Gestures recognition is of great importance to intelligent human-computer interaction technology, but it is also very difficult to deal with, especially when the environment is quite complex. In this paper, the recogn...Gestures recognition is of great importance to intelligent human-computer interaction technology, but it is also very difficult to deal with, especially when the environment is quite complex. In this paper, the recognition algorithm of dynamic and combined gestures, which based on multi-feature fusion, is proposed. Firstly, in image segmentation stage, the algorithm extracts interested region of gestures in color and depth map by combining with the depth information. Then, to establish support vector machine （SVM） model for static hand gestures recognition, the algorithm fuses weighted Hu invariant moments of depth map into the Histogram of oriented gradients （HOG） of the color image. Finally, an hidden Markov model （HMM） toolbox supporting multi-dimensional continuous data input is adopted to do the training and recognition. Experimental results show that the proposed algorithm can not only overcome the influence of skin object, multi-object moving and hand gestures interference in the background, but also real-time and practical in Human-Computer interaction.展开更多
How high-level emotional representation of art paintings can be inferred from percep tual level features suited for the particular classes (dynamic vs. static classification)is presented. The key points are feature se...How high-level emotional representation of art paintings can be inferred from percep tual level features suited for the particular classes (dynamic vs. static classification)is presented. The key points are feature selection and classification. According to the strong relationship between notable lines of image and human sensations, a novel feature vector WLDLV (Weighted Line Direction-Length Vector) is proposed, which includes both orientation and length information of lines in an image. Classification is performed by SVM (Support Vector Machine) and images can be classified into dynamic and static. Experimental results demonstrate the effectiveness and superiority of the algorithm.展开更多
基金supported by the National Ministries Foundation of China (Y42013040181)the National Ministries Research of Twelfth Five projects (Y31011040315)the Fundamental Research Funds for the Central Universities (NSIY191414)
文摘Gestures recognition is of great importance to intelligent human-computer interaction technology, but it is also very difficult to deal with, especially when the environment is quite complex. In this paper, the recognition algorithm of dynamic and combined gestures, which based on multi-feature fusion, is proposed. Firstly, in image segmentation stage, the algorithm extracts interested region of gestures in color and depth map by combining with the depth information. Then, to establish support vector machine （SVM） model for static hand gestures recognition, the algorithm fuses weighted Hu invariant moments of depth map into the Histogram of oriented gradients （HOG） of the color image. Finally, an hidden Markov model （HMM） toolbox supporting multi-dimensional continuous data input is adopted to do the training and recognition. Experimental results show that the proposed algorithm can not only overcome the influence of skin object, multi-object moving and hand gestures interference in the background, but also real-time and practical in Human-Computer interaction.
文摘How high-level emotional representation of art paintings can be inferred from percep tual level features suited for the particular classes (dynamic vs. static classification)is presented. The key points are feature selection and classification. According to the strong relationship between notable lines of image and human sensations, a novel feature vector WLDLV (Weighted Line Direction-Length Vector) is proposed, which includes both orientation and length information of lines in an image. Classification is performed by SVM (Support Vector Machine) and images can be classified into dynamic and static. Experimental results demonstrate the effectiveness and superiority of the algorithm.
