Basing on a lot of examinations, according to the fundamental inage processing theories and methods, getting touch with the property of wood anatomical structure image,we put forward the optimum method and theory whic...Basing on a lot of examinations, according to the fundamental inage processing theories and methods, getting touch with the property of wood anatomical structure image,we put forward the optimum method and theory which are suitable for the binary processing of the wood anatomical structure image. After the wood image has been processed binary, with the help of computer vision technology, the boundary of wood anatomical structure molecular binary image was sought This kind of theory and method lay a solid foundaion on the collection of feature and the pottern recognition and other high level processing of wood anatomical structure molecular image.展开更多
Nowadays,wood identification is made by experts using hand lenses,wood atlases,and field manuals which take a lot of cost and time for the training process.The quantity and species must be strictly set up,and accurate...Nowadays,wood identification is made by experts using hand lenses,wood atlases,and field manuals which take a lot of cost and time for the training process.The quantity and species must be strictly set up,and accurate identification of the wood species must be made during exploitation to monitor trade and enforce regulations to stop illegal logging.With the development of science,wood identification should be supported with technology to enhance the perception of fairness of trade.An automatic wood identification system and a dataset of 50 commercial wood species from Asia are established,namely,wood anatomical images collected and used to train for the proposed model.In the convolutional neural network(CNN),the last layers are usually soft-max functions with dense layers.These layers contain the most parameters that affect the speed model.To reduce the number of parameters in the last layers of the CNN model and enhance the accuracy,the structure of the model should be optimized and developed.Therefore,a hybrid of convolutional neural network and random forest model(CNN-RF model)is introduced to wood identification.The accuracy’s hybrid model is more than 98%,and the processing speed is 3 times higher than the CNN model.The highest accuracy is 1.00 in some species,and the lowest is 0.92.These results show the excellent adaptability of the hybrid model in wood identification based on anatomical images.It also facilitates further investigations of wood cells and has implications for wood science.展开更多
文摘Basing on a lot of examinations, according to the fundamental inage processing theories and methods, getting touch with the property of wood anatomical structure image,we put forward the optimum method and theory which are suitable for the binary processing of the wood anatomical structure image. After the wood image has been processed binary, with the help of computer vision technology, the boundary of wood anatomical structure molecular binary image was sought This kind of theory and method lay a solid foundaion on the collection of feature and the pottern recognition and other high level processing of wood anatomical structure molecular image.
文摘Nowadays,wood identification is made by experts using hand lenses,wood atlases,and field manuals which take a lot of cost and time for the training process.The quantity and species must be strictly set up,and accurate identification of the wood species must be made during exploitation to monitor trade and enforce regulations to stop illegal logging.With the development of science,wood identification should be supported with technology to enhance the perception of fairness of trade.An automatic wood identification system and a dataset of 50 commercial wood species from Asia are established,namely,wood anatomical images collected and used to train for the proposed model.In the convolutional neural network(CNN),the last layers are usually soft-max functions with dense layers.These layers contain the most parameters that affect the speed model.To reduce the number of parameters in the last layers of the CNN model and enhance the accuracy,the structure of the model should be optimized and developed.Therefore,a hybrid of convolutional neural network and random forest model(CNN-RF model)is introduced to wood identification.The accuracy’s hybrid model is more than 98%,and the processing speed is 3 times higher than the CNN model.The highest accuracy is 1.00 in some species,and the lowest is 0.92.These results show the excellent adaptability of the hybrid model in wood identification based on anatomical images.It also facilitates further investigations of wood cells and has implications for wood science.
