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Image Analysis and Recognition

Overview of attention for book
Cover of 'Image Analysis and Recognition'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Weighted Fisher Discriminant Analysis in the Input and Feature Spaces
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    Chapter 2 Backprojection for Training Feedforward Neural Networks in the Input and Feature Spaces
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    Chapter 3 Parallel Implementation of the DRLSE Algorithm
  5. Altmetric Badge
    Chapter 4 A Multiscale Energy-Based Time-Domain Approach for Interference Detection in Non-stationary Signals
  6. Altmetric Badge
    Chapter 5 SMAT: Smart Multiple Affinity Metrics for Multiple Object Tracking
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    Chapter 6 Combining Mixture Models and Spectral Clustering for Data Partitioning
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    Chapter 7 MSPNet: Multi-level Semantic Pyramid Network for Real-Time Object Detection
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    Chapter 8 Multi-domain Document Layout Understanding Using Few-Shot Object Detection
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    Chapter 9 Object Tracking Through Residual and Dense LSTMs
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    Chapter 10 Theoretical Insights into the Use of Structural Similarity Index in Generative Models and Inferential Autoencoders
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    Chapter 11 Efficient Prediction of Gold Prices Using Hybrid Deep Learning
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    Chapter 12 Exploring Information Theory and Gaussian Markov Random Fields for Color Texture Classification
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    Chapter 13 Anomaly Detection for Images Using Auto-encoder Based Sparse Representation
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    Chapter 14 A Framework for Fusion of T1-Weighted and Dynamic MRI Sequences
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    Chapter 15 Contributions to a Quantitative Unsupervised Processing and Analysis of Tongue in Ultrasound Images
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    Chapter 16 Improving Multiple Sclerosis Lesion Boundaries Segmentation by Convolutional Neural Networks with Focal Learning
  18. Altmetric Badge
    Chapter 17 B-Mode Ultrasound Breast Anatomy Segmentation
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    Chapter 18 Enhancing the Prediction of Lung Cancer Survival Rates Using 2D Features from 3D Scans
  20. Altmetric Badge
    Chapter 19 Lesion Localization in Paediatric Epilepsy Using Patch-Based Convolutional Neural Network
  21. Altmetric Badge
    Chapter 20 Deep Learning Models for Segmentation of Mobile-Acquired Dermatological Images
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    Chapter 21 Semi-automatic Tool to Identify Heterogeneity Zones in LGE-CMR and Incorporate the Result into a 3D Model of the Left Ventricle
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    Chapter 22 A Deep Learning Based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images
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    Chapter 23 Studying the Effect of Digital Stain Separation of Histopathology Images on Image Search Performance
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    Chapter 24 Generalized Multiple Instance Learning for Cancer Detection in Digital Histopathology
  26. Altmetric Badge
    Chapter 25 A Multi-dataset Approach for DME Risk Detection in Eye Fundus Images
  27. Altmetric Badge
    Chapter 26 Enhancement of Retinal Fundus Images via Pixel Color Amplification
  28. Altmetric Badge
    Chapter 27 Wavelet-Based Retinal Image Enhancement
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    Chapter 28 An Interpretable Data-Driven Score for the Assessment of Fundus Images Quality
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    Chapter 29 Optic Disc and Fovea Detection in Color Eye Fundus Images
  31. Altmetric Badge
    Chapter 30 The Effect of Menopause on the Sexual Dimorphism in the Human Retina – Texture Analysis of Optical Coherence Tomography Data
  32. Altmetric Badge
    Chapter 31 Deep Retinal Diseases Detection and Explainability Using OCT Images
  33. Altmetric Badge
    Chapter 32 An Automated Workflow for Lung Nodule Follow-Up Recommendation Using Deep Learning
  34. Altmetric Badge
    Chapter 33 Pulmonary-Nodule Detection Using an Ensemble of 3D SE-ResNet18 and DPN68 Models
  35. Altmetric Badge
    Chapter 34 3DCNN for Pulmonary Nodule Segmentation and Classification
  36. Altmetric Badge
    Chapter 35 Residual Networks for Pulmonary Nodule Segmentation and Texture Characterization
  37. Altmetric Badge
    Chapter 36 Automatic Lung Cancer Follow-Up Recommendation with 3D Deep Learning
  38. Altmetric Badge
    Chapter 37 Deep Residual 3D U-Net for Joint Segmentation and Texture Classification of Nodules in Lung
Attention for Chapter 26: Enhancement of Retinal Fundus Images via Pixel Color Amplification
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

6 tweeters

Readers on

4 Mendeley
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Chapter title
Enhancement of Retinal Fundus Images via Pixel Color Amplification
Chapter number 26
Book title
Image Analysis and Recognition
Published in
arXiv, June 2020
DOI 10.1007/978-3-030-50516-5_26
Book ISBNs
978-3-03-050515-8, 978-3-03-050516-5

Alex Gaudio, Asim Smailagic, Aurélio Campilho

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 75%
Student > Ph. D. Student 1 25%
Readers by discipline Count As %
Computer Science 2 50%
Engineering 2 50%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 30 July 2020.
All research outputs
of 16,682,934 outputs
Outputs from arXiv
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Outputs of similar age
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Outputs of similar age from arXiv
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Altmetric has tracked 16,682,934 research outputs across all sources so far. This one is in the 45th percentile – i.e., 45% of other outputs scored the same or lower than it.
So far Altmetric has tracked 668,016 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done well, scoring higher than 76% of its peers.
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 293,749 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 39,035 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.