標題: 分子類神經網路於數位影像處理的應用
Applications of Cellular Neural Networks in digital image processing
作者: 壽宇文
Yu-Wen Shou
林進燈
Chin-Teng Lin
電控工程研究所
關鍵字: 分子類神經網路;基因演算法;文件影像處理;紋理分析;影像去網點;Cellular Neural Network;Genetic Algorithm;Image Documentation;Texture Analysis;Image descreening
公開日期: 2005
摘要: 在這篇論文裡,我們將分子類神經網路(Cellular Neural Networks)應用於複雜且具有代表性地數位影像處理;分子類神經網路一直在學術界有著其特殊且不可取代的地位,其原因主要在於其具備了完整的理論基礎以及在實用時穩定性(stability)和堅固性(robustness)的易於操控,當然最吸引人地莫過於分子類神經網路可硬體實現化的優勢,不過由於硬體實現可能性的考量,分子類神經網路中樣版(template)的設計往往是愈單純愈容易達到硬體實現的目的,但此一設計限制卻和一般數位影像處理演算法的需求大異其逕,也因此使得在過去的文獻裡分子類神經網路只侷限於應用在一些簡單的影像處理技術,為了突破此一瓶頸,我們所提出的這篇論文不但清楚詳細地討論分子類神經網路於高階影像處裡的可能應用演算法更提出實際案例來證明分子類神經網路應用的可能性,所以我們所提出的方法不僅可以解決過去一些高階影像處理的問題,同時也為未來種種數位影像處理於硬體實現的可能提供了一個完整及實際的實現策略。 這篇論文主要可以分為三大部分:在第一部份裡,我們會詳細地說明並討論在過去到現在大部分將分子類神經網路應用於影像處理的相關文獻及未來所有可能的發展和技術,另外也將分子類神經網路作一完整的介紹,除此之外,我們也會特別著重於分子類神經網路在影像處理相關應用理論的討論以及其硬體實現化的考量;在第二部分裡,我們提出了一個將分子類神經網路應用於影像辨識處理的基礎分析—紋路分析(Texture Analysis),這是由於紋路分析的複雜性和普遍性會使得分子類神經網路於高階影像處理的應用不會只侷限在單一的影像處理技術,其中我們也提出了一個相當有用的空間特徵(spatial feature),此一特徵不但可以使複雜地高階影像處理能夠應用分子類神經網路,也為影像辨識技術提供了一個很好的辨識機制;在最後一部分裡,我們也將文件影像分析做了一個完整的剖析,並以文件影像的去網點為例來說明在實際情況下的分子類神經網路的應用,如此演算法的開發也為文件影像處理提供了更多實際的應用,更考量了文件影像處理若以軟體實現時的計算量負荷,而對未來高階數位影像處理能夠以硬體實現來提高處理速度提供了無限的可能。
This dissertation tackles the all-time challenging research field of digital image processing by using Cellular Neural Network as means of its application. As we all know, Cellular Neural Network has been critically acclaimed by the academia for its impeccable theoretical structure and the stability and robustness its applications speak for. Aside from these advantages it presents, Cellular Neural Network appears to be compelling in that it can be practically realized for hardware compilation. However, this does not mean Cellular Neural Network is without limitations. When it comes to hardware compilation of Cellular Neural Network, decent and satisfactory results only come with easy and simple template design, which on the contrary contradicts the algorithmic expectations we have for image processing. This explains why, for years, among all those respectable academic papers and researches, Cellular Neural Network has been applied only for simple, low-level image processing technology. In this dissertation, I will not only dig into the possible algorithms of applications of Cellular Neural Network in higher-level image processing, but use practical case study to justify how these applications may turn out with unexpectedly outstanding performance. In such doing, this dissertation serves as a step stone for papers of its counterparts to come, and, more importantly, it proposes a strategic alternative to the realization of models for image processing. This dissertation consists of three major parts. In the first part, detailed discussions and delicate analyses of academic papers on Cellular Neural Network will be provided in the hope of helping us see the potentiality of Cellular Neural Network in the applications of image processing. I will focus on the aforementioned limitations on hardware compilation as well. In the second part, I will put forth “texture analysis” as one basic model of analysis when we apply Cellular Neural Network to image processing. In this so-called texture analysis, a useful “spatial feature” is especially drawn to help us overcome possible problems of more complicated Cellular Neural Network applications in image processing. “Spatial feature” also serves as a well-functioning mechanism for technology of image identification. In the last part of this thesis, I will look into a case study, where Cellular Neural Network is applied to help de-screen document image. Using it as an example, we will see how algorithms of Cellular Neural Network may be of marvelous use in applications in document image processing, since it would reduce a great deal of calculation and computation when applied to software compilation, yet opens up unlimited possibilities for higher-speed hardware compilation of high-level image processing.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT008912576
http://hdl.handle.net/11536/77068
Appears in Collections:Thesis


Files in This Item:

  1. 257601.pdf