AI Article Synopsis
- The paper introduces a new system for detecting objects based on their contours in messy environments, starting from a specific contour model for each object class.
- It breaks down these contour models into smaller parts called fragments and groups them into part bundles that can overlap.
- The proposed method utilizes both local shape similarity for identifying candidate object shapes in images and global shape similarity for optimizing these configurations, while also incorporating texture information for better detection of uniquely textured objects.
Article Abstract
In this paper we propose a novel framework for contour based object detection from cluttered environments. Given a contour model for a class of objects, it is first decomposed into fragments hierarchically. Then, we group these fragments into , where a part bundle can contain overlapping fragments. Given a new image with set of edge fragments we develop an efficient voting method using local shape similarity between part bundles and edge fragments that generates high quality candidate part configurations. We then use global shape similarity between the part configurations and the model contour to find optimal configuration. Furthermore, we show that appearance information can be used for improving detection for objects with distinctive texture when model contour does not sufficiently capture deformation of the objects.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5328026 | PMC |
| http://dx.doi.org/10.1016/j.cviu.2010.03.009 | DOI Listing |
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