Full Description
The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014.
The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.
Contents
Visual Tracking by Sampling Tree-Structured Graphical Models.- Tracking Interacting Objects Optimally Using Integer Programming.- Learning Latent Constituents for Recognition of Group Activities in Video.- Large-Scale Object Classification Using Label Relation Graphs.- 30Hz Object Detection with DPM V5.- Knowing a Good HOG Filter when You See It: Efficient Selection of Filters for Detection.- Linking People in Videos with "Their" Names Using Coreference Resolution.- Optimal Essential Matrix Estimation via Inlier-Set Maximization.- UPnP: An Optimal O(n) Solution to the Absolute Pose Problem with Universal Applicability.- 3D Reconstruction of Dynamic Textures in Crowd Sourced Data.- 3D Interest Point Detection via Discriminative Learning.- Pose Locality Constrained Representation for 3D Human Pose Reconstruction.- Synchronization of Two Independently Moving Cameras without Feature Correspondences.- Multi Focus Structured Light for ecovering Scene Shape and Global Illumination.- Coplanar Common Points in Non-centric Cameras.- SRA: Fast Removal of General Multipath for ToF Sensors.- Sub-pixel Layout for Super-Resolution with Images in the Octic Group.- Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition.- Read My Lips: Continuous Signer Independent Weakly Supervised Viseme Recognition.- Multilinear Wavelets: A Statistical Shape Space for Human Faces.- Distance Estimation of an Unknown Person from a Portrait.- Probabilistic Temporal Head Pose Estimation Using a Hierarchical Graphical Model.- Description-Discrimination Collaborative Tracking.- Online, Real-Time Tracking Using a Category-to-Individual Detector.- Robust Visual Tracking with Double Bounding Box Model.- Tractable and Reliable Registration of 2D Point Sets.- Graduated Consistency-Regularized Optimization for Multi-graph Matching.- Optical Flow Estimation with Channel Constancy.- Non-local Total Generalized Variation for Optical Flow Estimation.- Learning Brightness Transfer Functions for the Joint Recovery of Illumination Changes and Optical Flow.- Hipster Wars: Discovering Elements of Fashion Styles.- From Low-Cost Depth Sensors to CAD: Cross-Domain 3D Shape Retrieval via Regression Tree Fields.- Fast and Accurate Texture Recognition with Multilayer Convolution and Multifractal Analysis.- Learning to Rank 3D Features.- Salient Color Names for Person Re-identification.- Learning Discriminative and Shareable Features for Scene Classification.- Image Retrieval and Ranking via Consistently Reconstructing Multi-attribute Queries.- Neural Codes for Image Retrieval.- Architectural Style Classification Using Multinomial Latent Logistic Regression.- Instance Segmentation of Indoor Scenes Using a Coverage Loss.- Superpixel Graph Label Transfer with Learned Distance Metric.- Precision-Recall-Classification Evaluation Framework: Application to Depth Estimation on Single Images.- A Multi-stage Approach to Curve Extraction.- Geometry Driven Semantic Labeling of Indoor Scenes.- A Novel Topic-Level Random Walk Framework for Scene Image Co-segmentation.- Surface Matching and Registration by Landmark Curve-Driven Canonical Quasiconformal Mapping.- Activity Group Localization by Modeling the Relations among Participants.- Finding Coherent Motions and Semantic Regions in Crowd Scenes: A Diffusion and Clustering Approach.- Semantic Aware Video Transcription Using Random Forest Classifiers.- Ranking Domain-Specific Highlights by Analyzing Edited Videos.- A Multi-transformational Model for Background Subtraction with Moving Cameras.- Visualizing and Understanding Convolutional Networks.- Part-Based R-CNNs for Fine-Grained Category Detection.
