the fuction performs thresholding to the eye images. this function can be used in implementing daugman algo for iris localization.
标签： implementing thresholding the performs
上传时间： 2017-03-23
上传用户：trepb001
Internationalization The Java盲 2 platform provides internationalization features that let you separate culturally dependent data from the application (internationalization) and adapt it to as many cultures as needed (localization).
标签： Internationalization internationalization platform features
上传时间： 2017-04-29
上传用户：hakim
Learn how to: * Tokenize a null-terminated string * Create a search and replace function for strings * Implement subtraction for string objects * Use the vector, deque, and list sequence containers * Use the container adaptors stack, queue, and priority_queue * Use the map, multimap, set, and multiset associative containers * Reverse, rotate, and shuffle a sequence * Create a function object * Use binders, negators, and iterator adapters * Read and write files * Use stream iterators to handle file I/O * Use exceptions to handle I/O errors * Create custom inserters and extractors * Format date, time, and numeric data * Use facets and the localization library * Overload the [ ], ( ), and -> operators * Create an explicit constructor * And much, much more
标签： null-terminated Tokenize Create string
上传时间： 2014-01-18
上传用户：yph853211
This C++ application demonstrates how to display Chinese characters from resource files. The application supports internationalization and localization. In the updated version also the context-sensitive help has been added. The application supports following languages: English, Taiwan Chinese, Taiwan English, HongKong Chinese, HongKong English, Mainland Chinese, and Mainland English. Main classes: TFontSpec, CCnvCharacterSetConverter
标签： demonstrates application characters resource
上传时间： 2014-01-09
上传用户：hakim
We consider the problem of target localization by a network of passive sensors. When an unknown target emits an acoustic or a radio signal, its position can be localized with multiple sensors using the time difference of arrival (TDOA) information. In this paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex optimization problem.We also propose a formulation for robust target localization in the presence of sensor location errors. Two Cramer-Rao bounds are derived corresponding to situations with and without sensor node location errors. Simulation results confirm the efficiency and superior performance of the convex relaxation approach as compared to the existing least squares based approach when large sensor node location errors are present.
标签： 传感器网络
上传时间： 2016-11-27
上传用户：xxmluo
