NDLI: Patch Match Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation

Content Provider	IEEE Xplore Digital Library
Author	Jiangbo Lu Hongsheng Yang Dongbo Min Do, M.N.
Copyright Year	2013
Description	Author affiliation: Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA (Do, M.N.) \|\| Adv. Digital Sci. Center, Singapore, Singapore (Jiangbo Lu; Hongsheng Yang; Dongbo Min)
Abstract	Though many tasks in computer vision can be formulated elegantly as pixel-labeling problems, a typical challenge discouraging such a discrete formulation is often due to computational efficiency. Recent studies on fast cost volume filtering based on efficient edge-aware filters have provided a fast alternative to solve discrete labeling problems, with the complexity independent of the support window size. However, these methods still have to step through the entire cost volume exhaustively, which makes the solution speed scale linearly with the label space size. When the label space is huge, which is often the case for (sub pixel-accurate) stereo and optical flow estimation, their computational complexity becomes quickly unacceptable. Developed to search approximate nearest neighbors rapidly, the Patch Match method can significantly reduce the complexity dependency on the search space size. But, its pixel-wise randomized search and fragmented data access within the 3D cost volume seriously hinder the application of efficient cost slice filtering. This paper presents a generic and fast computational framework for general multi-labeling problems called Patch Match Filter (PMF). For the very first time, we explore effective and efficient strategies to weave together these two fundamental techniques developed in isolation, i.e., Based-based randomized search and efficient edge-aware image filtering. By decompositing an image into compact super pixels, we also propose super pixel-based novel search strategies that generalize and improve the original Patch Match method. Focusing on dense correspondence field estimation in this paper, we demonstrate PMF's applications in stereo and optical flow. Our PMF methods achieve state-of-the-art correspondence accuracy but run much faster than other competing methods, often giving over 10-times speedup for large label space cases.
Starting Page	1854
Ending Page	1861
File Size	1156607
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.242
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Complexity theory Labeling Estimation Optical imaging Image segmentation Search problems Optical filters optical flow PatchMatch nearest-neighbor search edge-aware image filtering cost volume filtering stereo
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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M.: Patch match filter: Efficient edge-aware filtering meets randomized search for fast correspondence field estimation

M.: Patch match filter: Efficient edge-aware filtering meets randomized search for fast correspondence field estimation

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