Motion and structure factorization and segmentation of long multiple motion image sequences

Debrunner, C.; Ahuja, Narendra

doi:10.1007/3-540-55426-2_24

Cited by 18 publications

(13 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The factorization method [5][13] [19], in essence, is principal component analysis of some measurement matrix. Principal component analysis expresses the variance of the measurement matrix in a compact and robust way and has been extensively studied in computational statistics [7].…”

Section: Past Workmentioning

confidence: 99%

Principal component analysis with missing data and its application to object modeling

Shum

Ikeuchi

Reddy

1994

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94

View full text Add to dashboard Cite

Section: Past Workmentioning

confidence: 99%

Principal component analysis with missing data and its application to object modeling

Shum

Ikeuchi

Reddy

1994

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94

View full text Add to dashboard Cite

“…Tomasi and Kanade [14] developed an algorithm to recover structure and motion from a sequence of orthographic images which was later extended for scaled orthographic projection and the paraperspective models by Poelman and Kanade [12]. In addition, algorithms for segmenting multiple moving objects were proposed in [4], [6], [3].…”

Section: Introductionmentioning

confidence: 99%

A geometric interpretation of weak-perspective motion

Shimshoni

Rivlin

1999

IEEE Trans. Pattern Anal. Machine Intell.

View full text Add to dashboard Cite

Abstract-We present a geometric interpretation of the problem of motion recovery from three weak-perspective images. Our interpretation is based on reducing the problem of estimating the motion to a problem of finding triangles on a sphere whose angles are known. Using this geometric interpretation, a simple method to completely recover the motion parameters using three images is developed. The results of running the algorithm on real images are presented. In addition, we describe which of the various motion parameters can be recovered already from two images.Index Terms-Weak-perspective projection, structure from motion.

show abstract

“…The first stage extends the trajectories and the second one attempts to correct any errors. Debrunner and Ahuja [5] uses a two stage method to finding trajectories. The first step computes short feature paths of constant velocity.…”

Section: Introductionmentioning

confidence: 99%

Pixel matching and motion segmentation in image sequences

Ahuja

Charan

1996

Recent Developments in Computer Vision

Self Cite

View full text Add to dashboard Cite

Abstract. This paper presents a coarse-to-fine algorithm to obtain pinel trajectories in a long image sequence and to segment it into subsets corresponding to distinctly moving objects. Much of the previous related work has addressed the computation of optical flow over two frames or sparse feature trajectories in sequences. The features used are often small in number and restrictive assumptions are made about them such as the visibility of features in all the frames. The algorithm described here uses a coarse scale point feature detector to form a 3-D dot pattern in the spatiotemporal space. The trajectories are extracted as 3-D curves formed by the points using perceptual grouping. Increasingly dense correspondences are obtained iteratively from the sparse feature trajectories. At the finest level, which is the focus of this paper, all pixels are matched and the finest boundaries of the moving objects are obtained. Keywords: Motion Segmentation, Perceptual Grouping, Pixel Matching, Triangulation, Feature Matching, Optical Flow. IntroductionThis paper describes a component of our work aimed at interpretation of image sequences. Given an image sequence containing an arbitrary number of rigid objects in motion, the objectives of the overall work are to identify feature points in the scene, obtain spatially dense trajectories of those points, segment moving objects, compute image flow at each pixel, and derive a qualitative description of the scene structure and dynamics from the image sequence. Such qualitative interpretation of the image sequence is useful for a variety of applications such as traffic scene analysis, biological image analysis and aerial image understanding. The focus of this paper is on the detection of pixel flow trajectories. Next section reviews some related previous work. Section 3 summarizes the steps of coarseto-fine detection of sparse feature trajectories. Section 4 then gives the details of the algorithm for finding pixel flow trajectories which is the objective of this paper. Section 5 presents experimental results and Section 6 presents concluding remarks.

show abstract

Motion and structure factorization and segmentation of long multiple motion image sequences

Cited by 18 publications

References 7 publications

Principal component analysis with missing data and its application to object modeling

Principal component analysis with missing data and its application to object modeling

A geometric interpretation of weak-perspective motion

Pixel matching and motion segmentation in image sequences

Contact Info

Product

Resources

About