Results 261 to 270 of about 47,265 (300)

Finite random sets and morphology

Proceedings of the 12th IAPR International Conference on Pattern Recognition (Cat. No.94CH3440-5), 2002
In order to be able to optimally design morphological shape extraction algorithms operating on binary digital images, a probability theory is needed for finite random sets and probability relations that show how the probability changes as a finite random set is propagated through a morphological operation.
Robert M. Haralick, Su S. Chen, Xinhua Zhuang   +2 more
openaire   +1 more source

Generalizing random-vector SLAM with random finite sets

2015 IEEE International Conference on Robotics and Automation (ICRA), 2015
The simultaneous localization and mapping (SLAM) problem in mobile robotics has traditionally been formulated using random vectors. Alternatively, random finite sets(RFSs) can be used in the formulation, which incorporates non-heursitic-based data association and detection statistics within an estimator that provides both spatial and cardinality ...
Keith Yu Kit Leung, Felipe Inostroza, Martin David Adams   +2 more
openaire   +1 more source

Labeled Random Finite Sets and Multi-Object Conjugate Priors

IEEE Transactions on Signal Processing, 2013
The objective of multi-object estimation is to simultaneously estimate the number of objects and their states from a set of observations in the presence of data association uncertainty, detection uncertainty, false observations, and noise.
Ba-Ngu Võ
exaly   +2 more sources

A labeled random finite set spawning model

2017 International Conference on Control, Automation and Information Sciences (ICCAIS), 2017
Previous labeled random finite set filter developments use a target motion model that only accounts for survival and birth. While such a model provides the means for a multi-target tracking filter such as the Generalized Labeled Multi-Bernoulli filter to capture target births and deaths in a wide variety of applications, it lacks the capability to ...
Daniel S. Bryant, Ba-Tuong Vo, Ba-Ngu Vo, Brandon A. Jones   +3 more
openaire   +1 more source

Finite Random Sets

1997
In this and the four chapters which follow, we develop the random set approach to data fusion summarized in Section 2.5 of Chapter 2. This chapter sets the stage by developing the mathematical cornerstone of this approach: the concept of a finite random set.
I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen   +2 more
openaire   +1 more source

Labeled Random Finite Sets With Moment Approximation

IEEE Transactions on Signal Processing, 2017
The probability hypothesis density (PHD) filter was proposed as a practical approximation of the multitarget Bayes filter. The cardinalized PHD (CPHD) filter improves on the PHD filter by propagating cardinality distribution. However, both the PHD and CPHD filters have limitations in dealing with missed detections, extracting target state in their ...
Zhejun Lu, Weidong Hu, Thia Kirubarajan
openaire   +1 more source

A Random-Finite-Set Approach to Bayesian SLAM

IEEE Transactions on Robotics, 2011
This paper proposes an integrated Bayesian frame work for feature-based simultaneous localization and map building (SLAM) in the general case of uncertain feature number and data association. By modeling the measurements and feature map as random finite sets (RFSs), a formulation of the feature-based SLAM problem is presented that jointly estimates the
John Mullane, Ba-Ngu Vo, Martin David Adams, Ba-Tuong Vo   +3 more
openaire   +3 more sources

Why Random Finite Sets?

2011
We begin the justification for the use of RFSs by re-evaluating the basic issues of feature representation, and considering the fundamental mathematical relationship between environmental feature representations, and robot motion. We further the justification for the use of RFSs in FBRM and SLAM by considering an issue of fundamental mathematical ...
Mullane, J., Vo, Ba-Ngu, Adams, M., Vo, B.   +3 more
openaire   +2 more sources

Estimation with Random Finite Sets

2011
The previous chapter provided the motivation to adopt an RFS representation for the map in both FBRM and SLAM problems. The main advantage of the RFS formulation is that the dimensions of the measurement likelihood and the predicted FBRM or SLAM state do not have to be compatible in the application of Bayes theorem, for optimal state estimation.
Mullane, J., Vo, Ba-Ngu, Adams, M., Vo, B.   +3 more
openaire   +2 more sources

Mobile Robotics in a Random Finite Set Framework

2011
This paper describes the Random Finite Set approach to Bayesian mobile robotics, which is based on a natural multi-object filtering framework, making it well suited to both single and swarm-based mobile robotic applications. By modeling the measurements and feature map as random finite sets (RFSs), joint estimates the number and location of the objects
John Mullane, Ba-Ngu Vo, Martin Adams, Ba-Tuong Vo   +3 more
openaire   +2 more sources