Contributions

We will organize this thesis by giving our contributed parts and the whole constructed structure of our strategic approach in this section. This thesis contributes to applying CNN’s in digital image processing. CNN’s have been only used in the simple and direct operations of image processing techniques. In this thesis, we try to see CNN’s in various realized simulations for image analysis. More clearly, this thesis deals with image processing problems by a separate mechanism which makes use of CNN architecture and original information extracted from image data. At this place, CNN plays an important role in image analysis – feature extraction. CNN is not only distinguished in its hardware implementability but also reveals some information of digital images in some sense by placing different CNN templates. To be more systematically, we shall firstly introduce our structure of CNN and our significant contributions in two aspects.

A. Image analysis

CNN has been used in many areas including digital image processing. Since using CNN is subject to the relationship between the original image files and CNN parameters, the image processing techniques by CNN should be simple and the convolved results from CNN should be reflected directly. This thesis pursues using

CNN to analyze the information of image data. Later in the chapter 3 of this thesis, we demonstrated how to use CNN in some high-level image processing like image documentation. CNN here is a classifier for specific predefined patterns to determine the following strategy after classification where we have referred the CNN structure as one part of the whole software simulations and algorithm development.

Simultaneously, CNN is used to decide the related arguments that could reflect the linking relationship between image data and CNN parameter setting. This thesis thus introduces a complete strategic approach to understand the image natures for image analysis. We then explicitly list the main contributions in this identified category which have been made by our proposed structure as follows.

A.1 Image preprocessing

CNN used to handle some simple image applications, so many studies only focused on the preprocessing steps of digital image processing. In this thesis, we have clearly defined the range of space that CNN could be applied to and the corresponding adjustments and critical points in CNN parameter setting. That is, our applications in image processing by CNN would focus on algorithm development rather than hardware implementation only because the lack of CNN applications makes it hard to use the advantages of CNN structure well.

A.2 Image documentation

We applied CNN to image documentation since this field is a very important one in various researching fields of image processing. Also many problems in image documentation have been challenging and difficult even in software engineering and simulations. Therefore, we prove that CNN could not only be valid for simple operations of image analysis but also useful for more complicated applications. This thesis makes use of CNN structure to discuss the natures of image data for understanding the characteristics of documental images. In this way, CNN is like an

analytical tool for illustrating image data rather than a specific tool for some specific application. This thesis successfully uses CNN in image processing in a different point of view. For the other significant contribution proposed by this thesis, we have the following category.

B. Feature extraction

Unlike the traditional approaches using CNN’s in the literature, this thesis does not focus on finding a specific CNN template for some specific image applications.

CNN is taken as an analytical tool for digital image processing, in which CNN could be regarded as a transformation mapping like FFT, wavelet transform, and so forth in software engineering. The previous researches by CNN put a higher stress on hardware construction than being applied to more complicated applications and understanding the nature of images. This thesis hence tries to work on applying CNN to more complicated image applications and associating CNN templates with the characteristics of image files. Therefore, we have proposed a new approach in feature extraction by using CNN’s and provided a transformation process to project the original image information into another feature space in this thesis. And this mainly focuses on the following subparts.

B.1 A pre-classified mechanism for different screening images

In the past, the performance of image descreening has been restricted only because the same strategy has to be applied to for many kinds of images. We thus in this thesis proposed a very different structure – the pre-classified mechanism for different screening images. This mechanism makes it easier to deal with various screening images and made a great improvement in image descreening performance.

It implies that the CNN structure can also combine with any motivated mechanism from software-oriented status.

B.2 Illustrating CNN outputs in image natures

Our proposed approach in this thesis illustrates the simple CNN outputs in a very different way and also associates the image natures with every adjusted arguments of CNN cell. For image analysis and texture discrimination, the illustrative way of CNN outputs makes the applications by CNN more prevailing and flexible.

B.3 A proliferated structure of CNN templates

The optimization of CNN templates was difficult and insufficient. Thus, we try to propose a proliferated structure to determine CNN templates more adaptively and efficiently. In our proposed structure, CNN templates could be flexibly proliferated for any set of texture patterns. The number of CNN templates that we have to optimize for any prepared texture patterns could be effectively reduced and the optimization process would be shortened in the predefinition of CNN templates.

B.4 A new feature series for texture analysis – Transition Bit String (TBS)

The most significant matters in the difficulty of applying CNN to the higher-level image processing lie in the lack of image information extracted from CNN. Because of this, we proposed the one-dimensional feature curve as well as its feature series in this thesis to provide more chances of using CNN for more applications of image processing. Our proposed feature curves help to understand the natures of textured image patterns and also offer an evaluation index to determine whether the texture patterns that we have to classify in our database are discriminated enough and to decide the number of CNN templates and the corresponding parameters in the optimized templates. As above, we have roughly introduced in this thesis how we could apply CNN in complicated applications of image processing and what kind of relative CNN arguments we have to predefine and determine in advance. To sum up, we have introduced in here the main contributions before getting in the kernel part of this thesis. We did this to make the organization of this thesis more clear and definite.