27,392 research outputs found
No-reference Image Denoising Quality Assessment
A wide variety of image denoising methods are available now. However, the
performance of a denoising algorithm often depends on individual input noisy
images as well as its parameter setting. In this paper, we present a
no-reference image denoising quality assessment method that can be used to
select for an input noisy image the right denoising algorithm with the optimal
parameter setting. This is a challenging task as no ground truth is available.
This paper presents a data-driven approach to learn to predict image denoising
quality. Our method is based on the observation that while individual existing
quality metrics and denoising models alone cannot robustly rank denoising
results, they often complement each other. We accordingly design denoising
quality features based on these existing metrics and models and then use Random
Forests Regression to aggregate them into a more powerful unified metric. Our
experiments on images with various types and levels of noise show that our
no-reference denoising quality assessment method significantly outperforms the
state-of-the-art quality metrics. This paper also provides a method that
leverages our quality assessment method to automatically tune the parameter
settings of a denoising algorithm for an input noisy image to produce an
optimal denoising result.Comment: 17 pages, 41 figures, accepted by Computer Vision Conference (CVC)
201
Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation
Autonomous harvesting and transportation is a long-term goal of the forest
industry. One of the main challenges is the accurate localization of both
vehicles and trees in a forest. Forests are unstructured environments where it
is difficult to find a group of significant landmarks for current fast
feature-based place recognition algorithms. This paper proposes a novel
approach where local observations are matched to a general tree map using the
Delaunay triangularization as the representation format. Instead of point cloud
based matching methods, we utilize a topology-based method. First, tree trunk
positions are registered at a prior run done by a forest harvester. Second, the
resulting map is Delaunay triangularized. Third, a local submap of the
autonomous robot is registered, triangularized and matched using triangular
similarity maximization to estimate the position of the robot. We test our
method on a dataset accumulated from a forestry site at Lieksa, Finland. A
total length of 2100\,m of harvester path was recorded by an industrial
harvester with a 3D laser scanner and a geolocation unit fixed to the frame.
Our experiments show a 12\,cm s.t.d. in the location accuracy and with
real-time data processing for speeds not exceeding 0.5\,m/s. The accuracy and
speed limit is realistic during forest operations
Counting, generating and sampling tree alignments
Pairwise ordered tree alignment are combinatorial objects that appear in RNA
secondary structure comparison. However, the usual representation of tree
alignments as supertrees is ambiguous, i.e. two distinct supertrees may induce
identical sets of matches between identical pairs of trees. This ambiguity is
uninformative, and detrimental to any probabilistic analysis.In this work, we
consider tree alignments up to equivalence. Our first result is a precise
asymptotic enumeration of tree alignments, obtained from a context-free grammar
by mean of basic analytic combinatorics. Our second result focuses on
alignments between two given ordered trees and . By refining our grammar
to align specific trees, we obtain a decomposition scheme for the space of
alignments, and use it to design an efficient dynamic programming algorithm for
sampling alignments under the Gibbs-Boltzmann probability distribution. This
generalizes existing tree alignment algorithms, and opens the door for a
probabilistic analysis of the space of suboptimal RNA secondary structures
alignments.Comment: ALCOB - 3rd International Conference on Algorithms for Computational
Biology - 2016, Jun 2016, Trujillo, Spain. 201
- …