Improving Ransac-Based Segmentation Through Cnn Encapsulation

Abstract

In this work, we present a method for improving a random sample consensus (RANSAC) based image segmentation algorithm by encapsulating it within a convolutional neural network (CNN). The improvements are gained by gradient descent training on the set of pre-RANSAC filtering and thresholding operations using a novel RANSAC-based loss function, which is geared toward optimizing the strength of the correct model relative to the most convincing false model. Thus, it can be said that our loss function trains the network on metrics that directly dictate the success or failure of the final segmentation rather than metrics that are merely correlated to the success or failure. We demonstrate successful application of this method to a RANSAC method for identifying the pupil boundary in images from the CASIA-IrisV3 iris recognition data set, and we expect that this method could be successfully applied to any RANSAC-based segmentation algorithm.

Publication Date

11-6-2017

Publication Title

Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

Volume

2017-January

Number of Pages

2661-2670

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CVPR.2017.285

Socpus ID

85044321083 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85044321083

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