#### Filter Results:

- Full text PDF available (2)

#### Publication Year

2016

2017

- This year (2)
- Last 5 years (4)
- Last 10 years (4)

#### Publication Type

#### Co-author

#### Journals and Conferences

Learn More

- Nir Halay, Koby Todros, Alfred O. Hero
- 2016 IEEE Statistical Signal Processing Workshop…
- 2016

In this paper, a generalization of the Gaussian quasi likelihood ratio test (GQLRT) for Bayesian binary hypothesis testing is developed. The proposed generalization, called measure-transformed GQLRT (MT-GQLRT), selects a Gaussian probability model that best empirically fits a transformed conditional probability measure of the data. By judicious choice of… (More)

- Nir Halay, Koby Todros, Alfred O. Hero
- IEEE Transactions on Signal Processing
- 2017

In this paper, the Gaussian quasi-likelihood ratio test (GQLRT) for non-Bayesian binary hypothesis testing is generalized by applying a transform to the probability distribution of the data. The proposed generalization, called measure-transformed GQLRT (MT-GQLRT), selects a Gaussian probability model that best empirically fits a transformed probability… (More)

- Nir Halay, Koby Todros
- IEEE Signal Processing Letters
- 2017

Recently, we developed a robust generalization of the Gaussian quasi-likelihood ratio test (GQLRT). This generalization, called measure-transformed GQLRT (MT-GQLRT), operates by selecting a Gaussian model that best empirically fits a transformed probability measure of the data. In this letter, a plug-in version of the MT-GQLRT is developed for robust… (More)

- Tsachi Hershkovich, Tamar Shalmon, +6 authors Tammy Riklin-Raviv
- Computer Vision and Image Understanding
- 2016

Fully-automated segmentation algorithms offer fast, objective, and reproducible results for large data collections. However these techniques cannot handle tasks that require contextual knowledge not readily available in the images alone. Thus, the expertise of an experienced physician is necessary. We present a generative approach to image segmentation,… (More)

- ‹
- 1
- ›