數(shù)字圖像處理：第七章 圖像分割（Image Segmentation）

1、第七章圖 像 分 割（Image Segmentation）圖象采集圖象預(yù)處理圖象分割特征提取與描述特征選擇模式識(shí)別 特征參數(shù)測(cè)量一并行邊界技術(shù)二串行邊界技術(shù)三并行區(qū)域技術(shù)四串行區(qū)域技術(shù)五分割性能評(píng)價(jià)圖象分割：把圖象分成各具特性的區(qū)域，并提取感興趣目標(biāo)的技術(shù)和過程這里的特性主要指的是：灰度，顏色，紋理或運(yùn)動(dòng)等其一是通過直接確定區(qū)域間的邊界來實(shí)現(xiàn)分割的邊界方法；其二是將各像素劃歸到相應(yīng)物體或區(qū)域的像素聚類方法, 即區(qū)域法；圖像中不同類型的邊界（a） 邊界； （b） 線； （c） 折線變化； （d） 緩慢的平滑變化 (a)(b)(d)(c)First and Second derivativest

2、he signs of the derivatives would be reversed for an edge that transitions from light to darkEdge Detectionthe most common approach for detecting meaningful discontinuities in gray level.we discuss approaches for implementingfirst-order derivative (Gradient operator)second-order derivative (Laplacia

3、n operator)Here, we will talk only about their properties for edge detection.Image SegmentationImage SegmentationLaplacian(linear operator)Laplacian operatorImage SegmentationImage SegmentationGradient Operatorfirst derivatives are implemented using the magnitude of the gradient.the magnitude become

4、s nonlinearcommonly approx.Gradient directionLet (x,y) represent the direction angle of the vector f at (x,y) (x,y) = tan-1(Gy/Gx)The direction of an edge at (x,y) is perpendicular to the direction of the gradient vector at that pointCriteriathe strength of the response of the gradient operator used

5、 to produce the edge pixelan edge pixel with coordinates (x0,y0) in a predefined neighborhood of (x,y) is similar in magnitude to the pixel at (x,y) if|f(x,y) - f (x0,y0) | ECriteriathe direction of the gradient vectoran edge pixel with coordinates (x0,y0) in a predefined neighborhood of (x,y) is si

6、milar in angle to the pixel at (x,y) if|(x,y) - (x0,y0) | 25gradient direction differs T and G2 consisting of pixels with gray level values TCompute the average gray level values 1 and 2 for the pixels in regions G1 and G2Compute a new threshold valueT = 0.5 (1 + 2)Repeat steps 2 through 4 until the

7、 difference in T in successive iterations is smaller than a predefined parameter To.Example: Heuristic methodnote: the clear valley of the histogram and the effective of the segmentation between object and backgroundT0 = 03 iterations with result T = 125Example : Adaptive ThresholdingOptimal Global

8、and Adaptive ThresholdingProbability of erroneously Minimum error Differentiating E(T) with respect to T (using Leibnizs rule) and equating the result to 0find T which makesif P1 = P2 thenthe optimum thresholdis where the curvep1(z) and p2(z) intersectGaussian density Example: use PDF = Gaussian den

9、sity : p1(z) and p2(z)where 1 and 12 are the mean and variance of the Gaussian density of one object 2 and 22 are the mean and variance of the Gaussian density of the other objectQuadratic equation if P1 = P2 or = 0 then the optimal threshold is the average of the meansRegion-Based SegmentationBasic

10、 FormulationP(Ri) is a logical predicate property defined over the points in set Riex. P(Ri) = TRUE if all pixel in Ri have the same gray levelRegion Growingstart with a set of “seed” pointsgrowing by appending to each seed those neighbors that have similar properties such as specific ranges of gray

11、 levelRegion Growingselect all seed points with gray level 255criteria:the absolute gray-level difference between any pixel and the seed has to be less than 65the pixel has to be 8-connected to at least one pixel in that region (if more, the regions are merged)Region splitting and mergingQuadtreeSpl

12、it into 4 disjoint quadrants any region Ri for whichP(Ri) = FALSEMerge any adjacent region Rj and Rk for which P(Ri Rk ) = TRUEStop when no further merging or splitting is possible.ExampleP(Ri) = TRUE if at least 80% of the pixels in Ri have the property |zj-mi| 2i, where zj is the gray level of the

13、 jth pixel in Rimi is the mean gray level of that regioni is the standard deviation of the gray levels in RiGlobal Processing via Graph-Theoretic Techniquessearching graph for low-cost paths that correspond to significant edgesperforms well in the presence of noise.time consuming process.Basic DefinitionGraph G = (N,U) is a finite, nonem