【正文】 ing 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 captures the rate of change in the intensity gradient. Thus, in the ideal continuous case, detection of zerocrossings in the second derivative captures local maxima in the gradient. We can e to a conclusion that,to be classified as a meaningful edge point,the transition in gray level associated with that point has to be significantly stronger than the background at that we are dealing with local putations,the method of choice to determine whether a value is “significant” or not id to use a we define a point in an image as being as being an edge point if its twodimensional firstorder derivative is greater than a specified criterion of connectedness is by definition an term edge segment generally is used if the edge is short in relation to the dimensions of the key problem in segmentation is to assemble edge segments into longer alternate definition if we elect to use the secondderivative is simply to define the edge ponits in an image as the zero crossings of its second definition of an edge in this case is the same as is important to note that these definitions do not guarantee success in finding edge in an simply give us a formalism to look for derivatives in an image are puted using the derivatives are obtained using the Laplacian. 數(shù)字圖像處理與邊緣檢測(cè) 數(shù)字圖像處理 數(shù)字圖像處理方法的研究源于兩個(gè)主要應(yīng)用領(lǐng)域：其一是為了便于人們分析而對(duì)圖像信息進(jìn)行改進(jìn)：其二是為使機(jī)器自動(dòng)理解而對(duì)圖像數(shù)據(jù)進(jìn)行存儲(chǔ)、傳輸及顯示。 blur caused by shadows created by light sources of nonzero radius。 Digital Image Processing and Edge Detection Digital Image Processing Interest in digital image processing methods stems from two principal applica tion areas: improvement of pictorial information for human interpretation。 and processing of image data for storage, transmission, and representation for au tonomous machine perception. An image may be defined as a twodimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital puter. Note that a digital image is posed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pels, and pixels. Pixel is the term most widely used to de