Open Access
Maximum Likelihood Shape Matching
Nicu Sebe,Michael S. Lew +1 more
- 01 Jan 2002
TL;DR: This paper implemented two algorithms from the research literature and for each algorithm the efficacy of the SSD metric, the SAD (sum of the absolute differences) metric, and the Cauchy metric were compared.
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Abstract: Many visual matching algorithms can be described in terms of the features and the inter-feature distance or metric. The most commonly used metric is the sum of squared differences (SSD), which is valid from a maximum likelihood perspective when the real noise distribution is Gaussian. However, we have found experimentally that the Gaussian noise distribution assumption is often invalid. This implies that other metrics, which have distributions closer to the real noise distribution, should be used. In this paper we considered a shape matching application. We implemented two algorithms from the research literature and for each algorithm we compared the efficacy of the SSD metric, the SAD (sum of the absolute differences) metric, and the Cauchy metric. Furthermore, in the case where sufficient training data is available, we discussed and experimentally tested a new metric based directly on the real noise distribution, which we denoted the maximum likelihood metric.
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Citations
IEEE Transactions on Pattern Analysis and Machine Intelligence
King-Sun Fu
- 15 Oct 2004
Abstract: Abstmct-In this correspondence, we show how to recover the motion of an observer relative to a planar surface from image brightness derivatives. We do not compute the optical flow as an intermediate step, only the spatial and temporal brightness gradients (at a minimum of eight points). We first present two iterative schemes for solving nine nonlinear equations in terms of the motion and surface parameters that are derived from a least-squares fomulation. An initial pass over the relevant image region is wed to accumulate a number of moments of the image brightness derivatives. All of the quantities used in the iteration are efficiently computed from these totals without the need to refer back to the image. We then show that either of two possible solutions can be obtained in closed form. We first solve a linear matrix equation for the elements of a 3 x 3 matrix. The eigenvalue decomposition of the symmetric part of the matrix is then used to compute the motion parameters and the plane orientation. A new compact notation allows us to show easily that there are at most two planar solutions.
2.1K
Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution
TL;DR: A new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed and it is shown that the proposed approach is able to correctly classify signals which are embedded in noise with a very low SNR ratio.
Tracking objects in an indoor environment using 3D models
Congdu Nguyen,Minh Tuan Le,Haekwang Kim +2 more
- 24 Jul 2006
TL;DR: A new approach of tracking real objects in an indoor environment using two cameras and 3D models that automatically achieves parameters by searching the best-matching set of template contours in the database and edge pixels of the objects in two given images using generalized Hough transform.
2
Pose-invariant, model-based objectrecognition, using linear combination of viewsand Bayesian statistics
Vasileios Zografos
- 01 Jan 2009
TL;DR: This thesis presents an in-depth study on the problem of object recognition, and in particular the detection of 3-D objects in 2-D intensity images which may be viewed from a variety of angles.
2
Robust Shape Retrieval Using Maximum Likelihood Theory
Naif Alajlan,Paul Fieguth,Mohamed S. Kamel +2 more
- 29 Sep 2004
TL;DR: This paper shows that the ML metric outperforms the SSD and SAD metrics for token matching, and proposes a Parzen windows method that is continuous and more robust than the current ML metric based on histograms for PDF approximation.
1
References
Snakes : Active Contour Models
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Visual pattern recognition by moment invariants
TL;DR: It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be accomplished and it is indicated that generalization is possible to include invariance with parallel projection.
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Robust statistics: the approach based on influence functions
Frank R. Hampel,Elvezio Ronchetti,Peter J. Rousseeuw,Werner A. Stahel +3 more
- 01 Jan 1986
TL;DR: This paper presents a meta-modelling framework for estimating the values of Covariance Matrices and Multivariate Location using one-Dimensional and Multidimensional Estimators.
4.2K
IEEE Transactions on Pattern Analysis and Machine Intelligence
King-Sun Fu
- 15 Oct 2004
Abstract: Abstmct-In this correspondence, we show how to recover the motion of an observer relative to a planar surface from image brightness derivatives. We do not compute the optical flow as an intermediate step, only the spatial and temporal brightness gradients (at a minimum of eight points). We first present two iterative schemes for solving nine nonlinear equations in terms of the motion and surface parameters that are derived from a least-squares fomulation. An initial pass over the relevant image region is wed to accumulate a number of moments of the image brightness derivatives. All of the quantities used in the iteration are efficiently computed from these totals without the need to refer back to the image. We then show that either of two possible solutions can be obtained in closed form. We first solve a linear matrix equation for the elements of a 3 x 3 matrix. The eigenvalue decomposition of the symmetric part of the matrix is then used to compute the motion parameters and the plane orientation. A new compact notation allows us to show easily that there are at most two planar solutions.
2.1K
Visual learning and recognition of 3-D objects from appearance
Hiroshi Murase,Shree K. Nayar +1 more
TL;DR: A near real-time recognition system with 20 complex objects in the database has been developed and a compact representation of object appearance is proposed that is parametrized by pose and illumination.
2.1K