Gradient Networks: Explicit Shape Matching Without Extracting Edges

We present a novel framework for shape-based template matching in images. While previous approaches required brittle contour extraction, considered only local information, or used coarse statistics, we propose to match the shape explicitly on low-level gradients by formulating the problem as traversing paths in a gradient network. We evaluate our algorithm on a challenging dataset of objects in cluttered environments and demonstrate significant improvement over state-of-theart methods for shape matching and object detection.

Authors

• Edward Hsiao Carnegie Mellon University
• Martial Hebert

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