標題: Image descreening by GA-CNN-based texture classification
作者: Shou, YW
Lin, CT
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: cellular neural network (CNN);genetic algorithm (GA);image descreening;texture classification
公開日期: 1-十一月-2004
摘要: This paper proposes a new image-descreening technique based on texture classification using a cellular neural network (CNN) with template trained by genetic algorithm (GA), called GA-CNN. Instead of using the fixed filters for image descreening, we are equipped with a more pliable mechanism for classifications in screening patterns. Using CNN makes it possible to get an accurate texture classification result in a faster speed by its superiority of implementable hardware and the flexible choices of templates. The use of the GA here helps us to look for the most appropriate template for CNNs more adaptively and methodically. The evolved parameters in the template for CNNs can not only provide a quicker classification mechanism but also help us with a better texture classification for screening patterns. After the class of screening patterns in the querying images is determined by the trained GA-CNN-based texture classification. system, the recommendatory filters are induced to solve the screening problems. The induction of the classification in screening patterns has simplified the choice of filters and made it valueless to determine a new structured filter. Eventually, our comprehensive methodology is going to be topped off with more desirable results and the indication for the decrease in time complexity. Index Terms-Cellular neural network (CNN), genetic algorithm (GA), image descreening, texture classification.
URI: http://dx.doi.org/10.1109/TCSI.2004.836861
http://hdl.handle.net/11536/25674
ISSN: 1057-7122
DOI: 10.1109/TCSI.2004.836861
期刊: IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
Volume: 51
Issue: 11
起始頁: 2287
結束頁: 2299
顯示於類別:期刊論文


文件中的檔案:

  1. 000225080800019.pdf