By David Fleet, Tomas Pajdla, Bernt Schiele, Tinne Tuytelaars
The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed lawsuits of the thirteenth ecu convention on laptop imaginative and prescient, ECCV 2014, held in Zurich, Switzerland, in September 2014.
The 363 revised papers awarded have been rigorously reviewed and chosen from 1444 submissions. The papers are geared up in topical sections on monitoring and job reputation; attractiveness; studying and inference; constitution from movement and have matching; computational images and low-level imaginative and prescient; imaginative and prescient; segmentation and saliency; context and 3D scenes; movement and 3D scene research; and poster sessions.
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Extra resources for Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part II
We refer to the diﬀerent algorithms evaluated in our experiments as: NN-Stein: Stein metric-based Nearest Neighbor classiﬁer. NN-AIRM: AIRM-based Nearest Neighbor classiﬁer. NN-Stein-ML: Stein metric-based Nearest Neighbor classiﬁer on the lowdimensional SPD manifold obtained with our approach. NN-AIRM-ML: AIRM-based Nearest Neighbor classiﬁer on the low-dimensional SPD manifold obtained with our approach. RSR: Riemannian Sparse Representation . RSR-ML: Riemannian Sparse Representation on the low-dimensional SPD manifold obtained with our approach.
RSR-ML: Riemannian Sparse Representation on the low-dimensional SPD manifold obtained with our approach. In addition to these methods, we also provide the results of the PLS-based Covariance Discriminant Learning (CDL) technique of , as well as of the state-of-the-art baselines of each speciﬁc dataset. 1 Material Categorization For the task of material categorization, we used the UIUC dataset . The UIUC material dataset contains 18 subcategories of materials taken in the wild from four general categories (see Fig.
3%). Finally, note that performing NN-AIRM on the original data required 490s on a 3GHz machine with Matlab. 7s. 2 Action Recognition from Motion Capture Data As a second experiment, we tackled the problem of human action recognition from motion capture sequences using the HDM05 database . This database contains the following 14 actions: ‘clap above head’, ‘deposit ﬂoor’, ‘elbow to knee’, ‘grab high’, ‘hop both legs’, ‘jog’, ‘kick forward’, ‘lie down ﬂoor’, ‘rotate both arms backward’, ‘sit down chair’, ‘sneak’, ‘squat’, ‘stand up lie’ and ‘throw basketball’ (see Fig.
Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part II by David Fleet, Tomas Pajdla, Bernt Schiele, Tinne Tuytelaars