An Optical Flow Based Segmentation Method for Objects Extraction

This paper describes a segmentation algorithm based on the cooperation of an optical flow estimation method with edge detection and region growing procedures. The proposed method has been developed as a pre-processing stage to be used in methodologies and tools for video/image indexing and retrieval by content. The addressed problem consists in extracting whole objects from background for producing images of single complete objects from videos or photos. The extracted images are used for calculating the object visual features necessary for both indexing and retrieval processes. The first task of the algorithm exploits the cues from motion analysis for moving area detection. Objects and background are then refined using respectively edge detection and region growing procedures. These tasks are iteratively performed until objects and background are completely resolved. The developed method has been applied to a variety of indoor and outdoor scenes where objects of different type and shape are represented on variously textured background. Keywords—Motion Detection, Object Extraction, Optical Flow, Segmentation.

[1]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[2]  Nikos Paragios,et al.  A Variational Approach for the Segmentation of the Left Ventricle in Cardiac Image Analysis , 2002, International Journal of Computer Vision.

[3]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[4]  Ahmed S. Abutaleb,et al.  Automatic thresholding of gray-level pictures using two-dimensional entropy , 1989, Comput. Vis. Graph. Image Process..

[5]  Marco Roggero,et al.  Object segmentation with region growing and principal component analisys , 2002 .

[6]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[7]  Ioannis Pitas,et al.  Optical flow estimation and moving object segmentation based on median radial basis function network , 1998, IEEE Trans. Image Process..

[8]  Xiaobo Li,et al.  Adaptive image region-growing , 1994, IEEE Trans. Image Process..

[9]  Nebojsa Jojic,et al.  Consistent segmentation for optical flow estimation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[10]  Jitendra Malik,et al.  Contour and Texture Analysis for Image Segmentation , 2001, International Journal of Computer Vision.

[11]  Nahum Kiryati,et al.  Dense discontinuous optical flow via contour-based segmentation , 2005, IEEE International Conference on Image Processing 2005.

[12]  Jianping Fan,et al.  Automatic image segmentation by integrating color-edge extraction and seeded region growing , 2001, IEEE Trans. Image Process..

[13]  Steven W. Zucker,et al.  Trace Inference, Curvature Consistency, and Curve Detection , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[15]  Anil K. Jain,et al.  Segmentation of X-ray and C-scan images of fiber reinforced composite materials , 1992, Pattern Recognit..

[16]  E. E. Eldukhri,et al.  Image segmentation using fuzzy min-max neural networks for wood defect detection , 2005 .

[17]  Françoise Dibos,et al.  Moving Objects Segmentation Using Optical Flow Estimation , 2003 .

[18]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[19]  S. Sastry,et al.  Segmentation of dynamic scenes from image intensities , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[20]  Elisa Francomano,et al.  An algorithm for optical flow computation based on a quasi-interpolant operator , 2006 .

[21]  Venansius Baryamureeba,et al.  PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 8 , 2005 .

[22]  Lance R. Williams,et al.  Stochastic Completion Fields: A Neural Model of Illusory Contour Shape and Salience , 1997, Neural Computation.

[23]  Shimon Ullman,et al.  Structural Saliency: The Detection Of Globally Salient Structures using A Locally Connected Network , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[24]  Hugues Talbot,et al.  Globally Optimal Geodesic Active Contours , 2005, Journal of Mathematical Imaging and Vision.

[25]  Klaus D. Tönnies,et al.  Segmentation of medical images using adaptive region growing , 2001, SPIE Medical Imaging.

[26]  Daniel Cremers,et al.  A variational framework for image segmentation combining motion estimation and shape regularization , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[27]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Xun Wang,et al.  Deformable Contour Method: A Constrained Optimization Approach , 2004, International Journal of Computer Vision.

[29]  Ugo Montanari,et al.  On the optimal detection of curves in noisy pictures , 1971, CACM.

[30]  Josef Kittler,et al.  Region growing: a new approach , 1998, IEEE Trans. Image Process..