.png){kind=icon}
返回
Multi-Circle Detection Guided by Multimodal Optimization Scheme
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3270429.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Automatic circle detection is one of the most important construction elements to design more complex industrial image tasks such as target detection, manufacture inspection and process control. Most of the literature concerning multi-circle detection can be categorized under deterministic or stochastic perspectives. Deterministic approaches combine geometric and histogram information to identify circle shapes. However, deterministic techniques are unable to detect circles in presence of noise, shape variance, or occlusion. On the other hand, stochastic methodologies such as metaheuristic algorithms have been also proposed as alternatives to deterministic approaches for circle detection. Although these techniques are in general more robust, they allow detecting only one circle per execution, translating the detection problem into a unimodal optimization problem. Under such conditions, it is necessary to execute multiple times the algorithm to identify all the circles contained in the image. In this paper, the multi-circle detection problem has been reformulated as a multimodal optimization problem. The proposed approach adopts the Multimodal Flower Pollination Algorithm (MFPA) to detect all the circular instances contained in the image. Experimental results suggest that the proposed approach significantly improves the multi-circle detection problem.
Keyword:
Circle detection
evolutionary algorithms
flower pollination algorithm (FPA)
multimodal optimization
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Kinematic and kinetic differences in the execution of vertical jumps between people with good and poor ankle joint dorsiflexion踝关节背屈良好和不良的人在执行垂直跳跃时的运动学和动力学差异
PREAI
分享收藏分享收藏EDCircles: A real-time circle detector with a false detection controlEDCircles: 具有错误检测控制的实时圆形检测器.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏分享收藏
查看更多内容