Compensating for Large In-Plane Rotations in Natural Images

Boominathan, Lokesh and Srinivas, Suraj and Babu, RV (2016) Compensating for Large In-Plane Rotations in Natural Images. In: 10th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), DEC 18-22, 2016, Indian Inst Technol, Guwahati, INDIA.

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Abstract

Rotation invariance has been studied in the computer vision community primarily in the context of small in-plane rotations. This is usually achieved by building invariant image features. However, the problem of achieving invariance for large rotation angles remains largely unexplored. In this work, we tackle this problem by directly compensating for large rotations, as opposed to building invariant features. This is inspired by the neuro-scientific concept of mental rotation, which humans use to compare pairs of rotated objects. Our contributions here are three-fold. First, we train a Convolutional Neural Network (CNN) to detect image rotations. We find that generic CNN architectures are not suitable for this purpose. To this end, we introduce a convolutional template layer, which learns representations for canonical `unrotated' images. Second, we use Bayesian Optimization to quickly sift through a large number of candidate images to find the canonical `unrotated' image. Third, we use this method to achieve robustness to large angles in an image retrieval scenario. Our method is task-agnostic, and can be used as a pre-processing step in any computer vision system.

Item Type:	Conference Proceedings
Additional Information:	Copy right for this article belongs to the ASSOC COMPUTING MACHINERY, 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	23 Dec 2017 08:56
Last Modified:	25 Feb 2019 05:48
URI:	http://eprints.iisc.ac.in/id/eprint/58495

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