4,765 research outputs found
Learning Rank Reduced Interpolation with Principal Component Analysis
In computer vision most iterative optimization algorithms, both sparse and
dense, rely on a coarse and reliable dense initialization to bootstrap their
optimization procedure. For example, dense optical flow algorithms profit
massively in speed and robustness if they are initialized well in the basin of
convergence of the used loss function. The same holds true for methods as
sparse feature tracking when initial flow or depth information for new features
at arbitrary positions is needed. This makes it extremely important to have
techniques at hand that allow to obtain from only very few available
measurements a dense but still approximative sketch of a desired 2D structure
(e.g. depth maps, optical flow, disparity maps, etc.). The 2D map is regarded
as sample from a 2D random process. The method presented here exploits the
complete information given by the principal component analysis (PCA) of that
process, the principal basis and its prior distribution. The method is able to
determine a dense reconstruction from sparse measurement. When facing
situations with only very sparse measurements, typically the number of
principal components is further reduced which results in a loss of
expressiveness of the basis. We overcome this problem and inject prior
knowledge in a maximum a posterior (MAP) approach. We test our approach on the
KITTI and the virtual KITTI datasets and focus on the interpolation of depth
maps for driving scenes. The evaluation of the results show good agreement to
the ground truth and are clearly better than results of interpolation by the
nearest neighbor method which disregards statistical information.Comment: Accepted at Intelligent Vehicles Symposium (IV), Los Angeles, USA,
June 201
Verifiability in Markets for Credence Goods
Theory predicts that efficiency prevails on credence goods markets if customers are able to verify which quality they receive from an expert seller. In a series of experiments with endogenous prices we observe that variability fails to result in efficient provision behavior and leads to very similar results as a setting without variability. Some sellers always provide appropriate treatment even if own money maximization calls for over- or undertreatment. Overall our endogenous price-results suggests that both inequality aversion and a taste for efficiency play an important role for experts provision behavior. We contrast the implications of those two motivations theoretically and discriminate between them empirically using a ïżœxed-price design. We then classify experimental experts according to their provision behavior
The Economics of Credence Goods: On the Role of Liability, Verifiability, Reputation and Competition
Credence goods markets are characterized by asymmetric information between sellers and consumers that may give rise to inefficiencies, such as under- and overtreatment or market break-down. We study in a large experiment with 936 participants the determinants for efficiency in credence goods markets. While theory predicts that either liability or verifiability yields efficiency, we find that liability has a crucial, but verifiability only a minor effect. Allowing sellers to build up reputation has little influence, as predicted. Seller competition drives down prices and yields maximal trade, but does not lead to higher efficiency as long as liability is violated.
The Impact of Distributional Preferences on (Experimental) Markets for Expert Services
Credence goods markets suffer from inefficiencies arising from informational asymmetries between expert sellers and customers. While standard theory predicts that inefficiencies disappear if customers can verify the quality received, verifiability fails to yield efficiency in experiments with endogenous prices. We identify heterogeneous distributional preferences as the main cause and design a parsimonious experiment with exogenous prices that allows classifying experts as either selfish, efficiency loving, inequality averse, inequality loving or competitive. Results show that most subjects exhibit non-standard distributional preferences, among which efficiency-loving and inequality aversion are most frequent. We discuss implications for institutional design and agent selection in credence goods markets.distributional preferences, credence goods, verifiability, experiment
The Economics of Credence Goods: On the Role of Liability, Verifiability, Reputation and Competition
Credence goods markets are characterized by asymmetric information between sellers and consumers that may give rise to inefficiencies, such as under- and overtreatment or market break-down. We study in a large experiment with 936 participants the determinants for efficiency in credence goods markets. While theory predicts that either liability or verifiability yields efficiency, we find that liability has a crucial, but verifiability only a minor effect. Allowing sellers to build up reputation has little influence, as predicted. Seller competition drives down prices and yields maximal trade, but does not lead to higher efficiency as long as liability is violated.competition, reputation, verifiability, liability, experiment, credence goods
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