Firm Performance and the Political Economy of Corporate Governance: Survey Evidence for Bulgaria, Hungary, Slovakia and Slovenia

Using survey data for 220 traditional manufacturing firms over 7 years of transition and 4 CEE countries, we find firms that produced for the EU market under planning consistently outperform those that produced for the CMEA market. Within the previously CMEA market, the best firms were selected to outside privatisation and outperformed insider/state owned firms. Outside privatisation was resisted in EU oriented firms and ownership was found to have no effect on performance. We argue that insider/state ownership in previously CMEA and EU markets builds up political support for the market system during its initial stages, ensuring its long-term success.http://deepblue.lib.umich.edu/bitstream/2027.42/39722/3/wp338.pd

Projective rectification from the fundamental matrix

This paper describes a direct, self-contained method for planar image rectification of stereo pairs. The method is based solely on an examination of the Fundamental matrix, where an improved method is given for the derivation of two projective transformations that horizontally align all the epipolar projections. A novel approach is proposed to uniquely optimise each transform in order to minimise perspective distortions. This ensures the rectified images resemble the original images as closely as possible. Detailed results show that the rectification precision exactly matches the estimation error of the Fundamental matrix. In tests the remaining perspective distortion offers on average less than one percent viewpoint distortion. Both these factors offer superior robustness and performance compared with existing techniques

Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification

Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval for a range of applications including surveillance, medical imaging and remote sensing. Deep learning methods have shown impressive results and are now the new state of the art for a wide range of computer vision tasks including image and video recognition and segmentation. In particular, Convolutional Neural Networks (CNNs) have recently proven to be well suited for texture analysis with a design similar to a filter bank approach. In this paper, we develop a new approach to DT analysis based on a CNN method applied on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames and temporal slices extracted from the DT sequences and combine their outputs to obtain a competitive DT classifier. Our results on a wide range of commonly used DT classification benchmark datasets prove the robustness of our approach. Significant improvement of the state of the art is shown on the larger datasets.Comment: 19 pages, 10 figure

Calibration and removal of lateral chromatic aberration in images

This paper addresses the problem of compensating for lateral chromatic aberration in digital images through colour plane realignment. Two main contributions are made: the derivation of a model for lateral chromatic aberration in images, and the subsequent calibration of this model from a single view of a chess pattern. These advances lead to a practical and accurate alternative for the compensation of lateral chromatic aberrations. Experimental results validate the proposed models and calibration algorithm. The effects of colour channel correlations resulting from the camera colour filter array interpolation is examined and found to have a negligible magnitude relative to the chromatic aberration. Results with real data show how the removal of lateral chromatic aberration significantly improves the colour quality of the image

Creating virtual models from uncalibrated camera views

The reconstruction of photorealistic 3D models from camera views is becoming an ubiquitous element in many applications that simulate physical interaction with the real world. In this paper, we present a low-cost, interactive pipeline aimed at non-expert users, that achieves 3D reconstruction from multiple views acquired with a standard digital camera. 3D models are amenable to access through diverse representation modalities that typically imply trade-offs between level of detail, interaction, and computational costs. Our approach allows users to selectively control the complexity of different surface regions, while requiring only simple 2D image editing operations. An initial reconstruction at coarse resolution is followed by an iterative refining of the surface areas corresponding to the selected regions

Colon centreline calculation for CT colonography using optimised 3D opological thinning

CT colonography is an emerging technique for colorectal cancer screening. This technique facilitates noninvasive imaging of the colon interior by generating virtual reality models of the colon lumen. Manual navigation through these models is a slow and tedious process. It is possible to automate navigation by calculating the centreline of the colon lumen. There are numerous well documented approaches for centreline calculation. Many of these techniques have been developed as alternatives to 3D topological thinning which has been discounted by others due to its computationally intensive nature. This paper describes a fully automated, optimised version of 3D topological thinning that has been specifically developed for calculating the centreline of the human colon

Color image segmentation using a spatial k-means clustering algorithm

This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and color since no local constraints are applied to impose spatial continuity. In addition, the initialization of the K-Means algorithm is problematic and usually the initial cluster centers are randomly picked. In this paper we detail the implementation of a novel technique to select the dominant colors from the input image using the information from the color histograms. The main contribution of this work is the generalization of the K-Means algorithm that includes the primary features that describe the color smoothness and texture complexity in the process of pixel assignment. The resulting color segmentation scheme has been applied to a large number of natural images and the experimental data indicates the robustness of the new developed segmentation algorithm