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 Samuel Schulter

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Average citations per article1.71
Citation Count12
Publication count7
Publication years2011-2016
Available for download1
Average downloads per article133.00
Downloads (cumulative)133
Downloads (12 Months)78
Downloads (6 Weeks)6
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1 published by ACM
September 2016 Journal on Computing and Cultural Heritage (JOCCH): Volume 9 Issue 4, December 2016
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 6,   Downloads (12 Months): 78,   Downloads (Overall): 133

Full text available: PDFPDF
Petroglyphs (rock engravings) have been pecked and engraved by humans into natural rock surfaces thousands of years ago and are among the oldest artifacts that document early human life and culture. Some of these rock engravings have survived until the present and serve today as a unique document of ancient ...
Keywords: 3D rock-art analysis, surface texture analysis, 3D segmentation, semi-automatic segmentation

2
December 2015 ICCV '15: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV)
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 0

Single image super-resolution is an important task in the field of computer vision and finds many practical applications. Current state-of-the-art methods typically rely on machine learning algorithms to infer a mapping from low-to high-resolution images. These methods use a single fixed blur kernel during training and, consequently, assume the exact ...

3
June 2014 CVPR '14: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 0

In this paper, we present a novel object detection approach that is capable of regressing the aspect ratio of objects. This results in accurately predicted bounding boxes having high overlap with the ground truth. In contrast to most recent works, we employ a Random Forest for learning a template-based model ...

4
December 2013 ICCV '13: Proceedings of the 2013 IEEE International Conference on Computer Vision
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 1

We present Alternating Regression Forests (ARFs), a novel regression algorithm that learns a Random Forest by optimizing a global loss function over all trees. This interrelates the information of single trees during the training phase and results in more accurate predictions. ARFs can minimize any differentiable regression loss without sacrificing ...
Keywords: Random Forest, Regression, Object Detection, Head Pose Estimation

5
June 2013 CVPR '13: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 4

This paper introduces a novel classification method termed Alternating Decision Forests (ADFs), which formulates the training of Random Forests explicitly as a global loss minimization problem. During training, the losses are minimized via keeping an adaptive weight distribution over the training samples, similar to Boosting methods. In order to keep ...
Keywords: Random Forests, Boosting, Global Loss

6
August 2011 AVSS '11: Proceedings of the 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 0

Unusual event detection, i.e., identifying (previously unseen) rare/critical events, has become one of the major challenges in visual surveillance. The main solution for this problem is to describe local or global normalness and to report events that do not fit to the estimated models. The majority of existing approaches, however, ...

7
June 2011 CVPR '11: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 5

Current state-of-the-art object classification systems are trained using large amounts of hand-labeled images. In this paper, we present an approach that shows how to use unlabeled video sequences, comprising weakly-related object categories towards the target class, to learn better classifiers for tracking and detection. The underlying idea is to exploit ...
Keywords: visual classifiers, unlabeled weakly-related videos, object classification systems, hand-labeled images, unlabeled video sequences, weakly-related object categories, space-time consistency, dense optical flow, part-based random forests, natural transformations, video forests, tracking-by-detection approach, general codebook



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