A high-speed comer detection algorithm based on fuzzy ID3 decision tree was proposed. In the algorithm, the Bresenham circle with 3-pixel radius was used as the test mask, overlapping the candidate comers with the nuc...A high-speed comer detection algorithm based on fuzzy ID3 decision tree was proposed. In the algorithm, the Bresenham circle with 3-pixel radius was used as the test mask, overlapping the candidate comers with the nucleus. Connected pixels on the circle were applied to compare the intensity value with the nucleus, with the membership function used to give the fuzzy result. The pixel with maximum information gain was chosen as the parent node to build a binary decision tree. Thus, the comer detector was derived. The pictures taken in Fengtai Railway Station in Beijing were used to test the method. The experimental results show that when the number of pixels on the test mask is chosen to be 9, best result can be obtained. The comer detector significantly outperforms existing detector in computational efficiency without sacrificing the quality and the method also provides high performance against Poisson noise and Gaussian blur.展开更多
针对多视觉任务中传输成本高、解码端计算压力大的问题,提出一种自适应可伸缩视频编码(adaptive scalable video coding,ASVC)传输框架,将视频分为语义层和背景层,分别传输语义和背景信息。此外,提出一种自适应压缩算法,构建了C4.5决策...针对多视觉任务中传输成本高、解码端计算压力大的问题,提出一种自适应可伸缩视频编码(adaptive scalable video coding,ASVC)传输框架,将视频分为语义层和背景层,分别传输语义和背景信息。此外,提出一种自适应压缩算法,构建了C4.5决策树模型分析网络环境对视频进行压缩的决策判定,并对帧序列进行光流分析,在保留变化显著的帧基础上引入插值机制保持图像的平滑性。仿真结果表明,ASVC方法在不同码率环境下表现更高的识别精准率,视频质量和传输效率的显著提升。展开更多
为提高林业运输车辆的保险杠喷涂合格率,以某公司汽车保险杠的涂装质量数据为例,运用排列图对涂装质量数据进行分析得出颗粒、桔皮属于影响喷涂质量的主要因素。从人、机、料、法、环5个方面分析产生颗粒、桔皮的主要原因,并采用斯皮尔...为提高林业运输车辆的保险杠喷涂合格率,以某公司汽车保险杠的涂装质量数据为例,运用排列图对涂装质量数据进行分析得出颗粒、桔皮属于影响喷涂质量的主要因素。从人、机、料、法、环5个方面分析产生颗粒、桔皮的主要原因,并采用斯皮尔曼(Spearman)相关系数进行特征提取以及重要性分析,得出喷漆房的温度、喷漆房相对湿度、机器人喷涂流量、旋杯转速、喷涂距离、喷涂速度、漆的品牌、机器人(机器①、机器②、…、机器⑥)均会影响保险杠的喷涂质量。运用决策树(classification and regression tree,CART)算法确定喷涂流量、喷漆房的温度、喷漆房相对湿度、机器人②和机器人④、漆的品牌是影响保险杠喷涂质量的较为关键的因素。结果表明,利用CART分类算法对喷涂质量数据进行分析能够实现对故障点的判别,对于提高保险杠喷涂质量具有借鉴意义。展开更多
In machine learning,randomness is a crucial factor in the success of ensemble learning,and it can be injected into tree-based ensembles by rotating the feature space.However,it is a common practice to rotate the featu...In machine learning,randomness is a crucial factor in the success of ensemble learning,and it can be injected into tree-based ensembles by rotating the feature space.However,it is a common practice to rotate the feature space randomly.Thus,a large number of trees are required to ensure the performance of the ensemble model.This random rotation method is theoretically feasible,but it requires massive computing resources,potentially restricting its applications.A multimodal genetic algorithm based rotation forest(MGARF)algorithm is proposed in this paper to solve this problem.It is a tree-based ensemble learning algorithm for classification,taking advantage of the characteristic of trees to inject randomness by feature rotation.However,this algorithm attempts to select a subset of more diverse and accurate base learners using the multimodal optimization method.The classification accuracy of the proposed MGARF algorithm was evaluated by comparing it with the original random forest and random rotation ensemble methods on 23 UCI classification datasets.Experimental results show that the MGARF method outperforms the other methods,and the number of base learners in MGARF models is much fewer.展开更多
基金Project(J2008X011) supported by the Natural Science Foundation of Ministry of Railway and Tsinghua University,China
文摘A high-speed comer detection algorithm based on fuzzy ID3 decision tree was proposed. In the algorithm, the Bresenham circle with 3-pixel radius was used as the test mask, overlapping the candidate comers with the nucleus. Connected pixels on the circle were applied to compare the intensity value with the nucleus, with the membership function used to give the fuzzy result. The pixel with maximum information gain was chosen as the parent node to build a binary decision tree. Thus, the comer detector was derived. The pictures taken in Fengtai Railway Station in Beijing were used to test the method. The experimental results show that when the number of pixels on the test mask is chosen to be 9, best result can be obtained. The comer detector significantly outperforms existing detector in computational efficiency without sacrificing the quality and the method also provides high performance against Poisson noise and Gaussian blur.
文摘针对多视觉任务中传输成本高、解码端计算压力大的问题,提出一种自适应可伸缩视频编码(adaptive scalable video coding,ASVC)传输框架,将视频分为语义层和背景层,分别传输语义和背景信息。此外,提出一种自适应压缩算法,构建了C4.5决策树模型分析网络环境对视频进行压缩的决策判定,并对帧序列进行光流分析,在保留变化显著的帧基础上引入插值机制保持图像的平滑性。仿真结果表明,ASVC方法在不同码率环境下表现更高的识别精准率,视频质量和传输效率的显著提升。
文摘为提高林业运输车辆的保险杠喷涂合格率,以某公司汽车保险杠的涂装质量数据为例,运用排列图对涂装质量数据进行分析得出颗粒、桔皮属于影响喷涂质量的主要因素。从人、机、料、法、环5个方面分析产生颗粒、桔皮的主要原因,并采用斯皮尔曼(Spearman)相关系数进行特征提取以及重要性分析,得出喷漆房的温度、喷漆房相对湿度、机器人喷涂流量、旋杯转速、喷涂距离、喷涂速度、漆的品牌、机器人(机器①、机器②、…、机器⑥)均会影响保险杠的喷涂质量。运用决策树(classification and regression tree,CART)算法确定喷涂流量、喷漆房的温度、喷漆房相对湿度、机器人②和机器人④、漆的品牌是影响保险杠喷涂质量的较为关键的因素。结果表明,利用CART分类算法对喷涂质量数据进行分析能够实现对故障点的判别,对于提高保险杠喷涂质量具有借鉴意义。
基金Project(61603274)supported by the National Natural Science Foundation of ChinaProject(2017KJ249)supported by the Research Project of Tianjin Municipal Education Commission,China。
文摘In machine learning,randomness is a crucial factor in the success of ensemble learning,and it can be injected into tree-based ensembles by rotating the feature space.However,it is a common practice to rotate the feature space randomly.Thus,a large number of trees are required to ensure the performance of the ensemble model.This random rotation method is theoretically feasible,but it requires massive computing resources,potentially restricting its applications.A multimodal genetic algorithm based rotation forest(MGARF)algorithm is proposed in this paper to solve this problem.It is a tree-based ensemble learning algorithm for classification,taking advantage of the characteristic of trees to inject randomness by feature rotation.However,this algorithm attempts to select a subset of more diverse and accurate base learners using the multimodal optimization method.The classification accuracy of the proposed MGARF algorithm was evaluated by comparing it with the original random forest and random rotation ensemble methods on 23 UCI classification datasets.Experimental results show that the MGARF method outperforms the other methods,and the number of base learners in MGARF models is much fewer.
