/Gradient Networks: Explicit Shape Matching Without Extracting Edges

Gradient Networks: Explicit Shape Matching Without Extracting Edges

Edward Hsiao and Martial Hebert
Conference Paper, AAAI Conference on Artificial Intelligence (AAAI), July, 2013

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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-the-art methods for shape matching and object detection.

BibTeX Reference
author = {Edward Hsiao and Martial Hebert},
title = {Gradient Networks: Explicit Shape Matching Without Extracting Edges},
booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
year = {2013},
month = {July},
keywords = {gradient networks, shape matching, object detection, edges, gradients},