Making The Pact More Flexible: Can It Lead to Less Flexible Fiscal Policies?

One of the most often discussed features of the Stability and Growth Pact is the rigidity of its 3% deficit rule. In the recent time several reform proposals aim at alleviating the rule in order to allow more room for the automatic stabilizers to operate. As the 3% limit became in the recent years the only binding (at least partially) rule of the Pact’s framework, such a reform is likely to cause even further deterioration of the member countries’ fiscal balances. The empirical evidence presented in the paper shows that in the past lowering the structural budget surplus had a strong negative impact on a degree of anti-cyclical fiscal stabilization. This, in turn, suggests that the Pact’s reform, through higher structural deficits, is likely to decrease, rather then increase, the scope of anti-cyclical fiscal actions undertaken by the EMU member countries.fiscal policy, stabilization policy, fiscal rules, Stability and Growth Pact

Convolutional Neural Networks for Counting Fish in Fisheries Surveillance Video

We present a computer vision tool that analyses video from a CCTV system installed on fishing trawlers to monitor discarded fish catch. The system aims to support expert observers who review the footage and verify numbers, species and sizes of discarded fish. The operational environment presents a significant challenge for these tasks. Fish are processed below deck under fluorescent lights, they are randomly oriented and there are multiple occlusions. The scene is unstructured and complicated by the presence of fishermen processing the catch. We describe an approach to segmenting the scene and counting fish that exploits the $N^4$-Fields algorithm. We performed extensive tests of the algorithm on a data set comprising 443 frames from 6 belts. Results indicate the relative count error (for individual fish) ranges from 2\% to 16\%. We believe this is the first system that is able to handle footage from operational trawlers

The alternating least squares technique for nonuniform intensity color correction

Color correction involves mapping device RGBs to display counterparts or to corresponding XYZs. A popular methodology is to take an image of a color chart and then solve for the best 3 × 3 matrix that maps the RGBs to the corresponding known XYZs. However, this approach fails at times when the intensity of the light varies across the chart. This variation needs to be removed before estimating the correction matrix. This is typically achieved by acquiring an image of a uniform gray chart in the same location, and then dividing the color checker image by the gray-chart image. Of course, taking images of two charts doubles the complexity of color correction. In this article, we present an alternative color correction algorithm that simultaneously estimates the intensity variation and the 3 × 3 transformation matrix from a single image of a color chart. We show that the color correction problem, that is, finding the 3 × 3 correction matrix, can be solved using a simple alternating least-squares procedure. Experiments validate our approach. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 232–242, 201

Extending Temporal Data Augmentation for Video Action Recognition

Pixel space augmentation has grown in popularity in many Deep Learning areas, due to its effectiveness, simplicity, and low computational cost. Data augmentation for videos, however, still remains an under-explored research topic, as most works have been treating inputs as stacks of static images rather than temporally linked series of data. Recently, it has been shown that involving the time dimension when designing augmentations can be superior to its spatial-only variants for video action recognition. In this paper, we propose several novel enhancements to these techniques to strengthen the relationship between the spatial and temporal domains and achieve a deeper level of perturbations. The video action recognition results of our techniques outperform their respective variants in Top-1 and Top-5 settings on the UCF-101 and the HMDB-51 datasets

Self-ensembling for visual domain adaptation

This paper explores the use of self-ensembling for visual domain adaptation problems. Our technique is derived from the mean teacher variant (Tarvainen et al., 2017) of temporal ensembling (Laine et al;, 2017), a technique that achieved state of the art results in the area of semi-supervised learning. We introduce a number of modifications to their approach for challenging domain adaptation scenarios and evaluate its effectiveness. Our approach achieves state of the art results in a variety of benchmarks, including our winning entry in the VISDA-2017 visual domain adaptation challenge. In small image benchmarks, our algorithm not only outperforms prior art, but can also achieve accuracy that is close to that of a classifier trained in a supervised fashion.Comment: 20 pages, 3 figure, accepted as a poster at ICLR 201

Multi-spectral Pedestrian Detection via Image Fusion and Deep Neural Networks

The use of multi-spectral imaging has been found to improve the accuracy of deep neural network-based pedestrian detection systems, particularly in challenging night time conditions in which pedestrians are more clearly visible in thermal long-wave infrared bands than in plain RGB. In this article, the authors use the Spectral Edge image fusion method to fuse visible RGB and IR imagery, prior to processing using a neural network-based pedestrian detection system. The use of image fusion permits the use of a standard RGB object detection network without requiring the architectural modifications that are required to handle multi-spectral input. We contrast the performance of networks trained using fused images to those that use plain RGB images and networks that use a multi-spectral input. © 2018 Society for Imaging Science and Technology

Spherical sampling methods for the calculation of metamer mismatch volumes

In this paper, we propose two methods of calculating theoretically maximal metamer mismatch volumes. Unlike prior art techniques, our methods do not make any assumptions on the shape of spectra on the boundary of the mismatch volumes. Both methods utilize a spherical sampling approach, but they calculate mismatch volumes in two different ways. The first method uses a linear programming optimization, while the second is a computational geometry approach based on half-space intersection. We show that under certain conditions the theoretically maximal metamer mismatch volume is significantly larger than the one approximated using a prior art method