Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling's Computer Vision – ECCV 2016: 14th European Conference, PDF

By Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

ISBN-10: 3319464744

ISBN-13: 9783319464749

ISBN-10: 3319464752

ISBN-13: 9783319464756

The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed complaints of the 14th eu convention on machine imaginative and prescient, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016.
The 415 revised papers provided have been conscientiously reviewed and chosen from 1480 submissions. The papers hide all features of desktop imaginative and prescient and development acceptance reminiscent of 3D computing device imaginative and prescient; computational images, sensing and exhibit; face and gesture; low-level imaginative and prescient and snapshot processing; movement and monitoring; optimization tools; physics-based imaginative and prescient, photometry and shape-from-X; attractiveness: detection, categorization, indexing, matching; segmentation, grouping and form illustration; statistical tools and studying; video: occasions, actions and surveillance; purposes. they're equipped in topical sections on detection, popularity and retrieval; scene figuring out; optimization; photo and video processing; studying; motion task and monitoring; 3D; and nine poster sessions.

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Extra resources for Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II

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However, we found that this whitening process, whose mean vector and covariance matrix are estimated from the random sampled images from the same dataset (inevitably using images from other categories), is crucial for the performance of their algorithm. 4 %. Table 1. Comparison to image co-localization approaches on the VOC 2007 dataset in terms of CorLoc metric [7]. 9 To further verify the effectiveness of our approach, we now present an evaluation on the VOC 2012 dataset [8] which has twice the number of images of VOC 2007.

The approach that we propose here, however, is the first to systematically leverage the idea of jointly performing object detection and localization for co-localizing common objects in images. 3 Approach We give an overview of our image co-localization framework in Fig. 1. The input to our framework is a set of N images I = {I1 , I2 , . . , aeroplane), and we aim to annotate the location of common object Fig. 1. An overview of our image co-localization framework. (a) The input of our system is a set of images contains a common object category (here, aeroplane).

2. Learning Visual Representations via Physical Interactions Successful grasps 7 Unsuccessful grasps Fig. 2. Examples of successful (left) and unsuccesful grasps (right). We use a patch based representation: given an input patch we predict 18-dim vector which represents whether the center location of the patch is graspable at 0◦ , 10◦ , . . 170◦ . Grasp Prediction Formulation: The grasp prediction problem can be formulated as finding a successful grasp configuration (xS , yS , θS ) given an image of an object I.

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Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II by Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

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