The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 28-679
Full Name: Xiaojuan Li
Position: Associate Professor
Age: ON
Sex: Female
Address: Haidian District Xisanhuan North Rd.56 Pox
Country: CHINA
Tel: 86-13520748126
Tel prefix: 86
Fax:
E-mail address: lixjxxxy@263.net
Other E-mails: chencs.th.btbu.edu.cn
Title of the Paper: An improved BP neural network for wastewater bacteria recognition based on microscopic image analysis
Authors as they appear in the Paper: Xiaojuan Li,Cunshe Chen
Email addresses of all the authors: lixjxxxy@263.net
Number of paper pages: 10
Abstract: The microscopic images of wastewater bacteria are analysed, and a scheme for classification and recognition for wastewater bacteria based on microscopic images analysis are put forward in the paper. An adaptive and enhanced edge detection solution for the images of wastewater bacteria is proposed, which can effectively remove noises in the images and get clear edges of microscopic image by optimizing segmentation threshold and the varied order of edge detection. Seven contour invariant moment features and four morphological features are extracted by analysis of microscopic images of wastewater bacteria in which six features are chosen by PCA in order to reduce the dimensionality of the features extracted from the images. A self-adaptive accelerated BP algorithm is developed for training the classification of bacteria microscopic images. The proposed method is tested with CECC database and the results show that the presented image recognition solution is effective!
and can greatly improve the speed and consistency in performing large-scale surveys or rapid determination of bacterial abundance, morphology
Keywords: BP Neural Network, Edge Detection ,Wastewater Bacteria, Contour Invariant moment,image analysis
EXTENSION of the file: .doc
Special (Invited) Session: A Novel Wastewater Bacteria Recognition Method Based on Microscopic Image Analysis
Organizer of the Session: 607-399
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IP ADDRESS: 61.232.0.138
