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Computer Vision -- ACCV 2014

Overview of attention for book
Cover of 'Computer Vision -- ACCV 2014'

Table of Contents

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    Book Overview
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    Chapter 1 Multi-view Geometry Compression
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    Chapter 2 Camera Calibration Based on the Common Self-polar Triangle of Sphere Images
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    Chapter 3 Multi-scale Tetrahedral Fusion of a Similarity Reconstruction and Noisy Positional Measurements
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    Chapter 4 DEPT: Depth Estimation by Parameter Transfer for Single Still Images
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    Chapter 5 Object Ranking on Deformable Part Models with Bagged LambdaMART
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    Chapter 6 Representation Learning with Smooth Autoencoder
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    Chapter 7 Single Image Smoke Detection
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    Chapter 8 Adaptive Sparse Coding for Painting Style Analysis
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    Chapter 9 Efficient Image Detail Mining
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    Chapter 10 Accuracy and Specificity Trade-off in $$k$$ -nearest Neighbors Classification
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    Chapter 11 Multi-view Point Cloud Registration Using Affine Shape Distributions
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    Chapter 12 Part Detector Discovery in Deep Convolutional Neural Networks
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    Chapter 13 Performance Evaluation of 3D Local Feature Descriptors
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    Chapter 14 Scene Text Detection Based on Robust Stroke Width Transform and Deep Belief Network
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    Chapter 15 Cross-Modal Face Matching: Beyond Viewed Sketches
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    Chapter 16 3D Aware Correction and Completion of Depth Maps in Piecewise Planar Scenes
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    Chapter 17 Regularity Guaranteed Human Pose Correction
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    Chapter 18 Accelerated Kmeans Clustering Using Binary Random Projection
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    Chapter 19 Divide and Conquer: Efficient Large-Scale Structure from Motion Using Graph Partitioning
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    Chapter 20 A Homography Formulation to the 3pt Plus a Common Direction Relative Pose Problem
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    Chapter 21 MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation
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    Chapter 22 Accelerating Cost Volume Filtering Using Salient Subvolumes and Robust Occlusion Handling
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    Chapter 23 3D Human Pose Estimation from Monocular Images with Deep Convolutional Neural Network
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    Chapter 24 Plant Leaf Identification via a Growing Convolution Neural Network with Progressive Sample Learning
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    Chapter 25 Understanding Convolutional Neural Networks in Terms of Category-Level Attributes
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    Chapter 26 Robust Scene Classification with Cross-Level LLC Coding on CNN Features
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    Chapter 27 A Graphical Model for Rapid Obstacle Image-Map Estimation from Unmanned Surface Vehicles
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    Chapter 28 On the Performance of Pose-Based RGB-D Visual Navigation Systems
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    Chapter 29 Elastic Shape Analysis of Boundaries of Planar Objects with Multiple Components and Arbitrary Topologies
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    Chapter 30 A Minimal Solution to Relative Pose with Unknown Focal Length and Radial Distortion
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    Chapter 31 Simultaneous Entire Shape Registration of Multiple Depth Images Using Depth Difference and Shape Silhouette
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    Chapter 32 Joint Camera Pose Estimation and 3D Human Pose Estimation in a Multi-camera Setup
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    Chapter 33 Singly-Bordered Block-Diagonal Form for Minimal Problem Solvers
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    Chapter 34 Stereo Fusion Using a Refractive Medium on a Binocular Base
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    Chapter 35 Saliency Detection via Nonlocal $$L_{0}$$ Minimization
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    Chapter 36 $$N^4$$ -Fields: Neural Network Nearest Neighbor Fields for Image Transforms
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    Chapter 37 Super-Resolution Using Sub-Band Self-Similarity
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    Chapter 38 Raindrop Detection and Removal from Long Range Trajectories
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    Chapter 39 Interest Points via Maximal Self-Dissimilarities
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    Chapter 40 Improving Local Features by Dithering-Based Image Sampling
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    Chapter 41 Sparse Kernel Learning for Image Set Classification
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    Chapter 42 Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning
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    Chapter 43 Texture Classification Using Dense Micro-block Difference (DMD)
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    Chapter 44 Nuclear- $$L_1$$ Norm Joint Regression for Face Reconstruction and Recognition
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    Chapter 45 Segmentation of X-ray Images by 3D-2D Registration Based on Multibody Physics
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    Chapter 46 View-Adaptive Metric Learning for Multi-view Person Re-identification
Overall attention for this book and its chapters
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

2 patents


2 Dimensions

Readers on

157 Mendeley
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Computer Vision -- ACCV 2014
Published by
Lecture notes in computer science, January 2015
DOI 10.1007/978-3-319-16808-1
978-3-31-916807-4, 978-3-31-916808-1

Cremers, Daniel, Reid, Ian, Saito, Hideo, Yang, Ming-Hsuan

Mendeley readers

The data shown below were compiled from readership statistics for 157 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Algeria 1 <1%
Netherlands 1 <1%
Unknown 155 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 20%
Student > Master 16 10%
Student > Bachelor 2 1%
Student > Doctoral Student 2 1%
Lecturer 2 1%
Other 3 2%
Unknown 101 64%
Readers by discipline Count As %
Computer Science 36 23%
Engineering 13 8%
Mathematics 2 1%
Arts and Humanities 1 <1%
Design 1 <1%
Other 0 0%
Unknown 104 66%

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 19 October 2017.
All research outputs
of 12,009,062 outputs
Outputs from Lecture notes in computer science
of 7,214 outputs
Outputs of similar age
of 267,084 outputs
Outputs of similar age from Lecture notes in computer science
of 111 outputs
Altmetric has tracked 12,009,062 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,214 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 58% 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 267,084 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 64% of its contemporaries.
We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.