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Signal and Image Analysis for Biomedical and Life Sciences

Overview of attention for book
Cover of 'Signal and Image Analysis for Biomedical and Life Sciences'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Visual Analytics of Signalling Pathways Using Time Profiles
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    Chapter 2 Modeling of testosterone regulation by pulse-modulated feedback.
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    Chapter 3 Hybrid Algorithms for Multiple Change-Point Detection in Biological Sequences
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    Chapter 4 Stochastic anomaly detection in eye-tracking data for quantification of motor symptoms in Parkinson's disease.
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    Chapter 5 Identification of the Reichardt Elementary Motion Detector Model
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    Chapter 6 Multi-complexity Ensemble Measures for Gait Time Series Analysis: Application to Diagnostics, Monitoring and Biometrics.
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    Chapter 7 Development of a motion capturing and load analyzing system for caregivers aiding a patient to sit up in bed.
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    Chapter 8 Classifying Epileptic EEG Signals with Delay Permutation Entropy and Multi-scale K-Means.
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    Chapter 9 Tracking of EEG Activity Using Motion Estimation to Understand Brain Wiring.
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    Chapter 10 Towards Automated Quantitative Vasculature Understanding via Ultra High-Resolution Imagery.
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    Chapter 11 Cloud based toolbox for image analysis, processing and reconstruction tasks.
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    Chapter 12 Pollen image classification using the classifynder system: algorithm comparison and a case study on new zealand honey.
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    Chapter 13 Digital image processing and analysis for activated sludge wastewater treatment.
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    Chapter 14 A Complete System for 3D Reconstruction of Roots for Phenotypic Analysis
Attention for Chapter 14: A Complete System for 3D Reconstruction of Roots for Phenotypic Analysis
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Chapter title
A Complete System for 3D Reconstruction of Roots for Phenotypic Analysis
Chapter number 14
Book title
Signal and Image Analysis for Biomedical and Life Sciences
Published in
Advances in experimental medicine and biology, October 2014
DOI 10.1007/978-3-319-10984-8_14
Pubmed ID
Book ISBNs
978-3-31-910983-1, 978-3-31-910984-8
Authors

Kumar, Pankaj, Cai, Jinhai, Miklavcic, Stanley J., Pankaj Kumar, Jinhai Cai, Stanley J. Miklavcic

Editors

Sun, Changming, Bednarz, Tomasz, Pham, Tuan D., Vallotton, Pascal, Wang, Dadong

Abstract

Here we present a complete system for 3D reconstruction of roots grown in a transparent gel medium or washed and suspended in water. The system is capable of being fully automated as it is self calibrating. The system starts with detection of root tips in root images from an image sequence generated by a turntable motion. Root tips are detected using the statistics of Zernike moments on image patches centred on high curvature points on root boundary and Bayes classification rule. The detected root tips are tracked in the image sequence using a multi-target tracking algorithm. Conics are fitted to the root tip trajectories using a novel ellipse fitting algorithm which weighs the data points by its eccentricity. The conics projected from the circular trajectory have a complex conjugate intersection which are image of the circular points. Circular points constraint the image of the absolute conics which are directly related to the internal parameters of the camera. The pose of the camera is computed from the image of the rotation axis and the horizon. The silhouettes of the roots and camera parameters are used to reconstruction the 3D voxel model of the roots. We show the results of real 3D reconstruction of roots which are detailed and realistic for phenotypic analysis.

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 2 12%
Student > Master 2 12%
Researcher 2 12%
Student > Ph. D. Student 2 12%
Student > Bachelor 1 6%
Other 2 12%
Unknown 6 35%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 24%
Computer Science 3 18%
Unspecified 2 12%
Mathematics 1 6%
Neuroscience 1 6%
Other 0 0%
Unknown 6 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 April 2015.
All research outputs
#18,382,900
of 22,769,322 outputs
Outputs from Advances in experimental medicine and biology
#3,304
of 4,929 outputs
Outputs of similar age
#182,716
of 255,751 outputs
Outputs of similar age from Advances in experimental medicine and biology
#39
of 95 outputs
Altmetric has tracked 22,769,322 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,929 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 255,751 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 95 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.