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 �xed-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