.png){kind=icon}
返回
Unified Optimization for Multiple Active Object Recognition Tasks with Feature Decision Tree
.png){kind=icon}
PRE
AI
DOI：10.1007/s10846-021-01488-x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Visual object recognition plays an important role in the fields of computer vision and robotics. Static analysis of an image from a single viewpoint may not contain enough information to recognize an object unambiguously. Active object recognition (AOR) is aimed at collecting additional information to reduce ambiguity by purposefully adjusting the viewpoint of an observer. Existing AOR methods are oriented to a single task whose goal is to recognize an object by the minimum number of viewpoints. This paper presents a novel framework to deal with multiple AOR tasks based on feature decision tree (FDT). In the framework, in the light of the distribution of predetermined features on each object in a model base, a prior feature distribution table is firstly created as a kind of prior knowledge. Then it is utilized for the construction of FDT which describes the transition process of recognition states when different viewpoints are selected. Finally, in order to determine the next best viewpoints for the tasks with different goals, a unified optimization problem is established and solved by tree dynamic programming algorithm. In addition, the existing evaluation method of viewpoint planning (VP) efficiency is improved. According to whether the prior probability of the appearance of each object is known, the VP efficiency of different tasks is evaluated respectively. Experiments on the simulation and real environment show that the proposed framework obtains rather promising results in different AOR tasks.
Keyword:
Active object recognition
Prior feature distribution table
Feature decision tree
Next best viewpoint
期刊
J
IF:
2.8
论文数:
3.9K
被引数:
6.9K
机构
引用论文
Doses of GBR12909 that suppress cocaine self-administration in non-human primates substantially occupy dopamine transporters as measured by [11C] WIN35,428 PET scans.png)
Synapse
SynapseIF0
PREAI
分享收藏
Active object recognition using hierarchical local-receptive-field-based extreme learning machineMEMETIC COMPUTING
MEMETIC COMPUTINGIF2.3
PREAI
分享收藏P300 analysis techniques in cognitive impairment after brain injury: Comparison with neuropsychological and imaging dataBrain Injury
Brain InjuryIF0
PREAI
分享收藏
查看更多内容