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Topics in Medical Image Processing and Computational Vision

Overview of attention for book
Cover of 'Topics in Medical Image Processing and Computational Vision'

Table of Contents

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    Book Overview
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    Chapter 1 Learning a Family of Detectors via Multiplicative Kernels
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    Chapter 2 Facial Expression Recognition Using FAPs-Based 3DMMM
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    Chapter 3 SVM Framework for Incorporating Content-Based Image Retrieval and Data Mining into the SBIM Image Manager
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    Chapter 4 Identification of Foliar Diseases in Cotton Crop
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    Chapter 5 Towards Ontological Cognitive System
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    Chapter 6 A Novel Edge Detector Based on Discrete t-norms for Noisy Images
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    Chapter 7 Colour Quantisation as a Preprocessing Step for Image Segmentation
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    Chapter 8 Medical Imaging and Computational Flow Models in Deformable Ducts
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    Chapter 9 Tracking Red Blood Cells in Microchannels: A Comparative Study Between an Automatic and a Manual Method
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    Chapter 10 A Survey for the Automatic Classification of Bone Tissue Images
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    Chapter 11 Colour Video Segmentation for the Quantification of Sweat Dynamic Function
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    Chapter 12 Current Issues in Medical Thermography
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    Chapter 13 Detection of Anatomic Structures in Retinal Images
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    Chapter 14 Database System for Clinical and Computer Assisted Diagnosis of Dermoscopy Images
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    Chapter 15 Segmentation Based Pattern Recognition for Peri-Urban Areas in X Band SAR Images
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    Chapter 16 Improving Flood Risk Management in the City of Lisbon: Developing a Detailed and Updated Map of Imperviousness Using Satellite Imagery
Overall attention for this book and its chapters
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Title
Topics in Medical Image Processing and Computational Vision
Published by
Lecture Notes in Computational Vision and Biomechanics, January 2013
DOI 10.1007/978-94-007-0726-9
ISBNs
978-9-40-070725-2, 978-9-40-070726-9
Editors

João Manuel R.S. Tavares, Renato M. Natal Jorge

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Iran, Islamic Republic of 1 3%
United States 1 3%
Portugal 1 3%
Poland 1 3%
Unknown 25 86%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 21%
Student > Ph. D. Student 6 21%
Other 4 14%
Student > Doctoral Student 3 10%
Student > Bachelor 3 10%
Other 7 24%
Readers by discipline Count As %
Engineering 11 38%
Computer Science 4 14%
Medicine and Dentistry 3 10%
Neuroscience 2 7%
Arts and Humanities 1 3%
Other 7 24%
Unknown 1 3%
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 02 May 2013.
All research outputs
#23,324,704
of 25,992,468 outputs
Outputs from Lecture Notes in Computational Vision and Biomechanics
#1
of 1 outputs
Outputs of similar age
#261,940
of 292,437 outputs
Outputs of similar age from Lecture Notes in Computational Vision and Biomechanics
#1
of 1 outputs
Altmetric has tracked 25,992,468 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1 research outputs from this source. They receive a mean Attention Score of 0.2. This one scored the same or higher as 0 of them.
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 292,437 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them