Publications de Engin Türetken

Defect segmentation for multi-illumination quality control systems

Pascal Fua, Engin Türetken, Siavash Arjomand Bigdeli, David Honzátko

Thanks to recent advancements in image processing and deep learning techniques, visual surface inspection in production lines has become an automated process as long as all the defects are visible in a single or a few images. However, it is often necessary ...

2021

Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo

Pascal Fua, Engin Türetken, David Honzátko

The problem of estimating a surface shape from its observed reflectance properties still remains a challenging task in computer vision. The presence of global illumination effects such as inter-reflections or cast shadows makes the task particularly diffic ...

IEEE COMPUTER SOC2021

Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences

Pascal Fua, Engin Türetken, Xinchao Wang, Carlos Joaquin Becker

We propose a novel approach to automatically tracking elliptical cell populations in time-lapse image sequences. Given an initial segmentation, we account for partial occlusions and overlaps by generating an over-complete set of competing detection hypothe ...

Institute of Electrical and Electronics Engineers2017

Reconstructing Evolving Tree Structures in Time Lapse Sequences by Enforcing Time-Consistency

Pascal Fua, Engin Türetken, Raphael Sznitman, Jan Kybic, Agata Justyna Mosinska, Przemyslaw Rafal Glowacki, Miguel Amável Dos Santos Pinheiro, Daniel Lebrecht

We propose a novel approach to reconstructing curvilinear tree structures evolving over time, such as road networks in 2D aerial images or neural structures in 3D microscopy stacks acquired in vivo. To enforce temporal consistency, we simultaneously proces ...

2017

Reconstructing Curvilinear Networks using Path Classifiers and Integer Programming

Pascal Fua, Engin Türetken, Fethallah Benmansour, Przemyslaw Rafal Glowacki

We propose a novel Bayesian approach to automated delineation of curvilinear structures that form complex and potentially loopy networks. By representing the image data as a graph of potential paths, we first show how to weight these paths using discrimina ...

2016

Tracking Interacting Objects Using Intertwined Flows

Pascal Fua, François Fleuret, Engin Türetken, Xinchao Wang

In this paper, we show that tracking different kinds of interacting objects can be formulated as a network-flow Mixed Integer Program. This is made possible by tracking all objects simultaneously using intertwined flow variables and expressing the fact tha ...

Institute of Electrical and Electronics Engineers2016

Multiscale Centerline Detection

Pascal Fua, Vincent Lepetit, Engin Türetken, Amos Sironi

Finding the centerline and estimating the radius of linear structures is a critical first step in many applications, ranging from road delineation in 2D aerial images to modeling blood vessels, lung bronchi, and dendritic arbors in 3D biomedical image stac ...

Institute of Electrical and Electronics Engineers2016

Systems and methods for tracking interacting objects

Pascal Fua, François Fleuret, Engin Türetken, Xinchao Wang

Systems and methods for tracking interacting objects may acquire, with a sensor, and two or more images associated with two or more time instances. A processor may generate image data from the two or more images. The processor may apply an extended Probabi ...

2015

On the Relevance of Sparsity for Image Classification

Pascal Fua, Vincent Lepetit, Engin Türetken, Germán González Serrano, Roberto Rigamonti, Matthew Brown, Fethallah Benmansour

In this paper we empirically analyze the importance of sparsifying representations for classification purposes. We focus on those obtained by convolving images with linear filters, which can be either hand designed or learned, and perform extensive experim ...

Elsevier2014

Tracking Interacting Objects Optimally Using Integer Programming

Pascal Fua, François Fleuret, Engin Türetken, Xinchao Wang

In this paper, we show that tracking different kinds of interacting objects can be formulated as a network-flow Mixed Integer Program. This is made possible by tracking all objects simultaneously and expressing the fact that one object can appear or disapp ...

Springer Int Publishing Ag2014