activity
20062021
most citedmodAL: A modular active learning framework for Python

26 citations · 28 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2021

Surface Segmentation Using Implicit Divergence Constraint Between Adjacent Minimal Paths

Jozsef Molnar, Peter Horvath

We introduce a novel approach for object segmentation from 3D images using modified minimal path Eikonal equation. The proposed method utilizes an implicit constraint - a second or…

math.MG2019

An Elastic Energy Minimization Framework for Mean Surface Calculation

Jozsef Molnar, Peter Horvath

As the continuation of the contour mean calculation - designed for averaging the manual delineations of 3D layer stack images - in this paper, the most important equations: a) the…

math.MG2019★ 1 cited

An Elastic Energy Minimization Framework for Mean Contour Calculation

Jozsef Molnar, Michael Barbier, Winnok H. De Vos +1

In this paper we propose a contour mean calculation and interpolation method designed for averaging manual delineations of objects performed by experts and interpolate 3D layer sta…

math.OC2018

Balanced Phase Field model for Active Surfaces

Jozsef Molnar, Peter Horvath

In this paper we present a balanced phase field model for active surfaces. This work is devoted to the generalization of the Balanced Phase Field Model for Active Contours devised…

cs.LG2018★ 26 cited

modAL: A modular active learning framework for Python

Tivadar Danka, Peter Horvath

modAL is a modular active learning framework for Python, aimed to make active learning research and practice simpler. Its distinguishing features are (i) clear and modular object o…

cs.CV2006★ 1 cited

A higher-order active contour model of a `gas of circles' and its application to tree crown extraction

Peter Horvath, Ian Jermyn, Zoltan Kato +1

Many image processing problems involve identifying the region in the image domain occupied by a given entity in the scene. Automatic solution of these problems requires models that…