Unsupervised merger detection and mitigation in still images using frequency and color content analysis
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.