Pairwise Clustering with Matrix Factorisation and the EM Algorithm
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
A Real-Time Region-Based Motion Segmentation Using Adaptive Thresholding and K-Means Clustering
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
A Maximum Likelihood Framework for Grouping and Segmentation
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Detected motion classification with a double-background and a neighborhood-based difference
Pattern Recognition Letters
Accurate object contour tracking based on boundary edge selection
Pattern Recognition
Spatio-temporal video object segmentation via scale-adaptive 3D structure tensor
EURASIP Journal on Applied Signal Processing
Kernel bandwidth estimation for nonparametric modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Smoothing of optical flow using robustified diffusion kernels
Image and Vision Computing
Robust processing of optical flow of fluids
IEEE Transactions on Image Processing
Histogram-based foreground object extraction for indoor and outdoor scenes
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Modelling and Simulation in Engineering
Optical flow diffusion with robustified kernels
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
2D shape measurement of multiple moving objects by GMM background modeling and optical flow
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Steering kernel-based video moving objects detection with local background texture dictionaries
Computers and Electrical Engineering
| Hi-index | 0.01 |
Various approaches have been proposed for simultaneous optical flow estimation and segmentation in image sequences. In this study, the moving scene is decomposed into different regions with respect to their motion, by means of a pattern recognition scheme. The inputs of the proposed scheme are the feature vectors representing still image and motion information. Each class corresponds to a moving object. The classifier employed is the median radial basis function (MRBF) neural network. An error criterion function derived from the probability estimation theory and expressed as a function of the moving scene model is used as the cost function. Each basis function is activated by a certain image region. Marginal median and median of the absolute deviations from the median (MAD) estimators are employed for estimating the basis function parameters. The image regions associated with the basis functions are merged by the output units in order to identify moving objects