کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
527409 | 869320 | 2015 | 15 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Factorization of view-object manifolds for joint object recognition and pose estimation Factorization of view-object manifolds for joint object recognition and pose estimation](/preview/png/527409.png)
• We address multi-view recognition problem by factorizing view-object manifold.
• We use a common manifold to represent view manifolds of different objects.
• We use the view manifold deformation for categorization.
• We extensively experiment to validate the robustness and strength of our approach.
Due to large variations in shape, appearance, and viewing conditions, 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 environments. 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 parameterized by its homeomorphic mapping/reconstruction from the unified manifold. In this work, we develop a novel framework to jointly solve the three challenging recognition sub-problems, by explicitly modeling the deformations of object manifolds and factorizing it in a view-invariant space for recognition. We perform extensive experiments on several challenging datasets and achieve state-of-the-art results.
Journal: Computer Vision and Image Understanding - Volume 139, October 2015, Pages 89–103