Parameter Estimation in Optimal Object Recognition

Moment Invariants (MIs) are widely used for object recognition. Most of the moment based object recognition work reported is for 2-Dimensional (2-D) objects only. There have been considerable attempts made in extending the 2-D MIs to 3-D space. However, using moments for 3-D motion parameter estimation is relatively neglected. In this paper we present two iterative schemes for motion estimation of planar objects using moments as features. One, using the Levinberg-Marquardt method, performs better compared with the other. Only the pure rotational case is considered. By using moments, the correspondence problem is completely eliminated. We show from simulation experiments that this method is a feasible one and the error performance is reasonable. As motion of a planar patch is considered, the algorithm estimates both the rotational parameters and the planar coefficients.

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Parameter Estimation in Optimal Object Recognition

Advances in Pattern Recognition - Markov Random Field Modeling in Image Analysis ◽

10.1007/978-1-84800-279-1_8 ◽

2009 ◽

pp. 1-27 ◽

Cited By ~ 2

Author(s):

Stan Z. Li

Keyword(s):

Parameter Estimation ◽

Object Recognition

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A NOVEL PROBABILISTIC MODEL FOR OBJECT RECOGNITION AND POSE ESTIMATION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001401000903 ◽

2001 ◽

Vol 15 (02) ◽

pp. 241-253 ◽

Cited By ~ 4

Author(s):

J. HORNEGGER ◽

H. NIEMANN

Keyword(s):

Parameter Estimation ◽

Object Recognition ◽

Probability Density ◽

Pose Estimation ◽

Probabilistic Model ◽

Experimental Evaluation ◽

Image Plane ◽

Bayes Rule ◽

Probabilistic Framework ◽

Parameter Estimation Problem

In this paper we consider the problem of object recognition and localization in a probabilistic framework. An object is represented by a parametric probability density, and the computation of pose parameters is implemented as a nonlinear parameter estimation problem. The presence of a probabilistic model allows for recognition according to Bayes rule. The introduced probabilistic model requires no prior segmentation but characterizes the statistical properties of observed intensity values in the image plane. A detailed discussion of the applied theoretical framework is followed by a concise experimental evaluation which demonstrates the benefit of the proposed approach.

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Weighted Combination of Prior and Sample Information and Parameter Estimation

Optimization ◽

10.1080/02331937608842369 ◽

1976 ◽

Vol 7 (5) ◽

pp. 665-672

Author(s):

H. Burke ◽

C. Hennig ◽

W H. Schmidt

Keyword(s):

Parameter Estimation ◽

Sample Information ◽

Weighted Combination

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Use It and Still Lose It?

GeroPsych ◽

10.1024/1662-9647/a000020 ◽

2010 ◽

Vol 23 (3) ◽

pp. 169-175 ◽

Cited By ~ 14

Author(s):

Adrian Schwaninger ◽

Diana Hardmeier ◽

Judith Riegelnig ◽

Mike Martin

Keyword(s):

Object Recognition ◽

Cognitive Development ◽

Recognition Test ◽

Cognitive Aging ◽

Recognition Task ◽

Airport Security ◽

Visual Object ◽

Long Term Effects ◽

Object Recognition Test ◽

X Ray

In recent years, research on cognitive aging increasingly has focused on the cognitive development across middle adulthood. However, little is still known about the long-term effects of intensive job-specific training of fluid intellectual abilities. In this study we examined the effects of age- and job-specific practice of cognitive abilities on detection performance in airport security x-ray screening. In Experiment 1 (N = 308; 24–65 years), we examined performance in the X-ray Object Recognition Test (ORT), a speeded visual object recognition task in which participants have to find dangerous items in x-ray images of passenger bags; and in Experiment 2 (N = 155; 20–61 years) in an on-the-job object recognition test frequently used in baggage screening. Results from both experiments show high performance in older adults and significant negative age correlations that cannot be overcome by more years of job-specific experience. We discuss the implications of our findings for theories of lifespan cognitive development and training concepts.

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