Tubular shape defined as a collection of points. Real tube image and mosaic of its interior.

MSc dissertation proposal 2014/2015

Pipeline Video Inspection

Introduction:

Inspecting the interior of pipelines has numerous applications [PVI_www]. One challenging aspect is that of analysing a number of hours of video footage in order to find an anomaly (e.g. video captured by a video pill [Sayaka]). A much more interesting interface is to build a mosaic of the interior of the tube, and therefore assess various locations of the pipeline as "reading a comics book".

Mosaicing the interior of tubular shapes (pipelines) consists in combining multiple images into a single one (mosaic) representing all the interior. In the framework of the present work, mosaicing the interior of a tubular shape involves acquiring images, finding and reconstructing corresponding feature points, fitting a simple 3D model to the reconstructed points, estimating the camera path and dewarping to one single mosaic image. This work focus on fitting a simple 3D model to the reconstructed points. We propose a 3D model that is based on cylindrical sections, and is useful both for generating simulated data and implementing the fitting procedure.

Objectives:

The objectives of this work are twofold: (i) proposing a robust reconstruction method of the structure of the tubular shape; (ii) building image mosaics from the reconstructed structure. At the end, this work is expected to demonstrate a fast and practical methodology for the visualization of the interior of tubular shapes.

Detailed description:

In the case of tubular shapes perfectly cylindrical (no curves), watched by cameras perfectly positioned and aligned with the cylinder axis, mosaicing the interior would be just a polar to cartesian dewarping followed by the registration of corresponding image features. Registration would than be just computing an homography between each pair of images. In many situations, the camera motion and the structure of the tubular shape are not so perfect (see e.g. the application of the capsule endoscope [Sayaka]).

In our case, we want to consider tubular shapes including straight and curved sections, and to allow free movement of the camera. Hence we start from the traditional idea of reconstructing points of the scene and then focus on fitting a simple 3D model, to the various tube sections, which makes again simple the dewarping step [Ruivo07].

The work is therefore organized in the following main steps:

1) Reconstructing the tubular shape by selecting and registering distinctive visual features along the tubular shape (the main motion parameters may be locally optimised in order to best interpret the input data) [SIFT features, OpenCV]

2) Augmenting the tubular model by allowing multiple segments.

3) Dewarping the tubular shape to obtain the image mosaic of its interior.

References:

[PVI_www] Pipeline video inspection, http://en.wikipedia.org/wiki/Pipeline_video_inspection

[Sayaka] Sayaka: Next-generation capsule endoscope, http://www.rfamerica.com/sayaka/index.html, http://www.youtube.com/watch?v=UHYPfcESvR0.

[Ruivo07] Mosaicing the Interior of Tubular Shapes: 3D-model Fitting, Luís Ruivo, José Gaspar, José Santos Victor, Proc. of RecPad 2007 - 13ª Conferência Portuguesa de Reconhecimento de Padrões, Lisbon, Portugal , 2007

[Pereira13] Mosaicing the Interior of Tubular Structures, D. Pereira, J. Tomaz, R. Ferreira, J. Gaspar, in 19th Portuguese Conference on Pattern Recognition (RecPad 2013)1st November, Lisbon, Portugal.

[SIFT features] http://www.cs.ubc.ca/~lowe/keypoints/

[OpenCV] Open Computer Vision Library, http://sourceforge.net/projects/opencv/

Expected results:

At the end of the work the students will have enriched their knowledge in:

* Computer vision

* Image mosaicing, vision based reconstruction

Examples of expected demonstrations in simulated and/or real environments:

* reconstruction of a tubular shape given a set of points tracked along the tubular shape

* obtaining the image mosaic of a simple (constant section) tubular shape

Observations:

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More MSc dissertation proposals on Computer and Robot Vision in:

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