Motion Segmentation with Weak Labeling Priors

Motions of organs or extremities are important features for clinical diagnosis. However, tracking and segmentation of complex, quickly changing motion patterns is challenging, certainly in the presence of occlusions. Neither state-of-the-art tracking nor motion segmentation approaches are able to deal with such cases. Thus far, motion capture systems or the like were needed which are complicated to handle and which impact on the movements. We propose a solution based on a single video camera, that is not only far less intrusive, but also a lot cheaper. The limitation of tracking and motion segmentation are overcome by a new approach to integrate prior knowledge in the form of weak labeling into motion segmentation. Using the example of Cerebral Palsy detection, we segment motion patterns of infants into the different body parts by analyzing body movements. Our experimental results show that our approach outperforms current motion segmentation and tracking approaches.

[1]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[2]  G Rau,et al.  Movement analysis in the early detection of newborns at risk for developing spasticity due to infantile cerebral palsy. , 2006, Human movement science.

[3]  Zdenek Kalal,et al.  Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  R. Vidal,et al.  Motion segmentation with missing data using PowerFactorization and GPCA , 2004, CVPR 2004.

[5]  Jitendra Malik,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence Segmentation of Moving Objects by Long Term Video Analysis , 2022 .

[6]  Vladimir Kolmogorov,et al.  Convergent Tree-Reweighted Message Passing for Energy Minimization , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Ivan Laptev,et al.  Track to the future: Spatio-temporal video segmentation with long-range motion cues , 2011, CVPR 2011.

[8]  Bodo Rosenhahn,et al.  Multi-scale Clustering of Frame-to-Frame Correspondences for Motion Segmentation , 2012, ECCV.

[9]  René Vidal,et al.  Sparse subspace clustering , 2009, CVPR.

[10]  Thomas Deselaers,et al.  ClassCut for Unsupervised Class Segmentation , 2010, ECCV.

[11]  Xiang Li,et al.  A Query-by-Example Content-Based Image Retrieval System of Non-melanoma Skin Lesions , 2009, MCBR-CDS.

[12]  René Vidal,et al.  A Benchmark for the Comparison of 3-D Motion Segmentation Algorithms , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Jitendra Malik,et al.  Object Segmentation by Long Term Analysis of Point Trajectories , 2010, ECCV.

[14]  Robert P. W. Duin,et al.  Statistical Analysis of Gait Data to Assist Clinical Decision Making , 2009, MCBR-CDS.

[15]  Anil M. Cheriyadat,et al.  Non-negative matrix factorization of partial track data for motion segmentation , 2010, 2009 IEEE 12th International Conference on Computer Vision.

[16]  Thomas Brox,et al.  Higher order motion models and spectral clustering , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Matthieu Guillaumin,et al.  Segmentation Propagation in ImageNet , 2012, ECCV.

[18]  Gentaro Taga,et al.  Specific characteristics of spontaneous movements in preterm infants at term age are associated with developmental delays at age 3 years , 2013, Developmental medicine and child neurology.

[19]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.

[20]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[21]  Wei Wu,et al.  Robust Trajectory Clustering for Motion Segmentation , 2013, 2013 IEEE International Conference on Computer Vision.

[22]  A. Jensenius,et al.  Early prediction of cerebral palsy by computer‐based video analysis of general movements: a feasibility study , 2010, Developmental medicine and child neurology.

[23]  Michael J. Black,et al.  Layered segmentation and optical flow estimation over time , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Hervé Delingette,et al.  Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 , 2012, Lecture Notes in Computer Science.

[25]  Zhuwen Li,et al.  Perspective Motion Segmentation via Collaborative Clustering , 2013, 2013 IEEE International Conference on Computer Vision.

[26]  Shuo Li,et al.  Regional Heart Motion Abnormality Detection via Multiview Fusion , 2012, MICCAI.

[27]  KolmogorovVladimir.  Convergent Tree-Reweighted Message Passing for Energy Minimization , 2006 .

[28]  O. M. Aamo,et al.  An Optical Flow-Based Method to Predict Infantile Cerebral Palsy , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[29]  Øyvind Stavdahl,et al.  Video-based early cerebral palsy prediction using motion segmentation , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.