AAAI Publications, Twenty-Seventh AAAI Conference on Artificial Intelligence

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Joint Object and Pose Recognition Using Homeomorphic Manifold Analysis
Haopeng Zhang, Tarek El-Gaaly, Ahmed Elgammal, Zhiguo Jiang

Last modified: 2013-06-30


Object recognition is a key precursory challenge in the fields of object manipulation and robotic/AI visual reasoning in general. Recognizing object categories, particular instances of objects and viewpoints/poses of objects are three critical subproblems robots must solve in order to accurately grasp/manipulate objects and reason about their environ- ments. Multi-view images of the same object lie on intrinsic low-dimensional manifolds in descriptor spaces (e.g. visual/depth descriptor spaces). These object manifolds share the same topology despite being geometrically different. Each object manifold can be represented as a deformed version of a unified manifold. The object manifolds can thus be parametrized by its homeomorphic mapping/reconstruction from the unified manifold. In this work, we construct a manifold descriptor from this mapping between homeomorphic manifolds and use it to jointly solve the three challenging recognition sub-problems. We extensively experiment on a challenging multi-modal (i.e. RGBD) dataset and other object pose datasets and achieve state-of-the-art results.


vision; kinect; object recognition; machine learning, multi-modal visual learning

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