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Computer Vision/Computer Graphics Collaboration Techniques

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Cover of 'Computer Vision/Computer Graphics Collaboration Techniques'

Table of Contents

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    Book Overview
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    Chapter 1 Bundle Adjustment for Stereoscopic 3D
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    Chapter 2 3D Modeling of Haussmannian Facades
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    Chapter 3 Lung Cancer Detection from Thoracic CT Scans Using 3-D Deformable Models Based on Statistical Anatomical Analysis
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    Chapter 4 An Evaluation on Estimators for Stochastic and Heuristic Based Cost Functions Using the Epipolar-Constraint
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    Chapter 5 Facial Movement Based Recognition
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    Chapter 6 Towards Temporally-Coherent Video Matting
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    Chapter 7 Leaf Segmentation and Tracking Using Probabilistic Parametric Active Contours
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    Chapter 8 Gallbladder Segmentation from 2–D Ultrasound Images Using Active Contour Models and Gradient Vector Flow
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    Chapter 9 Multi-view Alpha Matte for Free Viewpoint Rendering
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    Chapter 10 Using Spatially Distributed Patterns for Multiple View Camera Calibration
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    Chapter 11 Optimal Gabor Filters and Haralick Features for the Industrial Polarization Imaging
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    Chapter 12 Joint Histogram Modelling for Segmentation Multiple Sclerosis Lesions
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    Chapter 13 Surface Reconstruction of Scenes Using a Catadioptric Camera
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    Chapter 14 Creating Chinkin Works in the Virtual Space
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    Chapter 15 Real-Time Multi-view Human Motion Tracking Using 3D Model and Latency Tolerant Parallel Particle Swarm Optimization
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    Chapter 16 Snap Image Composition
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    Chapter 17 Towards the Automatic Generation of 3D Photo-Realistic Avatars Using 3D Scanned Data
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    Chapter 18 Content Based Image Retrieval Using Visual-Words Distribution Entropy
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    Chapter 19 Video Summarization Using a Self-Growing and Self-Organized Neural Gas Network
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    Chapter 20 Real-Time Upper-Body Human Pose Estimation Using a Depth Camera
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    Chapter 21 Image Matting with Transductive Inference
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    Chapter 22 A New Buckling Model for Cloth Simulation
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    Chapter 23 A Public System for Image Based 3D Model Generation
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    Chapter 24 A Novel Approach to Image Assessment by Seeking Unification of Subjective and Objective Criteria Based on Supervised Learning
Attention for Chapter 3: Lung Cancer Detection from Thoracic CT Scans Using 3-D Deformable Models Based on Statistical Anatomical Analysis
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Citations

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Chapter title
Lung Cancer Detection from Thoracic CT Scans Using 3-D Deformable Models Based on Statistical Anatomical Analysis
Chapter number 3
Book title
Computer Vision/Computer Graphics Collaboration Techniques
Published in
Lecture notes in computer science, October 2011
DOI 10.1007/978-3-642-24136-9_3
Book ISBNs
978-3-64-224135-2, 978-3-64-224136-9
Authors

Hotaka Takizawa, Shigeyuki Ishii

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 67%
Researcher 1 33%
Readers by discipline Count As %
Medicine and Dentistry 2 67%
Computer Science 1 33%
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 10 June 2014.
All research outputs
#20,231,392
of 22,757,090 outputs
Outputs from Lecture notes in computer science
#6,982
of 8,126 outputs
Outputs of similar age
#125,246
of 136,209 outputs
Outputs of similar age from Lecture notes in computer science
#52
of 68 outputs
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So far Altmetric has tracked 8,126 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 1st percentile – i.e., 1% 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 136,209 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 68 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.