【正文】 由于我們用局部計(jì)算進(jìn)行處理，決定一個(gè)值是否有效的選擇方法就是使用門限。然而非最大抑制的邊緣檢測(cè)，邊緣曲線的定義十分模糊，邊緣像素可能成為邊緣多邊形通過一個(gè)邊緣連接（邊緣跟蹤）的過程。在邊線的每一邊都有一個(gè)邊緣。為了描述數(shù)據(jù)以使感興趣的特征更明顯，還必須確定一種方法。雖然存儲(chǔ)技術(shù)在過去的十年內(nèi)有了很大改進(jìn)，但對(duì)傳輸能力我們還不能這樣說，尤其在互聯(lián)網(wǎng)上更是如此，互聯(lián)網(wǎng)是以大量的圖片內(nèi)容為特征的。通常，圖像獲取包括如設(shè)置比例尺等預(yù)處理。 根據(jù)上述討論，我們看到，圖像處理和圖像分析兩個(gè)領(lǐng)域合乎邏輯的重疊區(qū)域是圖像中特定區(qū)域或物體的識(shí)別這一領(lǐng)域。它們可以對(duì)非人類習(xí)慣的那些圖像源進(jìn)行加工，這些圖像源包括超聲波、電子顯微鏡及計(jì)算機(jī)產(chǎn)生的圖像。 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 zerocrossing based. The searchbased methods detect edges by first puting a measure of edge strength, usually a firstorder derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a puted estimate of the local orientation of the edge, usually the gradient direction. The zerocrossing based methods search for zero crossings in a secondorder derivative expression puted from the image in order to find edges, usually the zerocrossings of the Laplacian or the zerocrossings of a nonlinear differential expression, as will be described in the section on differential edge detection following below. As a preprocessing step to edge detection, a smoothing stage, typically Gaussian smoothing, is almost always applied (see also noise reduction). The edge detection methods that have been published mainly differ in the types of smoothing filters that are applied and the way the measures of edge strength are puted. As many edge detection methods rely on the putation of image gradients, they also differ in the types of filters used for puting gradient estimates in the x and ydirections. Once we have puted a measure of edge strength (typically the gradient magnitude), the next stage is to apply a threshold, to decide whether edges are present or not at an image point. The lower the threshold, the more edges will be detected, and the result will be increasingly susceptible to noise, and also to picking out irrelevant features from the image. Conversely a high threshold may miss subtle edges, or result in fragmented edges. If the edge thresholding is applied to just the gradient magnitude image, the resulting edges will in general be thick and some type of edge thinning postprocessing is necessary. For edges detected with nonmaximum suppression however, the edge curves are thin by definition and the edge pixels can be linked into edge polygon by an edge linking (edge tracking) procedure. On a discrete grid, the nonmaximum suppression stage can be implemented by estimating the gradient direction using firstorder derivatives, then rounding off the gradient direction to multiples of 45 degrees, and finally paring the values of the gradient magnitude in the estimated gradient direction. A monly used approach to handle the problem of appropriate thresholds for thresholding is by using thresholding with hysteresis. This method uses multiple thresholds to find edges. We begin by using the upper threshold to find the start of an edge. Once we have a start point, we then trace the path of the edge through the image pixel by pixel, marking an edge whenever we are above the lower threshold. We stop marking our edge only when the value falls below our lower threshold. This approach makes the assumption that edges are likely to be in continuous curves, and allows us to follow a faint section of an edge we have previously seen, without meaning that every noisy pixel in the image is marked down as an edge. Still, however, we have the problem of choosing appropriate thresholding parameters, and suitable thresholding values may vary over the image. Some edgedetection operators are instead based upon secondorder derivatives of the intensity. This essentially capt