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计算机图像图形处理中的点位预测和噪点分析 被引量:1
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作者 宁玉门 《信息记录材料》 2021年第5期185-186,共2页
图像图形处理是以图像图形软件为基础的后期图像处理技术,能够更精细、更高效地处理图像图形,提高图像图形处理的质量与效率。其中,点位预测与噪点分析在计算机图像图形处理中的涉及范围较广,能够切实提高图像图形处理的精准性与有效性... 图像图形处理是以图像图形软件为基础的后期图像处理技术,能够更精细、更高效地处理图像图形,提高图像图形处理的质量与效率。其中,点位预测与噪点分析在计算机图像图形处理中的涉及范围较广,能够切实提高图像图形处理的精准性与有效性。对此本文结合计算机图形处理的基本原理,分别探究点位预测与噪点分析的应用机制。 展开更多
关键词 计算机图像图形处理 点位预测 噪点分析
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钢纤维混凝土细观的参数测量
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作者 刘福华 刘福平 宋万明 《武汉大学学报(工学版)》 CAS CSCD 北大核心 2006年第4期115-118,共4页
针对钢纤维混凝土研究中缺乏有效纤维分布细观几何参数检测手段的状况,提出了基于计算机图形图像技术的纤维分布细观几何参数的测量方法,并论述了该方法的测量原理,根据钢纤维混凝土图像的特点,设计了对图像进行图像分割、噪声消除、特... 针对钢纤维混凝土研究中缺乏有效纤维分布细观几何参数检测手段的状况,提出了基于计算机图形图像技术的纤维分布细观几何参数的测量方法,并论述了该方法的测量原理,根据钢纤维混凝土图像的特点,设计了对图像进行图像分割、噪声消除、特征提取、直线拟合等计算机图形图像处理、然后进行参数测量的方案.对曲靖—陆良高速公路钢纤维混凝土试验路面进行了纤维分布细观几何参数测量实验,实验表明,测量数据准确可靠. 展开更多
关键词 复合材料 钢纤维混凝土 计算机图像图形处理 细观几何参数 测量
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A Skeleton Extraction Framework Based on Inner-Product and Border Gap
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作者 曾培峰 唐莉萍 《Journal of Donghua University(English Edition)》 EI CAS 2012年第5期412-416,共5页
A novel algorithm for skeleton extraction is proposed in the paper. By numbering objeet's border dements on spatial position, the border gap (BG) of inner pixel of the object is calculated; an 8-connected medial-ax... A novel algorithm for skeleton extraction is proposed in the paper. By numbering objeet's border dements on spatial position, the border gap (BG) of inner pixel of the object is calculated; an 8-connected medial-axis hierarchy is derived by the BG; a thinning method including slicing and counting is proposed to improve the processing speed; branches with minor importance are truncated by vector diversity Vd and length-width ratio (LWR) with support vector machine (SVM) classifier. Experiments demonstrate that the derived skeletons keep good connectivity, especially in long and narrow area. 展开更多
关键词 SKELETON inner-product medial-axis border gap (BG) distance transform
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Online Sequential Extreme Multilayer Perception with Time Series Learning Machine Based Output Self Feedback for Prediction 被引量:5
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作者 PAN Feng ZHAO Hai-bo 《Journal of Shanghai Jiaotong university(Science)》 EI 2013年第3期366-375,共10页
This study presents a time series prediction model with output self feedback which is implemented based on online sequential extreme learning machine. The output variables derived from multilayer perception can feedba... This study presents a time series prediction model with output self feedback which is implemented based on online sequential extreme learning machine. The output variables derived from multilayer perception can feedback to the network input layer to create a temporal relation between the current node inputs and the lagged node outputs while overcoming the limitation of memory which is a vital port for any time-series prediction application. The model can overcome the static prediction problem with most time series prediction models and can effectively cope with the dynamic properties of time series data. A linear and a nonlinear forecasting algorithms based on online extreme learning machine are proposed to implement the output feedback forecasting model. They are both recursive estimator and have two distinct phases: Predict and Update. The proposed model was tested against different kinds of time series data and the results indicate that the model outperforms the original static model without feedback. 展开更多
关键词 time series prediction extreme learning machine (ELM) autoregression (AR) online sequential learning ELM (OS-ELM) recurrent neural network (RNN)
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