Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper,...Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network（PNN） where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rocktypes. Overall the error of mis-classification is below 6%. When compared with other three classification algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.展开更多
Real-time hand gesture recognition technology significantly improves the user's experience for virtual reality/augmented reality(VR/AR) applications, which relies on the identification of the orientation of the ha...Real-time hand gesture recognition technology significantly improves the user's experience for virtual reality/augmented reality(VR/AR) applications, which relies on the identification of the orientation of the hand in captured images or videos. A new three-stage pipeline approach for fast and accurate hand segmentation for the hand from a single depth image is proposed. Firstly, a depth frame is segmented into several regions by histogrambased threshold selection algorithm and by tracing the exterior boundaries of objects after thresholding. Secondly, each segmentation proposal is evaluated by a three-layers shallow convolutional neural network(CNN) to determine whether or not the boundary is associated with the hand. Finally, all hand components are merged as the hand segmentation result. Compared with algorithms based on random decision forest(RDF), the experimental results demonstrate that the approach achieves better performance with high-accuracy(88.34% mean intersection over union, mIoU) and a shorter processing time(≤8 ms).展开更多
针对手势识别由于分割效果差,导致识别率较低等问题,提出基于改进支持向量机的动态多点手势动作识别方法。选用深度阈值法分割动态多点手势图像,提取出手掌中最大的圆细化手部区域,获取7维手部HOG(Histogram of Oriented Gradients)特...针对手势识别由于分割效果差,导致识别率较低等问题,提出基于改进支持向量机的动态多点手势动作识别方法。选用深度阈值法分割动态多点手势图像,提取出手掌中最大的圆细化手部区域,获取7维手部HOG(Histogram of Oriented Gradients)特征向量,完成手势动作图像预处理。引入支持向量机,并且通过误差项改进该算法。采用改进后的支持向量机最优线性分类特征向量,利用支持向量机输入分类后的手势特征向量,实现动态多点手势动作识别。实验结果表明,所提方法受光照影响波动小,在有光照情况下,识别率达到92.5%以上,而无光照情况下,识别率仍高于90.0%,并且图像分割信息完整、识别准确性高。展开更多
文摘Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network（PNN） where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rocktypes. Overall the error of mis-classification is below 6%. When compared with other three classification algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.
文摘Real-time hand gesture recognition technology significantly improves the user's experience for virtual reality/augmented reality(VR/AR) applications, which relies on the identification of the orientation of the hand in captured images or videos. A new three-stage pipeline approach for fast and accurate hand segmentation for the hand from a single depth image is proposed. Firstly, a depth frame is segmented into several regions by histogrambased threshold selection algorithm and by tracing the exterior boundaries of objects after thresholding. Secondly, each segmentation proposal is evaluated by a three-layers shallow convolutional neural network(CNN) to determine whether or not the boundary is associated with the hand. Finally, all hand components are merged as the hand segmentation result. Compared with algorithms based on random decision forest(RDF), the experimental results demonstrate that the approach achieves better performance with high-accuracy(88.34% mean intersection over union, mIoU) and a shorter processing time(≤8 ms).
文摘针对手势识别由于分割效果差,导致识别率较低等问题,提出基于改进支持向量机的动态多点手势动作识别方法。选用深度阈值法分割动态多点手势图像,提取出手掌中最大的圆细化手部区域,获取7维手部HOG(Histogram of Oriented Gradients)特征向量,完成手势动作图像预处理。引入支持向量机,并且通过误差项改进该算法。采用改进后的支持向量机最优线性分类特征向量,利用支持向量机输入分类后的手势特征向量,实现动态多点手势动作识别。实验结果表明,所提方法受光照影响波动小,在有光照情况下,识别率达到92.5%以上,而无光照情况下,识别率仍高于90.0%,并且图像分割信息完整、识别准确性高。
