3 research outputs found
Photometric Stereo by Hemispherical Metric Embedding
Photometric Stereo methods seek to reconstruct the 3d shape of an object from
motionless images obtained with varying illumination. Most existing methods
solve a restricted problem where the physical reflectance model, such as
Lambertian reflectance, is known in advance. In contrast, we do not restrict
ourselves to a specific reflectance model. Instead, we offer a method that
works on a wide variety of reflectances. Our approach uses a simple yet
uncommonly used property of the problem - the sought after normals are points
on a unit hemisphere. We present a novel embedding method that maps pixels to
normals on the unit hemisphere. Our experiments demonstrate that this approach
outperforms existing manifold learning methods for the task of hemisphere
embedding. We further show successful reconstructions of objects from a wide
variety of reflectances including smooth, rough, diffuse and specular surfaces,
even in the presence of significant attached shadows. Finally, we empirically
prove that under these challenging settings we obtain more accurate shape
reconstructions than existing methods
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"
This paper documents the release of the ELKI data mining framework, version
0.7.5.
ELKI is an open source (AGPLv3) data mining software written in Java. The
focus of ELKI is research in algorithms, with an emphasis on unsupervised
methods in cluster analysis and outlier detection. In order to achieve high
performance and scalability, ELKI offers data index structures such as the
R*-tree that can provide major performance gains. ELKI is designed to be easy
to extend for researchers and students in this domain, and welcomes
contributions of additional methods. ELKI aims at providing a large collection
of highly parameterizable algorithms, in order to allow easy and fair
evaluation and benchmarking of algorithms.
We will first outline the motivation for this release, the plans for the
future, and then give a brief overview over the new functionality in this
version. We also include an appendix presenting an overview on the overall
implemented functionality
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In
2015 and 2016, we thoroughly study 1,600+ papers in several
conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV