5 research outputs found
A Two-Stage Shape Retrieval (TSR) Method with Global and Local Features
A robust two-stage shape retrieval (TSR) method is proposed to address the 2D
shape retrieval problem. Most state-of-the-art shape retrieval methods are
based on local features matching and ranking. Their retrieval performance is
not robust since they may retrieve globally dissimilar shapes in high ranks. To
overcome this challenge, we decompose the decision process into two stages. In
the first irrelevant cluster filtering (ICF) stage, we consider both global and
local features and use them to predict the relevance of gallery shapes with
respect to the query. Irrelevant shapes are removed from the candidate shape
set. After that, a local-features-based matching and ranking (LMR) method
follows in the second stage. We apply the proposed TSR system to MPEG-7,
Kimia99 and Tari1000 three datasets and show that it outperforms all other
existing methods. The robust retrieval performance of the TSR system is
demonstrated
Supervised deep learning of elastic SRV distances on the shape space of curves
Motivated by applications from computer vision to bioinformatics, the field
of shape analysis deals with problems where one wants to analyze geometric
objects, such as curves, while ignoring actions that preserve their shape, such
as translations, rotations, or reparametrizations. Mathematical tools have been
developed to define notions of distances, averages, and optimal deformations
for geometric objects. One such framework, which has proven to be successful in
many applications, is based on the square root velocity (SRV) transform, which
allows one to define a computable distance between spatial curves regardless of
how they are parametrized. This paper introduces a supervised deep learning
framework for the direct computation of SRV distances between curves, which
usually requires an optimization over the group of reparametrizations that act
on the curves. The benefits of our approach in terms of computational speed and
accuracy are illustrated via several numerical experiments.Comment: 4 pages, 4 figures, 3 tables. Submitted to IEEE Signal Processing
Letter
A PDE-based Method for Shape Registration
In the square root velocity framework, the computation of shape space
distances and the registration of curves requires solution of a non-convex
variational problem. In this paper, we present a new PDE-based method for
solving this problem numerically. The method is constructed from numerical
approximation of the Hamilton-Jacobi-Bellman equation for the variational
problem, and has quadratic complexity and global convergence for the distance
estimate. In conjunction, we propose a backtracking scheme for approximating
solutions of the registration problem, which additionally can be used to
compute shape space geodesics. The methods have linear numerical convergence,
and improved efficiency compared previous global solvers
cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey
The "cvpaper.challenge" is a group composed of members from AIST, Tokyo Denki
Univ. (TDU), and Univ. of Tsukuba that aims to systematically summarize papers
on computer vision, pattern recognition, and related fields. For this
particular review, we focused on reading the ALL 602 conference papers
presented at the CVPR2015, the premier annual computer vision event held in
June 2015, in order to grasp the trends in the field. Further, we are proposing
"DeepSurvey" as a mechanism embodying the entire process from the reading
through all the papers, the generation of ideas, and to the writing of paper.Comment: Survey Pape
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In
2015 and 2016, we thoroughly study 1,600+ papers in several
conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV