Computer Vision – ECCV 2014: 13th European Conference, by David Fleet, Tomas Pajdla, Bernt Schiele, Tinne Tuytelaars

By David Fleet, Tomas Pajdla, Bernt Schiele, Tinne Tuytelaars

The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed complaints of the thirteenth eu convention on machine imaginative and prescient, ECCV 2014, held in Zurich, Switzerland, in September 2014.
The 363 revised papers provided have been rigorously reviewed and chosen from 1444 submissions. The papers are equipped in topical sections on monitoring and job acceptance; popularity; 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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By David Fleet, Tomas Pajdla, Bernt Schiele, Tinne Tuytelaars

The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed complaints of the thirteenth eu convention on machine imaginative and prescient, ECCV 2014, held in Zurich, Switzerland, in September 2014.
The 363 revised papers provided have been rigorously reviewed and chosen from 1444 submissions. The papers are equipped in topical sections on monitoring and job acceptance; popularity; 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.

Show description

Read or Download Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part II PDF

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Extra resources for Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part II

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The truncated identity matrix) W ← W old H old ← 0 repeat H ← −∇W L(W ) + ητ (H old , W old , W ) Line search along the geodesic γ(t) from W = γ(0) in the direction H to find W ∗ = argmin L(W ) W H old ← H W old ← W W ← W∗ until convergence construct A. Note, however, that our framework is general and also applies to unsupervised or semi-supervised settings. For example, in an unsupervised scen . Solving (7) nario, A could be built from pairwise similarities (distances) on S++ could then be understood as finding a mapping where nearby data pairs on the n m remain close in the induced manifold S++ .

9(a). As seen, our CFAN outperforms DCNN in general. 4 Comparison on LFPW Dataset Furthermore, we evaluate the methods on the Labeled Face Parts in the Wild (LFPW) dataset [3] which is collected from wild condition. LFPW dataset consists of 1132 training images and 300 test images with large variations in pose, expression, illumination, partial occlusion, etc, which makes the facial point detection quite challenging on this dataset. The original URLs of images are provided by [3], but some of them are not available any longer.

As a result, the first few layers of a stacked autoencoder network tend to capture the low-level features such as texture patterns in an image, while the higher layers tend to capture higher-level features containing context information of texture patterns. After the optimization, the prediction of the facial landmarks is achieved as S0 , which is a rough but robust and fast approximation of the ground truth. 3 Local SANs The global SAN described above will give a rough shape estimation S0 of input image x, which is already close to the ground truth locations but not close enough due to the highly complicated variations in expression, pose, identity, etc.

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