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Lookup NU author(s): Dr Telmo Amaral,
Professor Ilias Kyriazakis
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Institute of Electrical and Electronics Engineers, 2016.
For re-use rights please refer to the publisher's terms and conditions.
Difficulties can arise from the segmentation of three-dimensional objects formed by multiple non-rigid parts represented in two-dimensional images. Problems involving parts whose spatial arrangement is subject to weak restrictions, and whose appearance and form change across images, can be particularly challenging. Segmentation methods that take into account spatial context information have addressed these types of problem, which often involve image data of a multi-modal nature. An attractive feature of the auto-context (AC) technique is that a prior “atlas”, typically obtained by averaging multiple label maps created by experts, can be used as an initial source of contextual data. However, a prior obtained in this way is likely to hide the inherent multi-modality of the data. We propose a modification of AC in which a probabilistic atlas of part locations is iteratively improved and made available as an additional source of information. We illustrate this technique with the problem of segmenting individual organs in images of pig offal, reporting statistically significant improvements in relation to both conventional AC and a state-of-the-art technique based on conditional random fields.
Author(s): Amaral T, Kyriazakis I, McKenna SJ, Plötz T
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: IEEE Winter Conference on Applications of Computer Vision (WACV)
Year of Conference: 2016
Online publication date: 07/03/2016
Acceptance date: 15/01/2016
Date deposited: 24/03/2016
Publisher: Institute of Electrical and Electronics Engineers
Series Title: IEEE Workshop on Applications of Computer Vision. Proceedings