The extent to which member states transpose EU directives in a timely fashion is often argued to be strongly associated with the general effectiveness of national bureaucracies. But what kind of institutional solutions ensure better performance? This paper examines the patterns and effects of departmental oversight across 28 ministries in Estonia, Hungary, Poland and Slovenia. In mapping the strength of oversight, it relies on around 90 structured interviews regarding the rules-in-use on transposition planning, legal review and monitoring of deadlines. The analysis of the impact of departmental oversight is based on an original dataset of over 300 directives with transposition deadlines between January 2005 and December 2008.

On the european union

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Handbook Of Image And Video Processing

Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning

This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus for face recognition is highlighted in the evaluation of a standard face recognition algorithm, which illustrates the impact that age-progression, has on recognition rates. Assessment of the efficacy of this algorithm is evaluated against the variables of gender and racial origin. This work further concludes that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work.

MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.

A comprehensive survey on impulse and Gaussian denoising filters for digital images

An application for video data analysis based on computer vision and machine learning methods is presented. Novel gender and age classifiers based on adaptive features, local binary patterns and support vector machines are proposed. More than 94% accuracy of viewer's gender recognition is achieved. Our age estimation algorithm provides world-quality results for MORTH database, but focused on real-life audience measurement videodata in which faces can be looks more or less similar to RUS-FD private database. In this case we can reach total mean absolute error score less than 7. All the video processing stages are united into a real-time system of audience analysis. The system allows to extract all the possible information about people from the input video stream, to aggregate and analyze this information in order to measure different statistical parameters. The promising practical application of such algorithms can be human-computer interaction, surveillance monitoring, video content analysis, targeted advertising, biometrics, and entertainment.

Gender and age recognition for video analytics solution

The aim of this research is to create a deep learning algorithm for automated forest areas segmentation on high-resolution aerial images. Loss values and Dice coefficient, which compares results of algorithms with real masks, was used to measure the quality of developed model. On the whole this paper demonstrates how convolutional neural network implemented on modern GPUs can be applied for the detection of forests on satellite images.

Forest Areas Segmentation on Aerial Images by Deep Learning

This article presents results of two deep learning algorithms for sand quarries detection on high-resolution aerial photos. Planet database of high-resolution aerial images was collected. Input images contain blue, green, red and near-infrared channels. Before the training process there was implemented the equalization of brightness histogram which allows to cope with the problem of obscuration of satellite images. To implement numerical experiments there were extracted smaller patches. Training and test sets were enlarged by three types of data augmentation. Convolutional neural networks were pretrained on the SpaceNet dataset and tuned on the Planet database. Deep learning algorithms were launched on NVIDIA DGX-1 supercomputer. Special metrics, such as F1, precision, recall and Dice coefficient allowed to compare the quality of developed models.

Quarry Areas Segmentation on Satellite Images by Convolutional Neural Networks

Deep learning and modern type of neural network technologies are increasingly used for the detection, segmentation and classification of different objects in aerial multichannel images. The goal of given research was to develop a deep learning algorithm for automated building detection on four-channel satellite images. It is proposed to use U-Net neural network with two decoders to separate objects, one decoder is trained to segment buildings and structures, and the other detects narrow distances between buildings. It is shown that optimized U-Net can be used to detect such kind of objects efficiently. The model was implemented by means of open Keras library and launched on modern GPUs of high-performance supercomputer NVIDIA DGX-1. Before testing of developed algorithm on Planet aerial image dataset, modified U-Net had been pre-trained on SpaceNet database. The process of image data augmentation is described. The problem of effective building detection on high-resolution aerial photos can be used for urban planning, building control, search of the best locations for future outlets etc.

Separation of Closely Located Buildings on Aerial Images Using U-Net Neural Network

The system allowing to extract all the possible information about depicted people from the input video stream is discussed. As reported previously, the proposed system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and statistics analysis. The crucial part of the system is gender classifier construction on the basis of machine learning methods. We propose a novel algorithm consisting of two stages: adaptive feature extraction and support vector machine classification. Both training technique of the proposed algorithm and experimental results acquired on a large image dataset are presented. More than 90% accuracy of viewer's gender recognition is achieved.

Vladimir Khryashchev

Papers

Gender and age recognition for video analytics solution

Forest Areas Segmentation on Aerial Images by Deep Learning

Quarry Areas Segmentation on Satellite Images by Convolutional Neural Networks

Separation of Closely Located Buildings on Aerial Images Using U-Net Neural Network

Gender classification for real-time audience analysis system