Computer Vision -- ECCV 2014
Springer International Publishing (Verlag)
978-3-319-10589-5 (ISBN)
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.
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.
Erscheint lt. Verlag | 22.9.2014 |
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Reihe/Serie | Image Processing, Computer Vision, Pattern Recognition, and Graphics | Lecture Notes in Computer Science |
Zusatzinfo | XXVIII, 853 p. 361 illus. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Gewicht | 1317 g |
Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
Mathematik / Informatik ► Informatik ► Software Entwicklung | |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
Schlagworte | 3D • activity recognition and understanding • Artificial Intelligence • computational photography • Computer Graphics • computer vision • Computervision • computer vision problems • face recognition • image and video acquisition • image manipulation • Image Processing • Image Segmentation • interest point and salient region detections • machine learning • Matching • Motion Capture • Object detection • pattern recognition • reconstruction • Scene Understanding |
ISBN-10 | 3-319-10589-2 / 3319105892 |
ISBN-13 | 978-3-319-10589-5 / 9783319105895 |
Zustand | Neuware |
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