1,170 research outputs found
Wavelet based joint denoising of depth and luminance images
In this paper we present a new method for joint denoising of depth and luminance images produced by time-of-flight camera. Here we assume that the sequence does not contain outlier points which can be present in the depth images. Our method first performs estimation of noise and signal covariance matrices and then performs vector denoising. Two versions of the algorithm are presented, depending on the method used for the classification of the image contexts. Denoising results are compared with the ground truth images obtained by averaging of the multiple frames of the still scene
Depth video enhancement for 3D displays
At the current stage of technology, depth maps acquired using cameras based on a time-of-flight principle have much lower spatial resolution compared to images that are captured by conventional color cameras. The main idea of our work is to use high resolution color images to improve the spatial resolution and image quality of the depth maps
Wavelet based stereo images reconstruction using depth images
It is believed by many that three-dimensional (3D) television will be the next logical development toward a more natural and vivid home entertaiment experience. While classical 3D approach requires the transmission of two video streams, one for each view, 3D TV systems based on depth image rendering (DIBR) require a single stream of monoscopic images and a second stream of associated images usually termed depth images or depth maps, that contain per-pixel depth information. Depth map is a two-dimensional function that contains information about distance from camera to a certain point of the object as a function of the image coordinates. By using this depth information and the original image it is possible to reconstruct a virtual image of a nearby viewpoint by projecting the pixels of available image to their locations in 3D space and finding their position in the desired view plane. One of the most significant advantages of the DIBR is that depth maps can be coded more efficiently than two streams corresponding to left and right view of the scene, thereby reducing the bandwidth required for transmission, which makes it possible to reuse existing transmission channels for the transmission of 3D TV. This technique can also be applied for other 3D technologies such as multimedia systems.
In this paper we propose an advanced wavelet domain scheme for the reconstruction of stereoscopic images, which solves some of the shortcommings of the existing methods discussed above. We perform the wavelet transform of both the luminance and depth images in order to obtain significant geometric features, which enable more sensible reconstruction of the virtual view. Motion estimation employed in our approach uses Markov random field smoothness prior for regularization of the estimated motion field.
The evaluation of the proposed reconstruction method is done on two video sequences which are typically used for comparison of stereo reconstruction algorithms. The results demonstrate advantages of the proposed approach with respect to the state-of-the-art methods, in terms of both objective and subjective performance measures
Π£Π»ΠΎΠ³Π°ΡΠ° ΠΈ Π·Π½Π°ΡΠ΅ΡΠ΅ΡΠΎ Π½Π° ΠΎΡΠΊΠ΅ΡΡΠ°ΡΠΎΡ Π½Π° Π½Π°ΡΠΎΠ΄Π½ΠΈ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΈ Π½Π° ΠΌΠ°ΠΊΠ΅Π΄ΠΎΠ½ΡΠΊΠ°ΡΠ° ΡΠ°Π΄ΠΈΠΎ ΡΠ΅Π»Π΅Π²ΠΈΠ·ΠΈΡΠ° Π²ΠΎ ΠΎΠ΄ΡΠΆΡΠ²Π°ΡΠ΅ΡΠΎ Π½Π° ΡΠΎΠΏΡΡΠ²Π΅Π½Π°ΡΠ° ΠΌΡΠ·ΠΈΡΠΊΠ° ΡΡΠ°Π΄ΠΈΡΠΈΡΠ°
ΠΠΎ Π³Π΅Π½Π΅ΡΠ°Π»Π½Π°ΡΠ° ΠΏΡΠ΅Π·Π΅Π½ΡΠ°ΡΠΈΡΠ° Π½Π° ΠΌΠ°ΠΊΠ΅Π΄ΠΎΠ½ΡΠΊΠ°ΡΠ° ΠΌΡΠ·ΠΈΡΠΊΠ° ΠΊΡΠ»ΡΡΡΠ°,Π·Π½Π°ΡΠ°Π΅Π½ ΡΠ΅Π³ΠΌΠ΅Π½Ρ Π·Π°Π·Π΅ΠΌΠ° Π·Π²ΡΡΠ½Π°ΡΠ° Π½Π°Π΄Π³ΡΠ°Π΄Π±Π° Π½Π° ΠΌΠ°ΠΊΠ΅Π΄ΠΎΠ½ΡΠΊΠ°ΡΠ° ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π°Π»Π½Π° ΠΌΡΠ·ΠΈΠΊΠ°. ΠΡΡΡΠ½ΠΎΡΡ, ΠΎΠ²Π°Π° ΠΌΡΠ·ΠΈΠΊΠ° ΡΠ΅ ΡΠ΅ΠΌΠ΅Π»ΠΈ Π½Π° ΠΏΠΎΡΡΠΎΡΠ°Π½Π°ΡΠ° ΠΈ Π½Π΅ΠΏΡΠ΅ΡΡΡΠ½Π° ΠΈΠ½ΡΠΏΠΈΡΠ°ΡΠΈΡΠ° ΠΎΠ΄ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π°Π»Π½ΠΈΠΎΡ Π½Π°ΡΠΎΠ΄Π΅Π½ ΠΌΠ΅Π»ΠΎΡ Π²ΠΎ Π³ΡΠ°Π΄Π΅ΡΠ΅ΡΠΎ Π½Π° ΡΠΎΠΏΡΡΠ²Π΅Π½ΠΈΠΎΡ ΠΌΡΠ·ΠΈΡΠΊΠΈ ΠΈΠ·ΡΠ°Π·. ΠΠ»Π΅Π΄Π°Π½ΠΎ ΠΎΠ΄ ΠΈΡΡΠΎΡΠΈΡΠΊΠΎ-ΡΠΎΡΠΈΠΎΠ»ΠΎΡΠΊΠΈ ΠΈ ΠΌΡΠ·ΠΈΠΊΠΎΠ»ΠΎΡΠΊΠΈ Π°ΡΠΏΠ΅ΠΊΡ, ΠΏΠΎΡΠ΅ΡΠΎΠΊΠΎΡ Π½Π° ΠΏΡΠ΅Π·Π΅Π½ΡΠΈΡΠ°ΡΠ΅ΡΠΎ ΠΈ ΡΠΈΡΠΎΠΊΠΎΡΠΎ ΠΏΠΎΠΏΡΠ»Π°ΡΠΈΠ·ΠΈΡΠ°ΡΠ΅ Π½Π° ΠΎΠ²ΠΎΡ ΠΌΡΠ·ΠΈΡΠΊΠΈ ΠΆΠ°Π½Ρ Π΅ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½Π° ΠΏΠΎΡΠ°Π²Π° ΡΠ΅ΡΠ½ΠΎ ΠΏΠΎΠ²ΡΠ·Π°Π½Π° ΡΠΎ:
β ΠΏΠΎΡΡΠ΅Π±Π°ΡΠ° ΠΎΠ΄ Π½Π΅Π³ΡΠ²Π°ΡΠ΅ ΠΈ ΠΏΡΠ΅ΡΡΡΠ°Π²ΡΠ²Π°ΡΠ΅ Π½Π° ΡΠΎΠΏΡΡΠ²Π΅Π½Π°ΡΠ° ΠΌΡΠ·ΠΈΡΠΊΠ° ΠΊΡΠ»ΡΡΡΠ°;
β ΠΏΠΎΡΠ²ΡΠ΄Π°ΡΠ° Π½Π° ΡΠΎΠΏΡΡΠ²Π΅Π½ΠΈΠΎΡ ΠΌΡΠ·ΠΈΡΠΊΠΈ ΠΈΠ΄Π΅Π½ΡΠΈΡΠ΅Ρ;
β ΠΏΡΠΈΠ²Π»Π΅ΠΊΡΠ²Π°ΡΠ΅ΡΠΎ Π½Π° Π΅Π΄Π½Π° ΠΏΠΎΡΠΈΡΠΎΠΊΠ° ΡΡΡΡΠΊΡΡΡΠ° Π½Π° ΡΠ»ΡΡΠ°ΡΠ΅Π»ΠΈ ΠΈ
β ΠΎΡΠ²ΠΎΡΠ°ΡΠ΅ΡΠΎ Π½Π° ΠΌΠΎΠΆΠ½ΠΎΡΡΠ° Π·Π° Π³ΠΎΠ»Π΅ΠΌ Π±ΡΠΎΡ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠΈ Π΄Π° ΡΠ΅ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π°ΡΠ·Π° ΠΏΡΠΎΡΠ΅ΡΠΈΠΎΠ½Π°Π»Π½ΠΎ ΠΏΠΎΡΠ²Π΅ΡΡΠ²Π°ΡΠ΅ Π½Π° ΠΎΠ²Π° ΠΌΡΠ·ΠΈΡΠΊΠΎ ΠΏΠΎΠ»Π΅
Content adaptive wavelet based method for joint denoising of depth and luminance images
In this paper we present a new method for joint denoising of depth and luminance images produced by time-of-flight camera. Here we assume that the sequence does not contain outlier points which can be present in the depth images. Our method first performs estimation of noise and signal covariance matrices and then performs vector denoising. Luminance image is segmented into similar contexts usina k-means algorithm, which are used for calculation of covariance matrices. Denoising results are compared with the ground truth images obtained by averaging of the multiple frames of the still scene
ΠΠΎΡΠ΅Π½ΠΈ, ΠΠ΅ΡΠ°Π»Π±Π°ΡΡΠΊΠΈ ΠΌΠ΅ΡΠ°ΠΊ
ΠΠ²ΡΠΎΡ Π½Π° ΠΌΡΠ·ΠΈΠΊΠ° ΠΈ Π°ΡΠ°Π½ΠΆΠΌΠ°Π½: ΠΠ»ΡΠΎ ΠΠΎΠ²Π°Π½ΠΎΠ²
Π‘ΠΎ ΠΈΡΠΊΠ»ΡΡΠΎΠΊ Π½Π° Π±Ρ.6 ΠΈ10 Π°ΡΠ°Π½ΠΆΠΌΠ°Π½: ΠΠ»Π°Π³ΠΎΡΠ° ΠΠ΅ΡΠΊΠΎΡΠΊΠΈ
ΠΡΠ·ΠΈΠΊΠ°ΡΠ° Π½Π° ΠΎΠ²ΠΎΡ Π°Π²ΡΠΎΡΡΠΊΠΈ Π°Π»Π±ΡΠΌ Π΅ ΠΈΠ·Π±ΠΎΡ ΠΎΠ΄ Π΅Π΄Π½ΠΎ ΠΌΠΎΡΠ½Π΅ ΠΎΠ±Π΅ΠΌΠ½ΠΎ
ΡΠ²ΠΎΡΠ΅ΡΡΠ²ΠΎ Π½Π° Π΅Π΄Π΅Π½ Π½Π΅ΡΠ΅ΠΊΠΎΡΠ΄Π½Π΅Π²Π΅Π½ ΠΌΡΠ·ΠΈΡΠ°Ρ. βΠΠΎΡΠ΅Π½ΠΈβ Π΅ ΠΏΡΠΈΠΊΠ°Π·Π½Π° Π·Π° Π΅Π΄Π΅Π½ Π²ΡΡΠ±Π΅Π½ΠΈΠΊ Π²ΠΎ Ρ
Π°ΡΠΌΠΎΠ½ΠΈΠΊΠ°ΡΠ° ΠΊΠΎΡ ΡΠΈΠΎΡ ΡΠ²ΠΎΡ ΠΆΠΈΠ²ΠΎΡ Π½Π΅ ΠΏΡΠ΅ΡΡΠ°Π½Π°Π» Π΄Π° ΡΠΎΠ·Π΄Π°Π²Π° ΠΌΡΠ·ΠΈΠΊΠ° Π²ΠΎ Π΄ΡΡ
ΠΎΡ ΠΈ ΡΡΠ°Π΄ΠΈΡΠΈΡΠ°ΡΠ° Π½Π° ΡΠ²ΠΎΡΠΎΡ Π½Π°ΡΠΎΠ΄. ΠΠΎΡΠ»Π΅ ΠΌΠ½ΠΎΠ³Ρ Π½Π°ΠΏΠΈΡΠ°Π½ΠΈ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΠΈ, ΠΎΡΠΊΠ΅ΡΡΡΠ°ΡΠΈΠΈ ΠΈ ΡΠΎΠ»ΠΈΡΡΠΈΡΠΊΠΈ ΠΈΠ·Π²Π΅Π΄Π±ΠΈ ΠΠ»ΡΠΎ ΠΠΎΠ²Π°Π½ΠΎΠ² Π½ΠΈ ΡΠ΅ ΠΏΡΠ΅ΡΡΡΠ°Π²ΡΠ²Π° ΡΠΎ ΠΎΠ²ΠΎΡ ΠΊΠΎΠΌΠΏΠΈΠ»Π°ΡΠΈΡΠΊΠΈ ΠΎΡΠ²ΡΡ, ΠΎΠΏΡΡ ΠΊΠΎΡ Π·Π°ΠΏΠΎΡΠ½Π°Π» Π΄Π° ΡΠ΅ ΡΠΎΠ·Π΄Π°Π²Π° ΠΏΡΠ΅Π΄ ΡΡΠΈ Π΄Π΅ΡΠ΅Π½ΠΈΠΈ. Π ΠΎΠ΄Π΅Π½ Π΅ Π²ΠΎ Π¨ΡΠΈΠΏ Π²ΠΎ 1962 Π³ΠΎΠ΄ΠΈΠ½Π°. Π£ΡΠ΅ΡΡΠ²ΡΠ²Π° Π½Π° ΠΌΠ½ΠΎΠ³Ρ ΡΠ΅ΡΡΠΈΠ²Π°Π»ΠΈ ΠΈ ΠΊΠΎΠ½ΡΠ΅ΡΡΠΈΡΠ° Π½Π°ΡΠ΅ΠΊΠ°Π΄Π΅ ΠΏΠΎ ΡΠ²Π΅ΡΠΎΡ. ΠΠ²ΡΠΎΡ Π΅ Π½Π° Π³ΠΎΠ»Π΅ΠΌ Π±ΡΠΎΡ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΠΈ, Π°ΡΠ°Π½ΠΆΠΌΠ°Π½ΠΈ ΠΈ ΠΎΡΠΊΠ΅ΡΡΡΠ°ΡΠΈΠΈ ΠΈΠ½ΡΠΏΠΈΡΠΈΡΠ°Π½ΠΈ ΠΎΠ΄ ΠΌΠ°ΠΊΠ΅Π΄ΠΎΠ½ΡΠΊΠΈΠΎΡ ΡΠΎΠ»ΠΊΠ»ΠΎΡ. ΠΠ° Π½Π΅Π³ΠΎΠ²ΠΈΠΎΡ ΡΠΌΠ΅ΡΠ½ΠΈΡΠΊΠΈ ΠΏΡΠΈΠ΄ΠΎΠ½Π΅Ρ Π΄ΠΎΠ΄Π΅Π»Π΅Π½ΠΈ ΠΌΡ ΡΠ΅ Π±ΡΠΎΡΠ½ΠΈ Π²ΡΠ²Π½ΠΈ Π½Π°Π³ΡΠ°Π΄ΠΈ ΠΈ ΠΏΡΠΈΠ·Π½Π°Π½ΠΈΡΠ° ΠΌΠ΅ΡΡ ΠΊΠΎΠΈ ΠΎΡΠΎΠ±Π΅Π½ΠΎ ΠΌΠ΅ΡΡΠΎ Π·Π°Π·Π΅ΠΌΠ° βΠΡΠ²Π° Ρ
Π°ΡΠΌΠΎΠ½ΠΈΠΊΠ° Π½Π° ΠΡΠ³ΠΎΡΠ»Π°Π²ΠΈΡΠ°β ΠΎΡΠ²ΠΎΠ΅Π½Π° Π½Π° ΠΏΡΠ΅ΡΡΠΈΠΆΠ½ΠΈΠΎΡ Π½Π°ΡΠΏΡΠ΅Π²Π°Ρ Π²ΠΎ Π‘ΠΎΠΊΠΎ ΠΠ°ΡΠ° (ΠΏΠΎΡΠ°Π½Π΅ΡΠ½Π° ΠΡΠ³ΠΎΡΠ»Π°Π²ΠΈΡΠ°) Π²ΠΎ 1982 Π³ΠΎΠ΄ΠΈΠ½Π°. ΠΠΈΠΏΠ»ΠΎΠΌΠΈΡΠ°Π» Π½Π° Π€Π°ΠΊΡΠ»ΡΠ΅ΡΠΎΡ Π·Π° ΠΌΡΠ·ΠΈΡΠΊΠ° ΡΠΌΠ΅ΡΠ½ΠΎΡΡ Π²ΠΎ Π‘ΠΊΠΎΠΏΡΠ΅, ΠΌΠ°Π³ΠΈΡΡΡΠΈΡΠ°Π» Π½Π° ΠΡΠΆΠ°Π²Π½Π°ΡΠ° ΠΌΡΠ·ΠΈΡΠΊΠ° Π°ΠΊΠ°Π΄Π΅ΠΌΠΈΡΠ° βΠΠ°Π½ΡΠΎ ΠΠ»Π°Π΄ΠΈΠ³Π΅ΡΠΎΠ²'' Π²ΠΎ Π‘ΠΎΡΠΈΡΠ° Π° ΡΠ²ΠΎΡΠΎΡ Π΄ΠΎΠΊΡΠΎΡΠ°Ρ ΠΎΠ΄ ΠΎΠ±Π»Π°ΡΡΠ° Π½Π° ΠΡΠ·ΠΈΡΠΊΠ°ΡΠ° ΡΠ΅ΠΎΡΠΈΡΠ° ΠΈ ΠΏΠ΅Π΄Π°Π³ΠΎΠ³ΠΈΡΠ° Π³ΠΎ ΡΡΠ΅ΠΊΠ½ΡΠ²Π° Π½Π° MΠ΅ΡΡΠ½Π°ΡΠΎΠ΄Π½ΠΈΠΎΡ Π£Π½ΠΈΠ²Π΅ΡΠ·ΠΈΡΠ΅Ρ Π²ΠΎ ΠΠΈΠ΅Π²,
Π£ΠΊΡΠ°ΠΈΠ½Π°. ΠΡΠΎΡΠ΅ΡΠΎΡ Π΄-Ρ ΠΠ»ΡΠΎ ΠΠΎΠ²Π°Π½ΠΎΠ² ΠΎΠ΄ 2007 Π³ΠΎΠ΄ΠΈΠ½Π° Π΅ Π΄Π΅ΠΊΠ°Π½ Π½Π° Π€Π°ΠΊΡΠ»ΡΠ΅ΡΠΎΡ Π·Π° ΠΌΡΠ·ΠΈΡΠΊΠ° ΡΠΌΠ΅ΡΠ½ΠΎΡΡ ΠΏΡΠΈ Π£Π½ΠΈΠ²Π΅ΡΠ·ΠΈΡΠ΅ΡΠΎΡ ,,ΠΠΎΡΠ΅ ΠΠ΅Π»ΡΠ΅Π²'' Π²ΠΎ Π¨ΡΠΈΠΏ Π Π΅ΠΏΡΠ±Π»ΠΈΠΊΠ° ΠΠ°ΠΊΠ΅Π΄ΠΎΠ½ΠΈΡΠ°
Analysis of information threats and counteractions in consumer oriented organizations (separating the best from the rest
Generation Y, what do they really want? Itβs the 21st century and the greatest consumers of information ever are on roll. Consumers are embracing a digital lifestyle and enterprises are interacting in new ways. In times like this, when the information are the companies most valuable resource, the issue about information threats and security should be their top priority. With opportunities come risks and protection is about more than just technology, itβs about people, process and technology. While some companies are struggling to survive, others are rethinking their business strategies and redesigning the marketing practices to build more profitable, enduring relationships with their customers
ΠΠΎΡΠ΅Π½ΠΈ, ΠΠ»ΡΠΎΠ²ΠΎ ΠΎΡΠΎ
ΠΠ²ΡΠΎΡ Π½Π° ΠΌΡΡΠΈΡΠΊΠΈ Π°ΡΠ°ΠΆΠΌΠ°Π½: ΠΠ»ΡΠΎ ΠΠΎΠ²Π°Π½ΠΎΠ² Π‘ΠΎ ΠΈΡΠΊΠ»ΡΡΠΎΠΊ Π½Π° Π±Ρ. 6 ΠΈ 10 Π°ΡΠ°ΠΆΠΌΠ°Π½: ΠΠ»Π°Π³ΠΎΡΠ° ΠΠ΅ΡΠΊΠΎΡΠΊΠΈ
ΠΡΠ·ΠΈΠΊΠ°ΡΠ° Π½Π° ΠΎΠ²ΠΎΡ Π°Π²ΡΠΎΡΡΠΊΠΈ Π°Π»Π±ΡΠΌ Π΅ ΠΈΠ·Π±ΠΎΡ ΠΎΠ΄ Π΅Π΄Π½ΠΎ ΠΌΠΎΡΠ½Π΅ ΠΎΠ±Π΅ΠΌΠ½ΠΎ
ΡΠ²ΠΎΡΠ΅ΡΡΠ²ΠΎ Π½Π° Π΅Π΄Π΅Π½ Π½Π΅ΡΠ΅ΠΊΠΎΡΠ΄Π½Π΅Π²Π΅Π½ ΠΌΡΠ·ΠΈΡΠ°Ρ. βΠΠΎΡΠ΅Π½ΠΈβ Π΅ ΠΏΡΠΈΠΊΠ°Π·Π½Π° Π·Π° Π΅Π΄Π΅Π½ Π²ΡΡΠ±Π΅Π½ΠΈΠΊ Π²ΠΎ Ρ
Π°ΡΠΌΠΎΠ½ΠΈΠΊΠ°ΡΠ° ΠΊΠΎΡ ΡΠΈΠΎΡ ΡΠ²ΠΎΡ ΠΆΠΈΠ²ΠΎΡ Π½Π΅ ΠΏΡΠ΅ΡΡΠ°Π½Π°Π»Π΄Π° ΡΠΎΠ·Π΄Π°Π²Π° ΠΌΡΠ·ΠΈΠΊΠ° Π²ΠΎ Π΄ΡΡ
ΠΎΡ ΠΈ ΡΡΠ°Π΄ΠΈΡΠΈΡΠ°ΡΠ° Π½Π° ΡΠ²ΠΎΡΠΎΡ Π½Π°ΡΠΎΠ΄. ΠΠΎΡΠ»Π΅ ΠΌΠ½ΠΎΠ³Ρ Π½Π°ΠΏΠΈΡΠ°Π½ΠΈ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΠΈ, ΠΎΡΠΊΠ΅ΡΡΡΠ°ΡΠΈΠΈ ΠΈ ΡΠΎΠ»ΠΈΡΡΠΈΡΠΊΠΈ ΠΈΠ·Π²Π΅Π΄Π±ΠΈ ΠΠ»ΡΠΎ ΠΠΎΠ²Π°Π½ΠΎΠ² Π½ΠΈ ΡΠ΅ ΠΏΡΠ΅ΡΡΡΠ°Π²ΡΠ²Π° ΡΠΎ ΠΎΠ²ΠΎΡ ΠΊΠΎΠΌΠΏΠΈΠ»Π°ΡΠΈΡΠΊΠΈ ΠΎΡΠ²ΡΡ, ΠΎΠΏΡΡ ΠΊΠΎΡ Π·Π°ΠΏΠΎΡΠ½Π°Π» Π΄Π° ΡΠ΅ ΡΠΎΠ·Π΄Π°Π²Π° ΠΏΡΠ΅Π΄ ΡΡΠΈ Π΄Π΅ΡΠ΅Π½ΠΈΠΈ.
Π ΠΎΠ΄Π΅Π½ Π΅ Π²ΠΎ Π¨ΡΠΈΠΏ Π²ΠΎ 1962 Π³ΠΎΠ΄ΠΈΠ½Π°. Π£ΡΠ΅ΡΡΠ²ΡΠ²Π° Π½Π° ΠΌΠ½ΠΎΠ³Ρ ΡΠ΅ΡΡΠΈΠ²Π°Π»ΠΈ ΠΈ ΠΊΠΎΠ½ΡΠ΅ΡΡΠΈΡΠ° Π½Π°ΡΠ΅ΠΊΠ°Π΄Π΅ ΠΏΠΎ ΡΠ²Π΅ΡΠΎΡ. ΠΠ²ΡΠΎΡ Π΅ Π½Π° Π³ΠΎΠ»Π΅ΠΌ Π±ΡΠΎΡ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΠΈ, Π°ΡΠ°Π½ΠΆΠΌΠ°Π½ΠΈ ΠΈ ΠΎΡΠΊΠ΅ΡΡΡΠ°ΡΠΈΠΈ ΠΈΠ½ΡΠΏΠΈΡΠΈΡΠ°Π½ΠΈ ΠΎΠ΄ ΠΌΠ°ΠΊΠ΅Π΄ΠΎΠ½ΡΠΊΠΈΠΎΡ ΡΠΎΠ»ΠΊΠ»ΠΎΡ. ΠΠ° Π½Π΅Π³ΠΎΠ²ΠΈΠΎΡ ΡΠΌΠ΅ΡΠ½ΠΈΡΠΊΠΈ ΠΏΡΠΈΠ΄ΠΎΠ½Π΅Ρ Π΄ΠΎΠ΄Π΅Π»Π΅Π½ΠΈ ΠΌΡ ΡΠ΅ Π±ΡΠΎΡΠ½ΠΈ Π²ΡΠ²Π½ΠΈ Π½Π°Π³ΡΠ°Π΄ΠΈ ΠΈ ΠΏΡΠΈΠ·Π½Π°Π½ΠΈΡΠ° ΠΌΠ΅ΡΡ ΠΊΠΎΠΈ ΠΎΡΠΎΠ±Π΅Π½ΠΎ ΠΌΠ΅ΡΡΠΎ Π·Π°Π·Π΅ΠΌΠ° βΠΡΠ²Π° Ρ
Π°ΡΠΌΠΎΠ½ΠΈΠΊΠ° Π½Π° ΠΡΠ³ΠΎΡΠ»Π°Π²ΠΈΡΠ°β ΠΎΡΠ²ΠΎΠ΅Π½Π° Π½Π° ΠΏΡΠ΅ΡΡΠΈΠΆΠ½ΠΈΠΎΡ Π½Π°ΡΠΏΡΠ΅Π²Π°Ρ Π²ΠΎ Π‘ΠΎΠΊΠΎ ΠΠ°ΡΠ° (ΠΏΠΎΡΠ°Π½Π΅ΡΠ½Π° ΠΡΠ³ΠΎΡΠ»Π°Π²ΠΈΡΠ°) Π²ΠΎ 1982 Π³ΠΎΠ΄ΠΈΠ½Π°.
ΠΠΈΠΏΠ»ΠΎΠΌΠΈΡΠ°Π» Π½Π° Π€Π°ΠΊΡΠ»ΡΠ΅ΡΠΎΡ Π·Π° ΠΌΡΠ·ΠΈΡΠΊΠ° ΡΠΌΠ΅ΡΠ½ΠΎΡΡ Π²ΠΎ Π‘ΠΊΠΎΠΏΡΠ΅, ΠΌΠ°Π³ΠΈΡΡΡΠΈΡΠ°Π» Π½Π° ΠΡΠΆΠ°Π²Π½Π°ΡΠ° ΠΌΡΠ·ΠΈΡΠΊΠ° Π°ΠΊΠ°Π΄Π΅ΠΌΠΈΡΠ° βΠΠ°Π½ΡΠΎ ΠΠ»Π°Π΄ΠΈΠ³Π΅ΡΠΎΠ²'' Π²ΠΎ Π‘ΠΎΡΠΈΡΠ° Π° ΡΠ²ΠΎΡΠΎΡ Π΄ΠΎΠΊΡΠΎΡΠ°Ρ ΠΎΠ΄ ΠΎΠ±Π»Π°ΡΡΠ° Π½Π° ΠΡΠ·ΠΈΡΠΊΠ°ΡΠ° ΡΠ΅ΠΎΡΠΈΡΠ° ΠΈ ΠΏΠ΅Π΄Π°Π³ΠΎΠ³ΠΈΡΠ° Π³ΠΎ ΡΡΠ΅ΠΊΠ½ΡΠ²Π° Π½Π° MΠ΅ΡΡΠ½Π°ΡΠΎΠ΄Π½ΠΈΠΎΡ Π£Π½ΠΈΠ²Π΅ΡΠ·ΠΈΡΠ΅Ρ Π²ΠΎ ΠΠΈΠ΅Π², Π£ΠΊΡΠ°ΠΈΠ½Π°.
ΠΡΠΎΡΠ΅ΡΠΎΡ Π΄-Ρ ΠΠ»ΡΠΎ ΠΠΎΠ²Π°Π½ΠΎΠ² ΠΎΠ΄ 2007 Π³ΠΎΠ΄ΠΈΠ½Π° Π΅ Π΄Π΅ΠΊΠ°Π½ Π½Π° Π€Π°ΠΊΡΠ»ΡΠ΅ΡΠΎΡ Π·Π° ΠΌΡΠ·ΠΈΡΠΊΠ° ΡΠΌΠ΅ΡΠ½ΠΎΡΡ ΠΏΡΠΈ Π£Π½ΠΈΠ²Π΅ΡΠ·ΠΈΡΠ΅ΡΠΎΡ ,,ΠΠΎΡΠ΅ ΠΠ΅Π»ΡΠ΅Π²'' Π²ΠΎ Π¨ΡΠΈΠΏ - Π Π΅ΠΏΡΠ±Π»ΠΈΠΊΠ° ΠΠ°ΠΊΠ΅Π΄ΠΎΠ½ΠΈΡΠ°
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