IEEE Transactions on Pattern Analysis and Machine Intelligence
Measurement error models
Scene Segmentation from Visual Motion Using Global Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
A simplified linear optic-flow motion algorithm
Computer Vision, Graphics, and Image Processing
Inherent Ambiguities in Recovering 3-D Motion and Structure from a Noisy Flow Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Signal Processing - Multidimensional Signal Processing, Part II
Robust regression methods for computer vision: a review
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Statistical Analysis of Inherent Ambiguities in Recovering 3-D Motion from a Noisy Flow Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Displacements from Two 3-D Frames Obtained From Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Matching 3-D Line Segments with Applications to Multiple-Object Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vehicle-Type Motion Estimation From Multi-Frame Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Motion and Structure Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust 3-D-3-D Pose Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An adaptively refined block matching algorithm for motion compensated video coding
IEEE Transactions on Circuits and Systems for Video Technology
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Optical Flow Computation Based on Least-Median-of-Squares Regression
International Journal of Computer Vision
Hierarchical Estimation and Segmentation of Dense Motion Fields
International Journal of Computer Vision
Color image segmentation using fuzzy C-means and eigenspace projections
Signal Processing
Implementation of an Automatic Semi-Fluid Motion Analysis Algorithm on a Massively Parallel Computer
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
Application of Genetic Algorithms to 3-D Shape Reconstruction in an Active Stereo Vision System
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
Motion Analysis with the Radon Transform on Log-Polar Images
Journal of Mathematical Imaging and Vision
Histogram-based foreground object extraction for indoor and outdoor scenes
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Visual perception theory guided depth motion estimation
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
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
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Optic flow motion analysis represents an important family of visual information processing techniques in computer vision. Segmenting an optic flow field into coherent motion groups and estimating each underlying motion is a very challenging task when the optic flow field is projected from a scene of several independently moving objects. The problem is further complicated if the optic flow data are noisy and partially incorrect. In this paper, we present a novel framework for determining such optic flow fields by combining the conventional robust estimation with a modified genetic algorithm. The baseline model used in the development is a linear optic flow motion algorithm [38] due to its computational simplicity. The statistical properties of the generalized linear regression (GLR) model are thoroughly explored and the sensitivity of the motion estimates toward data noise is quantitatively established. Conventional robust estimators are then incorporated into the linear regression model to suppress a small percentage of gross data errors or outliers. However, segmenting an optic flow field consisting of a large portion of incorrect data or multiple motion groups requires a very high robustness that is unattainable by the conventional robust estimators. To solve this problem, we propose a genetic partitioning algorithm that elegantly combines the robust estimation with the genetic algorithm by a bridging genetic operator called self-adaptation.