4,549 research outputs found
Learning Action Maps of Large Environments via First-Person Vision
When people observe and interact with physical spaces, they are able to
associate functionality to regions in the environment. Our goal is to automate
dense functional understanding of large spaces by leveraging sparse activity
demonstrations recorded from an ego-centric viewpoint. The method we describe
enables functionality estimation in large scenes where people have behaved, as
well as novel scenes where no behaviors are observed. Our method learns and
predicts "Action Maps", which encode the ability for a user to perform
activities at various locations. With the usage of an egocentric camera to
observe human activities, our method scales with the size of the scene without
the need for mounting multiple static surveillance cameras and is well-suited
to the task of observing activities up-close. We demonstrate that by capturing
appearance-based attributes of the environment and associating these attributes
with activity demonstrations, our proposed mathematical framework allows for
the prediction of Action Maps in new environments. Additionally, we offer a
preliminary glance of the applicability of Action Maps by demonstrating a
proof-of-concept application in which they are used in concert with activity
detections to perform localization.Comment: To appear at CVPR 201
Structure Preserving Large Imagery Reconstruction
With the explosive growth of web-based cameras and mobile devices, billions
of photographs are uploaded to the internet. We can trivially collect a huge
number of photo streams for various goals, such as image clustering, 3D scene
reconstruction, and other big data applications. However, such tasks are not
easy due to the fact the retrieved photos can have large variations in their
view perspectives, resolutions, lighting, noises, and distortions.
Fur-thermore, with the occlusion of unexpected objects like people, vehicles,
it is even more challenging to find feature correspondences and reconstruct
re-alistic scenes. In this paper, we propose a structure-based image completion
algorithm for object removal that produces visually plausible content with
consistent structure and scene texture. We use an edge matching technique to
infer the potential structure of the unknown region. Driven by the estimated
structure, texture synthesis is performed automatically along the estimated
curves. We evaluate the proposed method on different types of images: from
highly structured indoor environment to natural scenes. Our experimental
results demonstrate satisfactory performance that can be potentially used for
subsequent big data processing, such as image localization, object retrieval,
and scene reconstruction. Our experiments show that this approach achieves
favorable results that outperform existing state-of-the-art techniques
Bi-Objective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models
Nonnegative matrix factorization (NMF) is a powerful class of feature
extraction techniques that has been successfully applied in many fields, namely
in signal and image processing. Current NMF techniques have been limited to a
single-objective problem in either its linear or nonlinear kernel-based
formulation. In this paper, we propose to revisit the NMF as a multi-objective
problem, in particular a bi-objective one, where the objective functions
defined in both input and feature spaces are taken into account. By taking the
advantage of the sum-weighted method from the literature of multi-objective
optimization, the proposed bi-objective NMF determines a set of nondominated,
Pareto optimal, solutions instead of a single optimal decomposition. Moreover,
the corresponding Pareto front is studied and approximated. Experimental
results on unmixing real hyperspectral images confirm the efficiency of the
proposed bi-objective NMF compared with the state-of-the-art methods
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