【正文】 附錄 A：外文文獻 An Effective Automatic Image Enhancement Method ABSTRACT Otsu method is proper to deal with two conditions: (1) two or more classes with distintive grayvalues respectively。 (2) classes without distinctive grayvalues, but with similar areas. However, when the grayvalue differences among classes are not so distinct, and the object is small relative to backgroud, the separabilities among classes are insufficient. In order to overe the above problem, this paper presents an improved spatial lowpass filter with a parameter and presents an unsupervised method of automatic parameter selection for image enhancement based on Otsu method. This method bines image enhancement with image segmentation as one procedure through a discriminant criterion. The optimal parameter of the filter is selected by the discriminant criterion given to maximize the separability between object and background. The optimal threshold for image segmentation is puted simultaneously. The method is used to detect the surface defect of container. Experiments illustrate the validity of the method. KEYWORDS image processing。 automated image enhancement。 image segmentation。 automated visual inspection 1 Introduction Automated visual inspection of cracked container (AVICC) is a practical application of machine vision technology. To realize our goal, four essential operations must be dealt with – image preprocessing, object detection, feature description and final cracked object classification. Image enhancement is to provide a result more suitable than original image for specific applications. In this paper the objective of enhancement, followed by image segmentation, is to obtain an image with a higher content about the object interesting with less content about noise and background. Gonzalez [1] discusses that image enhancement approaches fall into two main categories, in that spatial domain and frequency domain methods. Burton [2] applies image averaging technique to face recognition system, making it able to recognise familiar faces easily across large variations in image quality. Centeno [3] proposes an adaptive image enhancement algorithm, which reverse the processing order of image enhancement and segmentation in order to avoid sharpening noise and blurring borders. Munteanu [4] applies artificial intelligence technology to image enhancement providing denoising function. In addition to spatial domain methods, frequency domain processing techniques are based on modifying the Fourier transform of an image. Bakir [5] discusses image enhancement used for medical image processing in frequency space. Besides, Wang [6] presents a global multiscale analysis of images based on Haar wavelet technique for image denoising. Recently, Agaian [7] proposes image enhancement methods based on the properties of the logarithmic transform domain histogram and histogram equalization. We apply spatial processing here in order to guarantee the realtime and sufficient accuracy property of the system. Segmentation is discussed in [8]. The most simplest, represented by Otsu [9], is method using only the gray level histogram analysis to maximize the separability of the resultant classes. Kuntimad [10] describes a method for segmenting digital images using pulse coupled neural works (PCNN). Salzenstein [11] deals with a parison of recent statistical models on fuzzy Markov random fields and chains for multispectral image segmentation. Due to illdefined, there is no unique segmentation of an image. Evaluation of segmentation algorithms thus far has been largely subjective. Ranjith [12] demonstrates how a recently proposed measureof similarity can be used to perform a quantitative parison among image segmentation algorithms. In this paper, we present