289,333 research outputs found
A graph-based mathematical morphology reader
This survey paper aims at providing a "literary" anthology of mathematical
morphology on graphs. It describes in the English language many ideas stemming
from a large number of different papers, hence providing a unified view of an
active and diverse field of research
Generalizable semi-supervised learning method to estimate mass from sparsely annotated images
Mass flow estimation is of great importance to several industries, and it can
be quite challenging to obtain accurate estimates due to limitation in expense
or general infeasibility. In the context of agricultural applications, yield
monitoring is a key component to precision agriculture and mass flow is the
critical factor to measure. Measuring mass flow allows for field productivity
analysis, cost minimization, and adjustments to machine efficiency. Methods
such as volume or force-impact have been used to measure mass flow; however,
these methods are limited in application and accuracy. In this work, we use
deep learning to develop and test a vision system that can accurately estimate
the mass of sugarcane while running in real-time on a sugarcane harvester
during operation. The deep learning algorithm that is used to estimate mass
flow is trained using very sparsely annotated images (semi-supervised) using
only final load weights (aggregated weights over a certain period of time). The
deep neural network (DNN) succeeds in capturing the mass of sugarcane
accurately and surpasses older volumetric-based methods, despite highly varying
lighting and material colors in the images. The deep neural network is
initially trained to predict mass on laboratory data (bamboo) and then transfer
learning is utilized to apply the same methods to estimate mass of sugarcane.
Using a vision system with a relatively lightweight deep neural network we are
able to estimate mass of bamboo with an average error of 4.5% and 5.9% for a
select season of sugarcane.Comment: 22 pages, 21 figures, Computers and Electronics in Agriculture. arXiv
admin note: text overlap with arXiv:1908.0438
Log-Euclidean Bag of Words for Human Action Recognition
Representing videos by densely extracted local space-time features has
recently become a popular approach for analysing actions. In this paper, we
tackle the problem of categorising human actions by devising Bag of Words (BoW)
models based on covariance matrices of spatio-temporal features, with the
features formed from histograms of optical flow. Since covariance matrices form
a special type of Riemannian manifold, the space of Symmetric Positive Definite
(SPD) matrices, non-Euclidean geometry should be taken into account while
discriminating between covariance matrices. To this end, we propose to embed
SPD manifolds to Euclidean spaces via a diffeomorphism and extend the BoW
approach to its Riemannian version. The proposed BoW approach takes into
account the manifold geometry of SPD matrices during the generation of the
codebook and histograms. Experiments on challenging human action datasets show
that the proposed method obtains notable improvements in discrimination
accuracy, in comparison to several state-of-the-art methods
STV-based Video Feature Processing for Action Recognition
In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end
On The Effect of Hyperedge Weights On Hypergraph Learning
Hypergraph is a powerful representation in several computer vision, machine
learning and pattern recognition problems. In the last decade, many researchers
have been keen to develop different hypergraph models. In contrast, no much
attention has been paid to the design of hyperedge weights. However, many
studies on pairwise graphs show that the choice of edge weight can
significantly influence the performances of such graph algorithms. We argue
that this also applies to hypegraphs. In this paper, we empirically discuss the
influence of hyperedge weight on hypegraph learning via proposing three novel
hyperedge weights from the perspectives of geometry, multivariate statistical
analysis and linear regression. Extensive experiments on ORL, COIL20, JAFFE,
Sheffield, Scene15 and Caltech256 databases verify our hypothesis. Similar to
graph learning, several representative hyperedge weighting schemes can be
concluded by our experimental studies. Moreover, the experiments also
demonstrate that the combinations of such weighting schemes and conventional
hypergraph models can get very promising classification and clustering
performances in comparison with some recent state-of-the-art algorithms
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