image velocity information be, how much and what type of input data is needed, and under what circumstances is motion and structure feasible? That is, when can we be sure that a motion and structure computation will produce usable results? We base our answers on a numerical error analysis we conduct for a large number of motions.

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Recovery of the 3-D location and motion of a rigid object through camera image (An Extended Kalman Filter Approach)

Cited by 49 publications

References 15 publications

A Critique of Structure-from-Motion Algorithms

A Critique of Structure-from-Motion Algorithms

Filtering in a unit quaternion space for model-based object tracking

The feasibility of motion and structure from noisy time-varying image velocity information

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