The Experts below are selected from a list of 62415 Experts worldwide ranked by ideXlab platform
Jianyong Sun - One of the best experts on this subject based on the ideXlab platform.
-
User adaptation for online sketchy Shape Recognition
Lecture Notes in Computer Science, 2004Co-Authors: Zhengxing Sun, Wenyin Liu, Bin-bin Peng, Bin Zhang, Jianyong SunAbstract:This paper presents a method of online sketchy Shape Recognition that can adapt to different user sketching styles. The adaptation principle is based on incremental active learning and dynamic user modeling. Incremental active learning is used for sketchy stroke classification such that important data can actively be selected to train the classifiers. Dynamic user modeling is used to model the user's sketching style in an incremental decision tree, which is then used to recognize the composite Shapes dynamically by means of fuzzy matching. Experiments prove the proposed method both effective and efficient for user adaptation in online sketchy Shape Recognition.
-
GREC - User Adaptation for Online Sketchy Shape Recognition
Graphics Recognition. Recent Advances and Perspectives, 2004Co-Authors: Zhengxing Sun, Wenyin Liu, Bin-bin Peng, Bin Zhang, Jianyong SunAbstract:This paper presents a method of online sketchy Shape Recognition that can adapt to different user sketching styles. The adaptation principle is based on incremental active learning and dynamic user modeling. Incremental active learning is used for sketchy stroke classification such that important data can actively be selected to train the classifiers. Dynamic user modeling is used to model the user's sketching style in an incremental decision tree, which is then used to recognize the composite Shapes dynamically by means of fuzzy matching. Experiments prove the proposed method both effective and efficient for user adaptation in online sketchy Shape Recognition.
Zhengxing Sun - One of the best experts on this subject based on the ideXlab platform.
-
User adaptation for online sketchy Shape Recognition
Lecture Notes in Computer Science, 2004Co-Authors: Zhengxing Sun, Wenyin Liu, Bin-bin Peng, Bin Zhang, Jianyong SunAbstract:This paper presents a method of online sketchy Shape Recognition that can adapt to different user sketching styles. The adaptation principle is based on incremental active learning and dynamic user modeling. Incremental active learning is used for sketchy stroke classification such that important data can actively be selected to train the classifiers. Dynamic user modeling is used to model the user's sketching style in an incremental decision tree, which is then used to recognize the composite Shapes dynamically by means of fuzzy matching. Experiments prove the proposed method both effective and efficient for user adaptation in online sketchy Shape Recognition.
-
GREC - User Adaptation for Online Sketchy Shape Recognition
Graphics Recognition. Recent Advances and Perspectives, 2004Co-Authors: Zhengxing Sun, Wenyin Liu, Bin-bin Peng, Bin Zhang, Jianyong SunAbstract:This paper presents a method of online sketchy Shape Recognition that can adapt to different user sketching styles. The adaptation principle is based on incremental active learning and dynamic user modeling. Incremental active learning is used for sketchy stroke classification such that important data can actively be selected to train the classifiers. Dynamic user modeling is used to model the user's sketching style in an incremental decision tree, which is then used to recognize the composite Shapes dynamically by means of fuzzy matching. Experiments prove the proposed method both effective and efficient for user adaptation in online sketchy Shape Recognition.
Huang Jing - One of the best experts on this subject based on the ideXlab platform.
-
Hand-drawn Shape Recognition based on k-segment algorithm for principal curves
Journal of Computer Applications, 2009Co-Authors: Huang JingAbstract:A hand-drawn Shape Recognition algorithm based on improved K-segment principal curve was presented.The algorithm was to generate a curve passing through the "middle" of the distribution.Recognition was based on the average distance from the sample points to the projected points on the generated principal curve.Experimental results show that the algorithm is feasible for hand-drawn Shape Recognition and exhibits good results.
Fumio Kishino - One of the best experts on this subject based on the ideXlab platform.
-
Real time hand Shape Recognition for man-machine interfaces
[Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics, 1992Co-Authors: K. Ishibuchi, Haruo Takemura, Fumio KishinoAbstract:The authors describe a hand Shape Recognition method for man-machine interfaces using a pipeline image processor. The basic strategy is to use simple but effective real-time dynamical image processing to enhance the reliability of hand Shape Recognition. Low-level image processing reduces the data required for Recognition of a hand Shape. An algorithm for a noncontact-type hand Shape recognizer has been developed for incorporation into a virtual reality environment. To realize this recognizer, the most important considerations are processing speed, the pointer's positional accuracy, and command Recognition rate. The usefulness of the proposed method is verified through experimental results for these three points
-
Real time hand Shape Recognition using pipe-line image processor
[1992] Proceedings IEEE International Workshop on Robot and Human Communication, 1Co-Authors: K. Ishibuchi, Haruo Takemura, Fumio KishinoAbstract:This paper describes a new hand Shape Recognition method for man-machine interfaces using a pipeline image process. The basic strategy is to use simple but effective real time dynamical image processing to enhance the reliability of hand Shape Recognition since motion between frames is minimized. The algorithm for a noncontact-type hand Shape recognizer has been developed for incorporation into a virtual reality environment. >
Jong Shik Kim - One of the best experts on this subject based on the ideXlab platform.
-
Shape Recognition performance analysis and improvement in Sendzimir rolling mills
Journal of Mechanical Science and Technology, 2014Co-Authors: Chul Su Jung, Jung Hyun Park, Seong Ik Han, Jong Shik KimAbstract:Twenty-high Sendzimir rolling mills (ZRMs) typically use small diameter work rolls to provide massive rolling force. Because of the small diameter of the work rolls, a rolled steel strip has a complex Shape mixed with quarter, edge, and center waves. When the strip Shape is controlled automatically, actuator saturation occurs in the Shape actuator such as AS-U roll. These problems affect productivity and the quality of products made from the rolled material. We analyzed problems with the automatic Shape control of ZRMs. The Shape Recognition performance was analyzed by comparing the measured and recognized Shapes by multi-layer perceptron (MLP) method. In addition, neural networks were developed using the radial basis function (RBF) method, and are proposed to improve the Shape Recognition performance of the automatic Shape control system in a ZRM. Through simulation results, we found that Shape Recognition performance can be improved by the proposed method based on RBF neural networks.
-
Shape Recognition performance analysis and improvement in Sendzimir rolling mills
Journal of Mechanical Science and Technology, 2014Co-Authors: Cheol Su Jeong, Jung Hyun Park, Seong Ik Han, Jong Shik KimAbstract:Twenty-high Sendzimir rolling mills (ZRMs) typically use small diameter work rolls to provide massive pass reduction. Because of the small diameter of the work rolls, a rolled steel strip has a complex Shape mixed with quarter, edge, and center waves. When the strip Shape is controlled automatically, actuator saturation occurs in the Shape actuator such as AS-U rack. These problems affect productivity and the quality of products made from the rolled material. We analyzed problems on the Shape control system of a ZRM. The Shape Recognition performance was analyzed by comparing the measured and recognized Shapes by multi-layer perceptron (MLP) method. In addition, neural network using the radial basis function (RBF) method was proposed to improve the Shape Recognition performance of the Shape control system in a ZRM. P-gain which compensates the scale of the strip Shape is added to prevent actuator saturation. Finally, we verify the variation of actuator position using ZRM’s Shape control simulator. Through simulation results, we found that Shape Recognition performance can be improved by the proposed method based on RBF neural network and actuator saturation problem can be improved by increasing Shape Recognition performance.
