Computer vision -- ACCV 2014 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised selected papers. Part I /
The five-volume set LNCS 9003--9007 constitutes the thoroughly refereed post-conference proceedings of the 12th Asian Conference on Computer Vision, ACCV 2014, held in Singapore, Singapore, in November 2014. The total of 227 contributions presented in these volumes was carefully reviewed and selecte...
| Corporate Authors: | Asian Conference on Computer Vision Singapore, Singapore) |
|---|---|
| Other Authors: | Asian Conference on Computer Vision, Cremers, Daniel,, Reid, Ian,, Saito, Hideo, Ph. D., Yang, Ming-Hsuan,, SpringerLink (Online service) |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer,
2015.
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| Physical Description: |
1 online resource (xx, 727 pages) : illustrations. |
| Series: |
Lecture notes in computer science ;
9003. LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics. |
| Subjects: |
Table of Contents:
- Intro; Preface; Organization; Contents
- Part I; Recognition; Deep Representations to Model User L̀ikes'; 1 Introduction; 2 Semantic Feature Representation; 3 Proposed Approach; 3.1 User-Specific Feature Selection; 3.2 Learning Deep Bimodal Feature Representation; 4 Experiments; 4.1 Dataset; 4.2 Results and Analysis; 5 Conclusion; References; Submodular Reranking with Multiple Feature Modalities for Image Retrieval; 1 Introduction; 2 Related Works; 3 Submodular Reranking; 3.1 Preliminaries; 3.2 Information Gain with Graphical Models; 3.3 Relative Ranking Consistency; 3.4 Optimization.
- 4 Experiments4.1 Experimental Setting; 4.2 Results Comparisons; 4.3 Parameter Analysis; 5 Conclusions; References; Accurate Scene Text Recognition Based on Recurrent Neural Network; 1 Introduction; 2 Related Work; 2.1 Scene Text Recognition; 2.2 Recurrent Neural Network; 3 Feature Preparation; 4 Recurrent Neural Network Construction; 5 Word Scoring with Lexicon; 6 Experiments and Discussion; 6.1 System Details; 6.2 Experiments on ICDAR and SVT Datasets; 6.3 Discussion; 7 Conclusion; References; Massive City-Scale Surface Condition Analysis Using Ground and Aerial Imagery; 1 Introduction.
- 2 Related Work3 Large-Scale Estimation of Land Surface Condition; 3.1 Debris Detection; 3.2 Projection of Debris Probabilities onto the Ground; 3.3 Integration Using Gaussian Process Regression; 4 Experimental Results; 4.1 Our Data; 4.2 Ablative Analysis; 4.3 Extensions to City-Scale Vegetation Estimation; 5 Conclusion; References; Can Visual Recognition Benefit from Auxiliary Information in Training?; 1 Introduction; 2 Related Work; 3 A Latent Space Model: Addressing Missing-View-in-Test-Data; 4 DCCA: Formulation; 5 DCCA: Solution; 6 Experiments; 6.1 Compared Methods and Datasets.
- 6.2 NYU-Depth-V1-Indoor Scene Dataset6.3 RGBD Object Dataset; 6.4 Multi-Spectral Scene Dataset; 6.5 Discussion; 7 Conclusions; References; Low Rank Representation on Grassmann Manifolds; 1 Introduction; 2 LRR on Grassmann Manifold; 2.1 Low-Rank Representation (LRR); 2.2 LRR on Grassmann Manifolds; 3 Solution to LRR on Grassmann Manifold; 4 Experiments; 4.1 Data Preparation and Experiment Settings; 4.2 MNIST Handwritten Digits Clustering; 4.3 Dynamic Texture Clustering; 5 Conclusion and Future Work; References; Poster Session 1; Learning Detectors Quickly with Stationary Statistics.
- 1 Introduction2 Background; 2.1 Linear Discriminant Analysis with Stationarity; 2.2 Correlation Filters; 2.3 Related Work; 3 Fast Estimation of the Toeplitz Covariance; 4 From Toeplitz to Circulant; 5 Multi-channel, Two-Dimensional Signals; 5.1 Toeplitz Covariance Matrix; 5.2 Circulant Covariance Matrix; 5.3 From Toeplitz to Circulant; 6 Solving Toeplitz Systems; 6.1 Direct Methods; 6.2 Iterative Methods; 6.3 An Effective Heuristic; 7 Empirical Study; 7.1 Detection Performance; 7.2 Time and Memory; 8 Conclusion; References; Age Estimation Based on Complexity-Aware Features; 1 Introduction.