外文文獻翻譯數(shù)字圖像增強數(shù)字圖像處理與邊緣檢測中英文對照(編輯修改稿)

　

【文章內(nèi)容簡介】 more likely recognition is to succeed. Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the bound ary of a region (., the set of pixels separating one image region from another) or all the points in the region itself. In either case, converting the data to a form suitable for puter processing is necessary. The first decision that must be made is whether the data should be represented as a boundary or as a plete region. Boundary representation is appropriate when the focus is on external shape characteristics, such as corners and inflections. Regional representation is appropriate when the focus is on internal properties, such as texture or skeletal shape. In some applications, these representations plement each other. Choosing a representation is only part of the solution for trans forming raw data into a form suitable for subsequent puter processing. A method must also be specified for describing the data so that features of interest are highlighted. Description, also called feature selection, deals with extracting attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another. Recognition is the process that assigns a label (., “vehicle”) to an object based on its descriptors. As detailed before, we conclude our coverage of digital image processing with the development of methods for recognition of individual objects. So far we have said nothing about the need for prior knowledge or about the interaction between the knowledge base and the processing modules in Fig2 above. Knowledge about a problem domain is coded into an image processing system in the form of a knowledge database. This knowledge may be as sim ple as detailing regions of an image where the information of interest is known to be located, thus limiting the search that has to be conducted in seeking that information. The knowledge base also can be quite plex, such as an interrelated list of all major possible defects in a materials inspection problem or an image database containing highresolution satellite images of a region in con nection with changedetection applications. In addition to guiding the operation of each processing module, the knowledge base also controls the interaction between modules. This distinction is made in Fig2 above by the use of doubleheaded arrows between the processing modules and the knowledge base, as op posed to singleheaded arrows linking the processing modules. Edge detection Edge detection is a terminology in image processing and puter vision, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sharply or more formally has point and line detection certainly are important in any discussion on segmentation,edge dectection is by far the most mon approach for detecting meaningful discounties in gray level. Although certain literature has considered the detection of ideal step edges, the edges obtained from natural images are usually not at all ideal step edges. Instead they are normally affected by one or several of the following effects: blur caused by a finite depthoffield and finite point spread function。 blur caused by shadows created by light sources of nonzero radius。 at a smooth object edge。 specularities or interreflections in the vicinity of object edges. A typical edge might for instance be the border between a block of red color and a block of yellow. In contrast a line (as can be extracted by a ridge detector) can be a small number of pixels of a different color on an otherwise unchanging background. For a line, there ma y therefore usually be one edge on each side of the line. To illustrate why edge detection is not a trivial task, let us consider the problem of detecting edges in the following onedimensional signal. Here, we may intuitively say that there should be an edge between the 4th and 5th pixels. 5 7 6 4 152 148 149 If the intensity difference were smaller between the 4th and the 5th pixels and if the intensity differences between the adjacent neighbouring pixels were higher, it would not be as easy to say that there should be an edge in the corresponding region. Moreover, one could argue that this case is one in which there are several , to firmly state a specific threshold on how large the intensity change between two neighbouring pixels must be for us to say that there should be an edge between these pixels is not always a simple problem. Indeed, this is one of the reasons why edge detection may be a nontrivial problem unless the objects in the scene are particularly simple and the illumination conditions can be well controlled. There are many methods for edge detection, but most of them can be grouped into two categories,searchbased and zerocross