9,336 research outputs found
Designing a fruit identification algorithm in orchard conditions to develop robots using video processing and majority voting based on hybrid artificial neural network
The first step in identifying fruits on trees is to develop garden robots for different purposes
such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit
orchards and the unevenness of the various objects throughout it, usage of the controlled conditions
is very difficult. As a result, these operations should be performed in natural conditions, both
in light and in the background. Due to the dependency of other garden robot operations on the
fruit identification stage, this step must be performed precisely. Therefore, the purpose of this
paper was to design an identification algorithm in orchard conditions using a combination of video
processing and majority voting based on different hybrid artificial neural networks. The different
steps of designing this algorithm were: (1) Recording video of different plum orchards at different
light intensities; (2) converting the videos produced into its frames; (3) extracting different color
properties from pixels; (4) selecting effective properties from color extraction properties using
hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority
voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial
neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly
algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third
channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue
(LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation
intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution
and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation
criteria of the classifiers, it was found that the majority voting method had a higher performance.European Union (EU) under Erasmus+ project entitled
“Fostering Internationalization in Agricultural Engineering in Iran and Russia” [FARmER] with grant
number 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JPinfo:eu-repo/semantics/publishedVersio
Towards a human eye behavior model by applying Data Mining Techniques on Gaze Information from IEC
In this paper, we firstly present what is Interactive Evolutionary
Computation (IEC) and rapidly how we have combined this artificial intelligence
technique with an eye-tracker for visual optimization. Next, in order to
correctly parameterize our application, we present results from applying data
mining techniques on gaze information coming from experiments conducted on
about 80 human individuals
The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms
Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version
Expert-Augmented Machine Learning
Machine Learning is proving invaluable across disciplines. However, its
success is often limited by the quality and quantity of available data, while
its adoption by the level of trust that models afford users. Human vs. machine
performance is commonly compared empirically to decide whether a certain task
should be performed by a computer or an expert. In reality, the optimal
learning strategy may involve combining the complementary strengths of man and
machine. Here we present Expert-Augmented Machine Learning (EAML), an automated
method that guides the extraction of expert knowledge and its integration into
machine-learned models. We use a large dataset of intensive care patient data
to predict mortality and show that we can extract expert knowledge using an
online platform, help reveal hidden confounders, improve generalizability on a
different population and learn using less data. EAML presents a novel framework
for high performance and dependable machine learning in critical applications
Coarse-to-Fine Adaptive People Detection for Video Sequences by Maximizing Mutual Information
Applying people detectors to unseen data is challenging since patterns distributions, such
as viewpoints, motion, poses, backgrounds, occlusions and people sizes, may significantly differ
from the ones of the training dataset. In this paper, we propose a coarse-to-fine framework to adapt
frame by frame people detectors during runtime classification, without requiring any additional
manually labeled ground truth apart from the offline training of the detection model. Such adaptation
make use of multiple detectors mutual information, i.e., similarities and dissimilarities of detectors
estimated and agreed by pair-wise correlating their outputs. Globally, the proposed adaptation
discriminates between relevant instants in a video sequence, i.e., identifies the representative frames
for an adaptation of the system. Locally, the proposed adaptation identifies the best configuration
(i.e., detection threshold) of each detector under analysis, maximizing the mutual information to
obtain the detection threshold of each detector. The proposed coarse-to-fine approach does not
require training the detectors for each new scenario and uses standard people detector outputs, i.e.,
bounding boxes. The experimental results demonstrate that the proposed approach outperforms
state-of-the-art detectors whose optimal threshold configurations are previously determined and
fixed from offline training dataThis work has been partially supported by the Spanish government under the project TEC2014-53176-R
(HAVideo
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