67,254 research outputs found
Scatteract: Automated extraction of data from scatter plots
Charts are an excellent way to convey patterns and trends in data, but they
do not facilitate further modeling of the data or close inspection of
individual data points. We present a fully automated system for extracting the
numerical values of data points from images of scatter plots. We use deep
learning techniques to identify the key components of the chart, and optical
character recognition together with robust regression to map from pixels to the
coordinate system of the chart. We focus on scatter plots with linear scales,
which already have several interesting challenges. Previous work has done fully
automatic extraction for other types of charts, but to our knowledge this is
the first approach that is fully automatic for scatter plots. Our method
performs well, achieving successful data extraction on 89% of the plots in our
test set.Comment: Submitted to ECML PKDD 2017 proceedings, 16 page
Superpixel-based Semantic Segmentation Trained by Statistical Process Control
Semantic segmentation, like other fields of computer vision, has seen a
remarkable performance advance by the use of deep convolution neural networks.
However, considering that neighboring pixels are heavily dependent on each
other, both learning and testing of these methods have a lot of redundant
operations. To resolve this problem, the proposed network is trained and tested
with only 0.37% of total pixels by superpixel-based sampling and largely
reduced the complexity of upsampling calculation. The hypercolumn feature maps
are constructed by pyramid module in combination with the convolution layers of
the base network. Since the proposed method uses a very small number of sampled
pixels, the end-to-end learning of the entire network is difficult with a
common learning rate for all the layers. In order to resolve this problem, the
learning rate after sampling is controlled by statistical process control (SPC)
of gradients in each layer. The proposed method performs better than or equal
to the conventional methods that use much more samples on Pascal Context,
SUN-RGBD dataset.Comment: Accepted in British Machine Vision Conference (BMVC), 201
The Evolution of Neural Network-Based Chart Patterns: A Preliminary Study
A neural network-based chart pattern represents adaptive parametric features,
including non-linear transformations, and a template that can be applied in the
feature space. The search of neural network-based chart patterns has been
unexplored despite its potential expressiveness. In this paper, we formulate a
general chart pattern search problem to enable cross-representational
quantitative comparison of various search schemes. We suggest a HyperNEAT
framework applying state-of-the-art deep neural network techniques to find
attractive neural network-based chart patterns; These techniques enable a fast
evaluation and search of robust patterns, as well as bringing a performance
gain. The proposed framework successfully found attractive patterns on the
Korean stock market. We compared newly found patterns with those found by
different search schemes, showing the proposed approach has potential.Comment: 8 pages, In proceedings of Genetic and Evolutionary Computation
Conference (GECCO 2017), Berlin, German
Solar Magnetic Feature Detection and Tracking for Space Weather Monitoring
We present an automated system for detecting, tracking, and cataloging
emerging active regions throughout their evolution and decay using SOHO
Michelson Doppler Interferometer (MDI) magnetograms. The SolarMonitor Active
Region Tracking (SMART) algorithm relies on consecutive image differencing to
remove both quiet-Sun and transient magnetic features, and region-growing
techniques to group flux concentrations into classifiable features. We
determine magnetic properties such as region size, total flux, flux imbalance,
flux emergence rate, Schrijver's R-value, R* (a modified version of R), and
Falconer's measurement of non-potentiality. A persistence algorithm is used to
associate developed active regions with emerging flux regions in previous
measurements, and to track regions beyond the limb through multiple solar
rotations. We find that the total number and area of magnetic regions on disk
vary with the sunspot cycle. While sunspot numbers are a proxy to the solar
magnetic field, SMART offers a direct diagnostic of the surface magnetic field
and its variation over timescale of hours to years. SMART will form the basis
of the active region extraction and tracking algorithm for the Heliophysics
Integrated Observatory (HELIO)
Inverse problem of photoelastic fringe mapping using neural networks
This paper presents an enhanced technique for inverse analysis of photoelastic fringes using neural networks to determine the applied load. The technique may be useful in whole-field analysis of photoelastic images obtained due to external loading, which may find application in a variety of specialized areas including robotics and biomedical engineering. The presented technique is easy to implement, does not require much computation and can cope well within slight experimental variations. The technique requires image acquisition, filtering and data extraction, which is then fed to the neural network to provide load as output. This technique can be efficiently implemented for determining the applied load in applications where repeated loading is one of the main considerations. The results presented in this paper demonstrate the novelty of this technique to solve the inverse problem from direct image data. It has been shown that the presented technique offers better result for the inverse photoelastic problems than previously published works
Traffic monitoring using image processing : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Information and Telecommunications Engineering at Massey University, Palmerston North, New Zealand
Traffic monitoring involves the collection of data describing the characteristics of vehicles and their movements. Such data may be used for automatic tolls, congestion and incident detection, law enforcement, and road capacity planning etc. With the recent advances in Computer Vision technology, videos can be analysed automatically and relevant information can be extracted for particular applications. Automatic surveillance using video cameras with image processing technique is becoming a powerful and useful technology for traffic monitoring. In this research project, a video image processing system that has the potential to be developed for real-time application is developed for traffic monitoring including vehicle tracking, counting, and classification. A heuristic approach is applied in developing this system. The system is divided into several parts, and several different functional components have been built and tested using some traffic video sequences. Evaluations are carried out to show that this system is robust and can be developed towards real-time applications
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