7 research outputs found
Π€ΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π·Π΅ΠΌΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ Π½ΠΎΡΡΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΈΠΊΡΠ΅Π»Π΅ΠΉ Π»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΡ ΡΠ½ΠΈΠΌΠΊΠΎΠ² Π² ΠΌΠ½ΠΎΠ³ΠΎΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ Π±ΠΎΡΡΠΎΠ²ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΠ΅
ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΡΠΏΠΎΡΠΎΠ± ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ°Π·Π½ΠΎΡΠ°ΠΊΡΡΡΠ½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΊΠ²Π°Π·ΠΈΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΈΠΊΡΠ΅Π»Π΅ΠΉ ΠΊ ΠΈΡΡ
ΠΎΠ΄Π½ΡΠΌ ΡΠ½ΠΈΠΌΠΊΠ°ΠΌ Π·Π΅ΠΌΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ. ΠΡΡ
ΠΎΠ΄Π½ΡΠ΅ ΡΠ°Π·Π½ΠΎΡΠ°ΠΊΡΡΡΠ½ΡΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ, ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ Π±ΠΎΡΡΠΎΠ²ΠΎΠΉ Π°ΠΏΠΏΠ°ΡΠ°ΡΡΡΠΎΠΉ ΠΌΠ½ΠΎΠ³ΠΎΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½Π½ΡΡ
Π»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ, ΡΠΎΡΡΡΠΊΠΎΠ²ΡΠ²Π°ΡΡΡΡ Π² Π΅Π΄ΠΈΠ½ΡΠΉ ΡΠΎΡΡΠ°Π²Π½ΠΎΠΉ ΡΠ½ΠΈΠΌΠΎΠΊ ΠΈ ΠΏΡΠΈ ΠΏΠΎΠΌΠΎΡΠΈ Π²ΡΡΠΎΠΊΠΎΡΠΊΠΎΡΠΎΡΡΠ½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΊΠ²Π°Π·ΠΈΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΈΠΊΡΠ΅Π»Π΅ΠΉ ΡΠ΅Π΄ΡΡΠΈΡΡΡΡΡΡ Π΄ΠΎ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΡ
ΡΠ²Π΅ΡΠΎΠ² Ρ ΡΠΎΡ
ΡΠ°Π½Π΅Π½ΠΈΠ΅ΠΌ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ½ΡΡ
Π³ΡΠ°Π½ΠΈΡ. ΠΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΊΠ²Π°Π·ΠΈΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ ΡΠ΅ΡΠΈΠΈ ΡΠ°Π·Π±ΠΈΠ΅Π½ΠΈΠΉ Ρ ΠΏΠΎΡΡΠ΅ΠΏΠ΅Π½Π½ΠΎ ΡΠ²Π΅Π»ΠΈΡΠΈΠ²Π°ΡΡΠ΅ΠΉΡΡ Π΄Π΅ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠ΅ΠΉ Π·Π° ΡΡΠ΅Ρ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΡΠ»Π° ΠΊΠ»Π°ΡΡΠ΅ΡΠΎΠ². ΠΡΠ° ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ Π²ΡΠ±ΡΠ°ΡΡ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΡΡΠΈΠ΅ ΡΠ°Π·Π±ΠΈΠ΅Π½ΠΈΡ ΠΏΠ°Ρ ΡΠΎΡΡΡΠΊΠΎΠ²Π°Π½Π½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΈΠ· ΡΠ΅ΡΠΈΠΈ ΡΠ³Π΅Π½Π΅ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
.
ΠΠ° ΠΏΠ°ΡΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΈΠ· Π²ΡΠ±ΡΠ°Π½Π½ΠΎΠ³ΠΎ ΡΠ°Π·Π±ΠΈΠ΅Π½ΠΈΡ ΡΠΎΡΡΡΠΊΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ½ΠΈΠΌΠΊΠ° ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠΎΠΈΡΠΊ ΠΎΠΏΠΎΡΠ½ΡΡ
ΡΠΎΡΠ΅ΠΊ Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΡ
ΠΊΠΎΠ½ΡΡΡΠΎΠ². ΠΠ»Ρ ΡΡΠΈΡ
ΡΠΎΡΠ΅ΠΊ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅ΡΡΡ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ΅ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈ ΠΏΠΎΡΠ»Π΅ Π΅Π³ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΊ ΠΈΡΡ
ΠΎΠ΄Π½ΡΠΌ ΡΠ½ΠΈΠΌΠΊΠ°ΠΌ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΡΠ΅Π½ΠΊΠ° ΡΡΠ΅ΠΏΠ΅Π½ΠΈ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΈ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ. ΠΠ°ΠΊ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ ΠΎΠΏΠΎΡΠ½ΡΡ
ΡΠΎΡΠ΅ΠΊ ΠΊΠΎΠ½ΡΡΡΠ°, ΡΠ°ΠΊ ΠΈ ΡΠ°ΠΌΠΎ ΠΈΡΠΊΠΎΠΌΠΎΠ΅ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ΅ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΠΎΡΠ½ΡΠ΅ΡΡΡ Π΄ΠΎ ΡΠ΅Ρ
ΠΏΠΎΡ, ΠΏΠΎΠΊΠ° ΠΎΡΠ΅Π½ΠΊΠ° ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π΅ Π±ΡΠ΄Π΅Ρ ΠΏΡΠΈΠ΅ΠΌΠ»Π΅ΠΌΠΎΠΉ. ΠΠΈΠ΄ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ΄Π±ΠΈΡΠ°Π΅ΡΡΡ ΠΏΠΎ ΡΠ΅Π΄ΡΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌ ΠΏΠΎ ΡΠ²Π΅ΡΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠΌ, Π° Π·Π°ΡΠ΅ΠΌ ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΡΡΡ ΠΊ ΠΈΡΡ
ΠΎΠ΄Π½ΡΠΌ ΡΠ½ΠΈΠΌΠΊΠ°ΠΌ. ΠΡΠΎΡ ΠΏΡΠΎΡΠ΅ΡΡ ΠΏΠΎΠ²ΡΠΎΡΡΠ΅ΡΡΡ Π΄Π»Ρ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π½Π½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Ρ Π±ΠΎΠ»ΡΡΠ΅ΠΉ Π΄Π΅ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠ΅ΠΉ Π² ΡΠΎΠΌ ΡΠ»ΡΡΠ°Π΅, Π΅ΡΠ»ΠΈ ΠΎΡΠ΅Π½ΠΊΠ° ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π΅ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΡΠΈΠ΅ΠΌΠ»Π΅ΠΌΠΎΠΉ. Π¦Π΅Π»ΡΡ Π½Π°ΡΡΠΎΡΡΠ΅Π³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΡΠΏΠΎΡΠΎΠ±Π°, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅Π³ΠΎ ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°ΡΡ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ Π·Π΅ΠΌΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ ΠΈΠ· ΡΠ°Π·Π½ΠΎΡΠΎΡΠΌΠ°ΡΠ½ΡΡ
ΠΈ ΡΠ°Π·Π½ΠΎΡΠΎΠ΄Π½ΡΡ
ΡΠ½ΠΈΠΌΠΊΠΎΠ².
Π ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ»Π΅Π΄ΡΡΡΠΈΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΡΠΏΠΎΡΠΎΠ±Π° ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ. ΠΠ΅ΡΠ²Π°Ρ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ΅ Π΅Π΄ΠΈΠ½ΠΎΠ³ΠΎ ΡΠΎΡΡΠ°Π²Π½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΈΠ· ΠΏΠ°ΡΡ ΡΠΎΡΡΡΠΊΠΎΠ²Π°Π½Π½ΡΡ
ΠΈΡΡ
ΠΎΠ΄Π½ΡΡ
ΡΠ½ΠΈΠΌΠΊΠΎΠ² Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠΌ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΈΠΊΡΠ΅Π»Π΅ΠΉ, ΡΡΠΎ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΏΠΎΠ΄ΠΎΠ±Π½ΡΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ Π²ΡΠ΄Π΅Π»ΠΈΡΡ ΠΎΠ΄ΠΈΠ½Π°ΠΊΠΎΠ²ΡΠ΅ ΠΎΠ±Π»Π°ΡΡΠΈ Π½Π° Π΅Π³ΠΎ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠ°ΡΡΡΡ
. ΠΡΠΎΡΠ°Ρ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎ Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΠΌ ΡΠΎΡΠΊΠ°ΠΌ ΠΊΠΎΠ½ΡΡΡΠ° Π½Π° ΠΎΠ±ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠΉ ΠΏΠ°ΡΠ΅ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π½Π½ΡΡ
ΡΠ½ΠΈΠΌΠΊΠΎΠ², ΠΊΠΎΡΠΎΡΠΎΠ΅ ΠΈ ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΡΡΡ ΠΊ ΠΈΡΡ
ΠΎΠ΄Π½ΡΠΌ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠΌ Π΄Π»Ρ ΠΈΡ
ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ.
Π ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΊΠ°ΠΊ ΠΏΠΎ ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΡΠΌ (ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΠΌ) ΡΠ½ΠΈΠΌΠΊΠ°ΠΌ, ΡΠ°ΠΊ ΠΈ ΠΏΠΎ ΡΠ°Π·Π½ΠΎΡΠΎΠ΄Π½ΡΠΌ (ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠΌ ΠΈ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΠΌ) ΡΠ½ΠΈΠΌΠΊΠ°ΠΌ. ΠΡΠ»ΠΈΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠ΅ΡΡΠΎΠΉ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠΎΠ³ΠΎ ΡΠΏΠΎΡΠΎΠ±Π° ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ»ΡΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΈΡΠΎΠ³ΠΎΠ²ΠΎΠ³ΠΎ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π·Π΅ΠΌΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ
Formation of Fused Images of the Land Surface from Radar and Optical Images in Spatially Distributed On-Board Operational Monitoring Systems
This paper considers the issues of image fusion in a spatially distributed small-size on-board location system for operational monitoring. The purpose of this research is to develop a new method for the formation of fused images of the land surface based on data obtained from optical and radar devices operated from two-position spatially distributed systems of small aircraft, including unmanned aerial vehicles. The advantages of the method for integrating information from radar and optical information-measuring systems are justified. The combined approach allows removing the limitations of each separate system. The practicality of choosing the integration of information from several widely used variants of heterogeneous sources is shown. An iterative approach is used in the method for combining multi-angle location images. This approach improves the quality of synthesis and increases the accuracy of integration, as well as improves the information content and reliability of the final fused image by using the pixel clustering algorithm, which produces many partitions into clusters. The search for reference points on isolated contours is carried out on a pair of left and right images of the docked image from the selected partition. For these reference points, a functional transformation is determined. Having applied it to the original multi-angle heterogeneous images, the degree of correlation of the fused image is assessed. Both the position of the reference points of the contour and the desired functional transformation itself are refined until the quality assessment of the fusion becomes acceptable. The type of functional transformation is selected based on clustered images and then applied to the original multi-angle heterogeneous images. This process is repeated for clustered images with greater granularity in case if quality assessment of the fusion is considered to be poor. At each iteration, there is a search for pairs of points of the contour of the isolated areas. Areas are isolated with the use of two image segmentation methods. Experiments on the formation of fused images are presented. The result of the research is the proposed method for integrating information obtained from a two-position airborne small-sized radar system and an optical location system. The implemented method can improve the information content, quality, and reliability of the finally established fused image of the land surface
A Model of Pixel and Superpixel Clustering for Object Detection
The paper presents a model of structured objects in a grayscale or color image, described by means of optimal piecewise constant image approximations, which are characterized by the minimum possible approximation errors for a given number of pixel clusters, where the approximation error means the total squared error. An ambiguous image is described as a non-hierarchical structure but is represented as an ordered superposition of object hierarchies, each containing at least one optimal approximation in g0 = 1, 2,..., etc., colors. For the selected hierarchy of pixel clusters, the objects-of-interest are detected as the pixel clusters of optimal approximations, or as their parts, or unions. The paper develops the known idea in cluster analysis of the joint application of Wardβs and K-means methods. At the same time, it is proposed to modernize each of these methods and supplement them with a third method of splitting/merging pixel clusters. This is useful for cluster analysis of big data described by a convex dependence of the optimal approximation error on the cluster number and also for adjustable object detection in digital image processing, using the optimal hierarchical pixel clustering, which is treated as an alternative to the modern informally defined βsemanticβ segmentation
βApplied Orientalismβ in British India and Tsarist Turkestan
βWe cannot promise to those who may choose Oriental scholarship, that they shall find themselves abreast, in all the various high-roads of life which lead to profit and distinction, with the men who shall have devoted themselves to acquiring the knowledge which in these days is power, the intellectual treasures which make fifty years of Europe better than a cycle in Cathay, which are the sinews of peaceful empire as surely as money is the sinew of war.
Recueil de voyages et de mΓ©moires. Tome 5 / , publiΓ© par la SociΓ©tΓ© de gΓ©ographie
Comprend : Voyages de Marco Polo ; Peregrinatio Marci Pauli ; Relation de Ghanat et des coutumes de ses habitans ; Recherches sur les antiquitΓ©s des Etats-Unis de l'AmΓ©rique septentrionale ; Orographie de l'Europe ; Description des merveilles d'une partie de l'Asie ; GΓ©ographie d'Edrisi ; Grammaire et dictionnaire abrΓ©gΓ©s de la langue berbΓ¨re ; ItinΓ©raires de l'Afrique septentrionale ; MΓ©moire sur la partie mΓ©ridionale de l'Asie centrale ; MΓ©moire sur l'ethnographie de la PerseAppartient Γ lβensemble documentaire : Sinica1Appartient Γ lβensemble documentaire : RfnEns0Appartient Γ lβensemble documentaire : RfnAfn1Appartient Γ lβensemble documentaire : RfnCoop1Appartient Γ lβensemble documentaire : FranceJp0Appartient Γ lβensemble documentaire : BbLevt