Insect eyes and possible machine counterparts [Neumann05], and discrete camera [Galego13].

MSc dissertation proposal 2013/2014

Discrete Video Cameras: Modelling and Calibration

Introduction:

Discrete cameras are collections of pixels, photocells, organized as pencils of lines with unknown topologies. Distinctly from common cameras, discrete cameras can be formed just by some sparse, non regular, sets of pixels.

Discrete cameras are interesting for robotic applications due to allowing designs specific to the tasks at hand [Neumann05], but pose a challenge right from the calibration point.

Recent research work has shown that discrete cameras, which can be moved freely and have a central arrangement of the pixels, can be calibrated from natural scenes [Grossmann10]. This MSc project focuses on building and calibrating a discrete camera.

Objectives:

In this work the objectives are two fold: (i) mounting a discrete camera combining a standard camera with a cable of optic fibers, (ii) calibrating the camera.

Detailed description:

Conventional video cameras are built from CCD or CMOS sensors whose pixels are organized in rectangular grids. Determining the intrinsic parameters of a mobile camera without any assumptions about the imaged world is called camera self- or auto-calibration [Hassanpour04]. More commonly, cameras are static and one shows them a planar structured (chess) calibration pattern in various poses, which is enough to perform the calibration [Bouguet-WWW].

Discrete cameras simply combine pixels in a fixed manner but without a specific arrangement. Discrete cameras are interesting for robotic applications due to allowing designs specific to the tasks at hand [Neumann05], but pose a challenge right from the calibration point. Recent research work has shown that discrete cameras, which can be moved freely and have a central arrangement of the pixels, can be calibrated from natural scenes [Grossmann10, Galego13]. This MSc project focuses on building and calibrating a discrete camera.

The construction of the camera will be based on a standard camera and a standard lens. In between the camera and the lens one will insert a collection of optical fibbers, rigidly glued to each other [Neumann05].

A number of calibration methodologies are available for discrete cameras. The main idea is that neighbour photocells view approximately the same direction of the world and thus have higher correlations of their time-signal readings (pixel streams). As distinct from many conventional calibration methods in use today, calibrating discrete cameras requires moving them within a diversified (textured) natural world. This project follows approaches based on information theory and computer learning methodologies.

The calibration methodology will be first developed based on simulation and then using real cameras, namely cameras hand-held or pan-tilt-zoom mounted on static basis or on mobile robots.

The main steps of the work are therefore the following:

- building a simulated camera that allows acquiring calibration data

- testing the calibration of the simulated camera

- assembling the discrete central camera

- calibrating the assembled camera

References:

[Bouguet-WWW] Jean-Yves Bouguet, "Camera calibration toolbox for matlab", http://www.vision.caltech.edu/bouguetj/calib_doc/

[Hassanpour04] Camera auto-calibration using a sequence of 2D images with small rotations, Reza Hassanpour, Volkan Atalay, Pattern Recognition Letters, Vol.25, Issue 9, 2 July 2004, Pages 989-997

[Agapito01] Agapito, L., Hayman, E., Reid, I.D., 2001. Self calibration of rotating and zooming cameras. Int. J. Comput. Vision 45(2), 107–127.

[Neumann05] "Compound Eye Sensor for 3D Ego Motion Estimation", Jan Neumann, Cornelia Fermuller, Yiannis Aloimonos, Vladimir Brajovic, IROS 2005, see also http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.929

[Grossmann10] "Discrete camera calibration from pixel streams", Etienne Grossmann, José António Gaspar and Francesco Orabona, Computer Vision and Image Understanding (Special issue on Omnidirectional Vision, Camera Networks and Non-conventional Cameras), Volume 114, Issue 2, Pages 198-209, February 2010.

[Galego13] "Topological Auto-Calibration of Central Imaging Sensors", R. Galego, R. Ferreira, A. Bernardino, E. Grossmann and J. Gaspar, IbPRIA 2013

Requirements (grades, required courses, etc):

-

Expected results:

At the end of the work, the students will have enriched their experience in computer vision. In particular are expected to develop and assess:

- algorithms for calibrating central cameras.

Place for conducting the work-proposal:

ISR / IST

More MSc dissertation proposals on Computer and Robot Vision in:

http://omni.isr.ist.utl.pt/~jag