文件名称:find--k-best-1.00
介绍说明－－下载内容均来自于网络，请自行研究使用
Implementation of the Murty algorithm to obtain the best K assignments. Includes the implementation of the Jonker-Volgenant algorithm.
Usual applications are multiple target tracking algorithms, Joint Probabilistic Data Association (JPDA), Multiple Hypothesis Tracking (MHT), Global Nearest Neighbours (GNN), etc.
Based on the original code by Jonker-Volgenant, and the Murty Matlab implementation by Eric Trautmann
Can be used both from Java or Matlab.
Time for a 100x100 assignment problem, with k=20:
• Matlab implementation: 54.5 sec
• Java implementation: 1 sec
-Implementation of the Murty algorithm to obtain the best K assignments. Includes the implementation of the Jonker-Volgenant algorithm.
Usual applications are multiple target tracking algorithms, Joint Probabilistic Data Association (JPDA), Multiple Hypothesis Tracking (MHT), Global Nearest Neighbours (GNN), etc.
Based on the original code by Jonker-Volgenant, and the Murty Matlab implementation by Eric Trautmann
Can be used both from Java or Matlab.
Time for a 100x100 assignment problem, with k=20:
• Matlab implementation: 54.5 sec
• Java implementation: 1 sec
Usual applications are multiple target tracking algorithms, Joint Probabilistic Data Association (JPDA), Multiple Hypothesis Tracking (MHT), Global Nearest Neighbours (GNN), etc.
Based on the original code by Jonker-Volgenant, and the Murty Matlab implementation by Eric Trautmann
Can be used both from Java or Matlab.
Time for a 100x100 assignment problem, with k=20:
• Matlab implementation: 54.5 sec
• Java implementation: 1 sec
-Implementation of the Murty algorithm to obtain the best K assignments. Includes the implementation of the Jonker-Volgenant algorithm.
Usual applications are multiple target tracking algorithms, Joint Probabilistic Data Association (JPDA), Multiple Hypothesis Tracking (MHT), Global Nearest Neighbours (GNN), etc.
Based on the original code by Jonker-Volgenant, and the Murty Matlab implementation by Eric Trautmann
Can be used both from Java or Matlab.
Time for a 100x100 assignment problem, with k=20:
• Matlab implementation: 54.5 sec
• Java implementation: 1 sec
(系统自动生成,下载前可以参看下载内容)
下载文件列表
java-k-best-1.00\src\com\google\code\javakbest\Murty.java
................\...\...\......\....\.........\JVC.java
................\...\...\......\....\.........\PartitionNode.java
................\...\...\......\....\.........\Test.java
................\...\...\......\....\.........\Node.java
................\README.TXT
................\java-k-best.jar
................\findkbest.m
................\LICENSE
................\src\com\google\code\javakbest
................\...\...\......\code
................\...\...\google
................\...\com
................\src
java-k-best-1.00
................\...\...\......\....\.........\JVC.java
................\...\...\......\....\.........\PartitionNode.java
................\...\...\......\....\.........\Test.java
................\...\...\......\....\.........\Node.java
................\README.TXT
................\java-k-best.jar
................\findkbest.m
................\LICENSE
................\src\com\google\code\javakbest
................\...\...\......\code
................\...\com
................\src
java-k-best-1.00