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- Donald Geman, Bruno Jedynak
- IEEE Trans. Pattern Anal. Mach. Intell.
- 1996

We present a new approach for tracking roads from satellite images, and thereby illustrate a general computational strategy (\active testing") for tracking 1D structures and other recognition tasks in computer vision. Our approach is related to recent work in active vision on \where to look next" and motivated by the \divide-and-conquer" strategy of parlor… (More)

- Joshua T Vogelstein, Brendon O Watson, Adam M Packer, Rafael Yuste, Bruno Jedynak, Liam Paninski
- Biophysical journal
- 2009

As recent advances in calcium sensing technologies facilitate simultaneously imaging action potentials in neuronal populations, complementary analytical tools must also be developed to maximize the utility of this experimental paradigm. Although the observations here are fluorescence movies, the signals of interest--spike trains and/or time varying… (More)

- Bruno Jedynak, Peter I. Frazier, Raphael Sznitman
- J. Applied Probability
- 2012

We consider the problem of 20 questions with noisy answers, in which we seek to find a target by repeatedly choosing a set, asking an oracle whether the target lies in this set, and obtaining an answer corrupted by noise. Starting with a prior distribution on the target's location, we seek to minimize the expected entropy of the posterior distribution. We… (More)

This work is aimed at the detection of adult images that appear in Internet. Skin detection is of the paramount importance in the detection of adult images. We build a maximum entropy model for this task. This model, called the First Order Model in this paper, is subject to constraints on the color gradients of neighboring pixels. Parameter estimation as… (More)

This paper considers the task of finding a target location by making a limited number of sequential observations. Each observation results from evaluating an imperfect classifier of a chosen cost and accuracy on an interval of chosen length and position. Within a Bayesian framework, we study the problem of minimizing an objective that combines the entropy… (More)

- Hailiang Huang, Bruno Jedynak, Joel S. Bader
- PLoS Computational Biology
- 2007

Yeast two-hybrid screens are an important method for mapping pairwise physical interactions between proteins. The fraction of interactions detected in independent screens can be very small, and an outstanding challenge is to determine the reason for the low overlap. Low overlap can arise from either a high false-discovery rate (interaction sets have low… (More)

- Bruno Jedynak, Huicheng Zheng, Mohamed Daoudi
- EMMCVPR
- 2002

We consider a sequence of three models for skin detection built from a large collection of labelled images. Each model is a maximum entropy model with respect to constraints concerning marginal distributions. Our models are nested. The first model, called the baseline model is well known from practitioners. Pixels are considered as independent. Performance,… (More)

- Bruno Jedynak, Huicheng Zheng, Mohamed Daoudi
- 2003 Conference on Computer Vision and Pattern…
- 2003

We consider a sequence of three models for skin detection built from a large collection of labelled images. Each model is a maximum entropy model with respect to constraints concerning marginal distributions. Our models are nested. The first model is well known from practitioners. Pixels are considered as independent. The second model is a Hidden Markov… (More)

- HoJung Cho, Henrik Jönsson, +6 authors Andre Levchenko
- PLoS biology
- 2007

Colonies of bacterial cells can display complex collective dynamics, frequently culminating in the formation of biofilms and other ordered super-structures. Recent studies suggest that to cope with local environmental challenges, bacterial cells can actively seek out small chambers or cavities and assemble there, engaging in quorum sensing behavior. By… (More)

- Bruno Jedynak, Sanjeev Khudanpur
- Neural Computation
- 2005

We propose a new method for estimating the probability mass function (pmf) of a discrete and finite random variable from a small sample. We focus on the observed counts--the number of times each value appears in the sample--and define the maximum likelihood set (MLS) as the set of pmfs that put more mass on the observed counts than on any other set of… (More)