This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to “see” around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a ground-level semantic map that covers a larger area than can be built using the onboard sensors.
標簽： simultaneously difficulties limitation addresses
上傳時間： 2014-06-11
上傳用戶：waitingfy
in this document, i make a compartion between two algorithms, computational cost, algorithm analysis, on c. excuse my english
標簽： computational compartion algorithms algorithm
上傳時間： 2013-12-20
上傳用戶：小碼農lz
臨床醫藥試驗：Gehan s Two Stage Design
上傳時間： 2013-12-10
上傳用戶：qw12
臨床醫藥試驗:Simon s Two stage Design
上傳時間： 2014-01-10
上傳用戶：weiwolkt
*** *** *** *** *** *** ***** ** Two wire/I2C Bus READ/WRITE Sample Routines of Microchip s ** 24Cxx / 85Cxx serial CMOS EEPROM interfacing to a ** PIC16C54 8-bit CMOS single chip microcomputer ** Revsied Version 2.0 (4/2/92). ** ** Part use = PIC16C54-XT/JW ** Note: 1) All timings are based on a reference crystal frequency of 2MHz ** which is equivalent to an instruction cycle time of 2 usec. ** 2) Address and literal values are read in octal unless otherwise ** specified.
標簽： Microchip Routines Sample WRITE
上傳時間： 2013-12-27
上傳用戶：ljmwh2000
Muscl Euler Two dimensions
標簽： dimensions Muscl Euler Two
上傳時間： 2013-12-07
上傳用戶：363186
measure through the cross-entropy of test data. In addition, we introduce two novel smoothing techniques, one a variation of Jelinek-Mercer smoothing and one a very simple linear interpolation technique, both of which outperform existing methods.
標簽： cross-entropy introduce smoothing addition
上傳時間： 2014-01-06
上傳用戶：qilin
％ Train a two layer neural network with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is possible to use regularization by ％ weight decay. Also pruned (ie. not fully connected) networks can ％ be trained. ％ ％ Given a set of corresponding input-output pairs and an initial ％ network, ％ [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms) ％ trains the network with the Levenberg-Marquardt method. ％ ％ The activation functions can be either linear or tanh. The ％ network architecture is defined by the matrix NetDef which ％ has two rows. The first row specifies the hidden layer and the ％ second row specifies the output layer.
標簽： Levenberg-Marquardt desired network neural
上傳時間： 2016-12-27
上傳用戶：jcljkh
Train a two layer neural network with a recursive prediction error ％ algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully ％ connected) networks can be trained. ％ ％ The activation functions can either be linear or tanh. The network ％ architecture is defined by the matrix NetDef , which has of two ％ rows. The first row specifies the hidden layer while the second ％ specifies the output layer.
標簽： recursive prediction algorithm Gauss-Ne
上傳時間： 2016-12-27
上傳用戶：ljt101007
This work briefly explains common cryptosystems and details two most popular private-key ciphers: DES ,which is probably the most widely used, and AES, which is intended to replace DES.
標簽： cryptosystems private-key explains briefly
上傳時間： 2013-12-20
上傳用戶：xfbs821
