3 research outputs found
A Proximal Algorithm for Sampling
We study sampling problems associated with potentials that lack smoothness.
The potentials can be either convex or non-convex. Departing from the standard
smooth setting, the potentials are only assumed to be weakly smooth or
non-smooth, or the summation of multiple such functions. We develop a sampling
algorithm that resembles proximal algorithms in optimization for this
challenging sampling task. Our algorithm is based on a special case of Gibbs
sampling known as the alternating sampling framework (ASF). The key
contribution of this work is a practical realization of the ASF based on
rejection sampling for both non-convex and convex potentials that are not
necessarily smooth. In almost all the cases of sampling considered in this
work, our proximal sampling algorithm achieves better complexity than all
existing methods.Comment: 26 page
Monocular 3d Object Recognition
Object recognition is one of the fundamental tasks of computer vision. Recent advances in the field enable reliable 2D detections from a single cluttered image. However, many challenges still remain. Object detection needs timely response for real world applications. Moreover, we are genuinely interested in estimating the 3D pose and shape of an object or human for the sake of robotic manipulation and human-robot interaction.
In this thesis, a suite of solutions to these challenges is presented. First, Active Deformable Part Models (ADPM) is proposed for fast part-based object detection. ADPM dramatically accelerates the detection by dynamically scheduling the part evaluations and efficiently pruning the image locations. Second, we unleash the power of marrying discriminative 2D parts with an explicit 3D geometric representation. Several methods of such scheme are proposed for recovering rich 3D information of both rigid and non-rigid objects from monocular RGB images. (1) The accurate 3D pose of an object instance is recovered from cluttered images using only the CAD model. (2) A global optimal solution for simultaneous 2D part localization, 3D pose and shape estimation is obtained by optimizing a unified convex objective function. Both appearance and geometric compatibility are jointly maximized. (3) 3D human pose estimation from an image sequence is realized via an Expectation-Maximization algorithm. The 2D joint location uncertainties are marginalized out during inference and 3D pose smoothness is enforced across frames.
By bridging the gap between 2D and 3D, our methods provide an end-to-end solution to 3D object recognition from images. We demonstrate a range of interesting applications using only a single image or a monocular video, including autonomous robotic grasping with a single image, 3D object image pop-up and a monocular human MoCap system. We also show empirical start-of-art results on a number of benchmarks on 2D detection and 3D pose and shape estimation
Primena funkcionalnih normi za regularizaciju rangiranja nad temporalnim podacima
Quantifying the properties of interest is an important problem in
many domains, e.g., assessing the condition of a patient, estimating the risk of an
investment or relevance of the search result. However, the properties of interest are
often latent and hard to assess directly, making it dicult to obtain classication
or regression labels, which are needed to learn a predictive models from observable
features. In such cases, it is typically much easier to obtain relative comparison of
two instances, i.e. to assess which one is more intense (with respect to the property
of interest). One framework able to learn from such kind of supervised information
is ranking SVM, and it will make a basis of our approach...Kvantikovanje osobina (karakteristika) od interesa je vazan problem
u mnogim domenima, npr. utvrdivanje tezine bolesti kod pacijenata, ocena rizika
investicije ili relevantnost vracenih rezultata pretrage. Medutim, osobine od interesa
su cesto latentne i tesko se mogu izmeriti direktno, sto otezava dobijanje klasikacionih
oznaka (labela) ili ciljeva za regresiju, koji su potrebni za ucenje prediktivnih
modela iz merljivih karakteristika. U takvim slucajevima obicno je mnogo lakse
pribaviti relativno poredenje dva slucaja, tj. proceniti koji od dva je intenzivniji (iz
ugla karakteristike od interesa). Jedna klasa algoritama koji mogu uciti iz ovakvih
informacija je SVM za rangiranje i on ce biti osnova ovde predlozenog pristupa..