Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Proc., vol. 3, pp. 549-552, May 17-21, 2004, Montreal, Canada.

Unsupervised Merger Detection and Mitigation in Still Images Using Frequency and Color Content Analysis

Serene Banerjee and Brian L. Evans

Embedded Signal Processing Laboratory, Wireless Networking and Communications Group, The University of Texas at Austin, Austin, TX 78712 USA
serene@ece.utexas.edu - bevans@ece.utexas.edu

Draft of Paper - Poster - Code

Research on Digital Still Cameras at UT Austin

Abstract

When taking pictures, professional photographers apply photographic composition rules, e.g. avoidance of mergers. A merger occurs when equally focused foreground and background regions appear to merge as one object. This paper presents an unsupervised algorithm that (a) detects the main subject, (b) detects background objects merging with the main subject, and (c) reduces the visibility of merging background objects. Detection of the main subject requires automated adjustment of camera settings. The rest of the algorithm does not adjust or use the camera settings. The algorithm does not make assumptions about the scene setting (indoor/outdoor) or content. The algorithm is amenable to implementation on a fixed-point processor.

Man with trees
Original picture Proposed algorithm blurs tree above right shoulder and head blurred but does not blur the main subject or other background objects
Bird with trees
Original picture Proposed algorithm blurs the tree in background but not the main subject or other objects in the background.

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Last Updated 11/03/05.