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

Thank you very much for reading handbook of image and video processing. As you may know, people have search numerous times for their favorite novels like this handbook of image and video processing, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful bugs inside their laptop. handbook of image and video processing is available in our book collection an online access to it is set as public so you can download it instantly. Our digital library hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the handbook of image and video processing is universally compatible with any devices to read.

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

A new adaptive switching median filter is proposed to remove salt-and-pepper impulse noise from corrupted image. The algorithm is developed by combining advantages of the known median-type filters with impulse noise detection step. Comparison of the given method with traditional filters is provided. A visual example is given to demonstrate the performance of the proposed filter.

Image denoising using adaptive switching median filter

Deep learning and convolutional neural network technologies are increasingly used in the problems of analysis, segmentation and recognition of objects in images. In this article a convolutional neural network for automated wildfire detection on high-resolution aerial photos is presented. Two databases of satellite RGB-images with different spatial resolution containing 1457 and 393 high-resolution images, respectively, were prepared for training and testing the neural network. Various techniques of data augmentation are used to enlarge training and test sets generated by data windowing. U-Net neural network with the ResNet34 as encoder was used in research. Neural network training was learning using the NVIDIA DGX-1 supercomputer. Adaptive moment estimation algorithm was used for optimization of training process. Special metrics, such as Sorensen-Dice coefficient, precision, recall, F1-score and IoU value allows to measure the quality of developed model. The developed algorithm can be successfully applied for early wildland fires detection in practical applications.

Wildfire Segmentation on Satellite Images using Deep Learning

This article presents research results of two convolutional neural networks for building detection on satellite images of Planet database. To analyze the quality of developed algorithms, there was used Sorensen-Dice coefficient of similarity which compares results of algorithms with tagged masks. The masks were generated from json files and sliced on smaller parts together with respective images before the training of algorithms. This approach allows to cope with the problem of segmentation for aerial high-resolution images efficiently and effectively. The problem of building detection on satellite images can be put into practice for urban planning, building control, etc.

Building Detection on Aerial Images Using U-NET Neural Networks

Convolutional neural networks for detection geo-objects on the satellite images from DSTL, Landsat -8 and PlanetScope databases were analyzed. Three modification of convolutional neural network architecture for implementing the recognition algorithm was used. Images obtained from the Landsat -8 and PlanetScope satellites are used for estimation of automatic object detection quality. To analyze the accuracy of the object detection algorithm, the selected regions were compared with the areas by previously marked by experts. An important result of the study was the improvement of the detector for the class “Forest”. Segmentation of satellite images has found application at urban planning, forest management, climate modelling, etc.

Comparison of Different Convolutional Neural Network Architectures for Satellite Image Segmentation

This paper presents algorithms for smile detection and facial expression recognition. The developed algorithms are based on the implementation of a relatively new approach in the field of deep machine learning — a convolutional neural network. The aim of this network is to classify facial images into one of the six types of emotions. The studying of algorithms was carried using face images from the CMU MultiPie database. To accelerate the neural network operation, the training and testing processes were performed parallel, on a large number of independent streams on GPU. Fo r developed models there were given metrics of quality.

Vladimir Khryashchev

Papers

Image denoising using adaptive switching median filter

Wildfire Segmentation on Satellite Images using Deep Learning

Building Detection on Aerial Images Using U-NET Neural Networks

Comparison of Different Convolutional Neural Network Architectures for Satellite Image Segmentation

Facial expression recognition algorithm based on deep convolution neural network