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Proposal for Moving Object Extraction

Proposal for Moving Object Extraction. By Amra Ananda Sruthi Gaddam Madhavi Paidipalli. Outline. Introduction Related Work Summary Compare and Contrast Pros and Cons Limitations Future Work Conclusion References. Introduction .

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Proposal for Moving Object Extraction

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  1. Proposal for Moving Object Extraction By AmraAnanda SruthiGaddam MadhaviPaidipalli

  2. Outline • Introduction • Related Work • Summary • Compare and Contrast • Pros and Cons • Limitations • Future Work • Conclusion • References

  3. Introduction • Purpose: Important technique for obtaining semantic feature information in a video sequence is termed as moving object extraction. • Aim: To develop an automatic system for detection and extraction of moving objects • In this proposal, we summarize the general idea of moving object extraction by researching through several techniques and provide an analytical comparison between them.

  4. Applications • Object-based video coding in MPEG-4 standard • Video surveillance • Video indexing • Retrieval/editing of video sequences

  5. Related Work Some of the methods that we have used in our survey to extract moving objects are – • By clustering using minimum volume ellipsoid. • Using watershed algorithm. • By finding an optimal solution in determining clusters using merging and removing certain clusters. • Using morphological watershed algorithm.

  6. Related Work • Using region merging technique. • Using Kim’s algorithm. • Using K-means clustering. • Using Xia’s algorithm. • Using spatio-temporal information from still background. • By expanding X-means clustering technique.

  7. Minimum volume ellipsoid • The algorithm takes subsamples of the dataset and calculates the Volume of the Ellipsoid representing that subsample. • So the Minimum Volume Ellipsoid (MVE) will correspond with the actual core of the dataset. After this, data points exceeding the cut-off value will be designated as outliers.d • A new clustering approach is proposed. Minimum volume ellipsoid estimator identifies least volume region containing h percent of the data points. For given value of h, a cluster is delineated based on the ellipsoid and shape is compared to get best fit using Kolmogorov-Smirnov test.

  8. Watershed Algorithm •  A drop of water falling on a topographic relief flows along a path to finally reach a local minimum. • Local minima of the gradient of the image may be chosen as markers. • Water placed on any pixel enclosed by a common watershed line flows downhill to a common local intensity minimum (LIM) • Pixels draining to a common minimum form a catch basin, which represents a segment.

  9. Split and merge • Split-and-merge segmentation is based on a quad tree partition of an image. • It is sometimes called quad tree segmentation. • Conversely, if four son-squares are homogeneous, they can be merged as several connected components (the merging process) • This process continues recursively until no further splits or merges are possible.

  10. Morphological watershed • The morphological watershed algorithm is a modified algorithm in which morphological processing is included for the prevention of over-segmentation. • Morphological watershed analysis segments an image into different regions by interpreting the image as a topographic surface. • The assumption being made here is that regions of similar characteristics will have a lower height than those of the boundaries. • When the water reaches a certain level, it then tries to over-flood a large height so as to merge two or more basins. This is prevented by building a dam which is a thin line that has a height greater than the highest point in the image and is positioned in such a way that it prevents the multiple basins from merging.

  11. Region Merging Technique. • Region Merging - recursively merge regions that are similar. • Region merging goes on with merging at pixel level and then at region level. • Regions may merge with having similar property of some kind. • Region based segmentation is feature based.

  12. Kim’s algorithm • Localization of objects in consecutive temporal frames. Variance results in F-test. • Spatial segmentation gives precise object boundaries of moving objects. • Temporal segmentation gives change detection mask, moving areas in foreground, nonmoving areas in background. 4. Combination gives VOP - velocity of propagation.

  13. X Means • K means suffers -it scales poorly computationally, the number of clusters K has to be supplied by the user. • This is a new algorithm that efficiently, searches the space of cluster locations and number of clusters to optimize the Bayesian Information Criterion (BIC). • X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region. The decision between the children of each center and itself is done comparing the BIC-values of the two structures.

  14. Xia’s Algorithm • The extraction of temporal coherent masks of physical meaningful objects in video sequences. 2) 3-D nonlinear diffusion filters, morphological operators, region merging technique.

  15. Using spatio-temporal information from still background. • The mosaic representation of video allows us to fully exploit the spatio-temporal information in the video scene to achieve robust segmentation over a group of frames. Thus, their system is more robust and is able to extract the non-rigid object that has complex motion from one frame to the next. • Extended spatial information - this captures the appearance of the entire scene imaged in the video clip and is represented in the form of a few often just one panoramic mosaic images constructed by composing the information from the different views of the scene in the individual frames into a single image

  16. Expanding X-means clustering technique • Expansion of non hierarchical clustering algorithm • Merging cluster made by k-means iteration results in adequate clustering.

  17. Compare and Contrast

  18. Pros and Cons • The problem of x-means is solved, where the division is based on the assumption where amount of data in each cluster is equal. • The proposed approaches helped in extracting the objects very accurately with fast and complex motions. • These techniques can be applied for object based video coding and moving object detection.

  19. Limitations • Determination of optical flow is not so efficient when the background scenes have no sufficient features or when the background is texture less. • When the foreground object contains very thin structure or small holes with respect to image size ,incorrect separation may happen in these regions.

  20. Conclusion • We have tried to study a moving objects based on the methods described in these 12 papers. • We focus on moving objects in images mainly so most of the part comes from image processing,clustering,segmentation, thresholding etc.

  21. References • J.M. Jolion, P. Meer and S. Bataouche: “Robust Clustering with applications in computer vision”, IEEE Trnas. Pattern Anal. Machine Intell., Vol.13, No.8, pp.791– 802, 1991. • L. Vincent and P. Soille: “Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations”, IEEE Trans. Pattern Anal. Machine Intell., Vol.13, No.6, pp.583–598, 1991. • R. Krishnapuram, and C.P. Freg, “Fitting an unknown number of lines and planes to image data through compatible cluster merging”, Pattern recognition, 25, 1992, 385–400. • D. Cortez et al.: “Image Segmentation Towards New Image Representation Methods”, Signal Processing: Image Communication, Vol.6, pp.485–498, 1995. • U.M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From data mining to knowledge discovery: An overview”, in Advances in Knowledge Discovery and Data Mining, (AAAI Press/The MIT Press, 1996) 1–34 • F. Mochieni, S. Bhattacharjee, M. Kunt: “Spatiotemporal Segmentation Based on Region Merging”, IEEE Trans. Pattern Anal. Machine Intell., Vol.20, No.9, pp.897–915, 1998.

  22. References • M. Kim, J.G. Choi, D. Kim, and H. Lee, “A VOP generation tool: Automatic segmentation of moving objects in image sequences based on spatial-temporal information,” IEEE Trans. Circuits Syst. Video Technol., vol. 9, no. 8, pp. 1216-1226, 1999 • D. Pelleg, A. Moore: “X-means: Extended K-menas with Efficient Estimation of the Number of Clusters”, Proc. of the 17th International Conference on Machine Leaning, pp.727–734, 2000. • J. Xia and Y. Wang, “A spatio-temporal video analysis system for object segmentation,” ISPA, vol.2, pp.18-20, Sept. 2003. • L.-H. Chen, Y.-C. Lai, C.-W. Su, and H.-Y.M. Liao: “Extraction of Video Object with Complex Motion”, Pattern Recognition Letters, Vol.25, No.11, pp.1285– 1291, 2004. • T. Ishioka,T.: “An Expansion of X-means for Automatically Determining the Optimal Number of Clusters”, Proc. of The 4th IASTED International Conference on Computational Intelligence, pp.91-96, 2005. • M. Rousson, N. Paragios: “Prior Knowledge, Level Set Representations and Visual Grouping. International Journal of Computer Vision”, Vol.76, No.3, pp.231– 243, 2008.

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