【正文】 得出：基于信息論的迭代算法主要以圖像中目標(biāo)區(qū)域所攜帶的信息量為分割準(zhǔn)則，最大限度的保留圖像中分割目標(biāo)的信息。對下一步的圖像分析打下良好的基礎(chǔ)，滿足要求；但是它對目標(biāo)細(xì)節(jié)處理稍顯不足，尤其是當(dāng)目標(biāo)內(nèi)部色彩分別不大時(shí)容易丟失信息。鑒于此，下一步研究方向就是：將它用于圖像分割預(yù)處理，將總體信息有效保留，結(jié)合其他算法對圖像細(xì)節(jié)進(jìn)行處理，已達(dá)到更好的分割效果。結(jié)論本次論文主要對基于信息熵最大的圖像分割研究，是信息論和圖像處理的一種結(jié)合。 設(shè)計(jì)思路：圖像分割部分采用閾值分割，閾值的設(shè)定對處理后產(chǎn)生的結(jié)果影響非常大，過小的閾值會把不需要的內(nèi)容一起提取出來，過大的閾值則會去掉一些目標(biāo)物體，因此最佳閾值的確定十分重要，迭代算法有對閾值的自動選取功能，本次設(shè)計(jì)我采用迭代法，與信息論的結(jié)合主要體現(xiàn)在循環(huán)判據(jù)上。圖像的目標(biāo)和背景具有的信息量（即圖像達(dá)到理想分割后的區(qū)域）視為最佳信息量，我們需要做的就是尋找能夠?qū)D像分割后，前后景具有的信息量與最佳信息量相等或是接近的閾值。首先利用信息熵求出分割前圖像具有的信息量H0，H0就是達(dá)到理想分割時(shí)圖像所擁有的信息量，在循環(huán)迭代過程中，每次得到的閾值所分割的前后景信息量的和H1與H0比較，滿足誤差范圍或是達(dá)到規(guī)定迭代次數(shù)便停止循環(huán)。 設(shè)計(jì)過程：根據(jù)設(shè)計(jì)題目初步寫出流程圖，由于是首次接觸信息論和圖像處理這方面的知識，在老師的指導(dǎo)下翻閱相關(guān)資料，了解所用知識。從頭做起，這其中自然也走了不少彎路。 在理論基礎(chǔ)準(zhǔn)備完善后，就是將流程圖程序化，調(diào)試程序，作實(shí)驗(yàn)。設(shè)計(jì)總結(jié)：畢業(yè)設(shè)計(jì)是本科學(xué)習(xí)階段一次非常難得的理論與實(shí)際相結(jié)合的機(jī)會，和實(shí)際設(shè)計(jì)的結(jié)合鍛煉了我綜合運(yùn)用所學(xué)的專業(yè)基礎(chǔ)知識，解決實(shí)際工程問題的能力，同時(shí)也提高我查閱文獻(xiàn)資料、設(shè)計(jì)手冊、設(shè)計(jì)規(guī)范以及電腦設(shè)計(jì)等其他專業(yè)能力水平，而且通過對整體的掌控，對局部的取舍，以及對細(xì)節(jié)的斟酌處理，都使我的能力得到了鍛煉，經(jīng)驗(yàn)得到了豐富，提高是有限的但提高也是全面的，正是這一次設(shè)計(jì)讓我積累了無數(shù)實(shí)際經(jīng)驗(yàn)，使我的頭腦更好的被知識武裝了起來，也必然會讓我在未來的工作學(xué)習(xí)中表現(xiàn)出更高的應(yīng)變能力，更強(qiáng)的溝通力和理解力。順利如期的完成本次畢業(yè)設(shè)計(jì)給了我很大的信心，讓我了解專業(yè)知識的同時(shí)也對本專業(yè)的發(fā)展前景充滿信心，但在設(shè)計(jì)的過程中我也發(fā)現(xiàn)了我的許多不足，理論知識的不扎實(shí)，創(chuàng)造性的缺乏使我在設(shè)計(jì)中吃了不少苦頭，發(fā)現(xiàn)問題就要解決問題，正是我們?nèi)ジ玫难芯扛玫膭?chuàng)造的最大動力，今后我會更加關(guān)注本專業(yè)新技術(shù)新設(shè)備新工藝的出現(xiàn)，并爭取盡快的掌握這些先進(jìn)的知識，在今后的工作中做出更好的成績，更好的為祖國的四化服務(wù)。致謝 從接受課題到現(xiàn)在完成畢業(yè)設(shè)計(jì)論文,衷心的感謝我的指導(dǎo)老師王芳老師，她給予了我精心的指導(dǎo)和熱情的幫助,尤其在課題設(shè)計(jì)的前期準(zhǔn)備階段和本人程序的設(shè)計(jì)階段,導(dǎo)師提出許多寶貴的設(shè)計(jì)意見,在最后的修改定稿階段老師在百忙之中抽出時(shí)間為我們提供了必要的幫助,這樣使得我們得以順利的完成畢業(yè)設(shè)計(jì)開發(fā)工作,在短暫的相處時(shí)間里,老師淵博的知識,敏銳的思路和實(shí)事求是的工作作風(fēng)給我留下了深刻的印象,這將使得我終身受益,謹(jǐn)此向老師表示衷心的感謝和崇高的敬意。 同時(shí)也感謝我的同學(xué)朋友在此期間給予我的鼓勵和幫助。英文資料Segmentation (image processing)In puter vision, segmentation refers to the process of partitioning a digital image into multiple segments (sets of pixels) (Also known as superpixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.[1] Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics.The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or puted property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s).[1]Contents1. Clustering methods 2. Histogrambased methods 3. Edge detection methods 4. Region growing methods 5. Graph partitioning methods 6. Model based segmentation 7. Semiautomatic segmentation 8. Neural networks segmentation ApplicationsSome of the practical applications of image segmentation are: Medical Imaging[2] o Locate tumors and other pathologies o Measure tissue volumes o Computerguided surgery o Diagnosis o Treatment planning o Study of anatomical structure Locate objects in satellite images (roads, forests, etc.) Face recognition Fingerprint recognition Traffic control systems Brake light detection Machine vision Several generalpurpose algorithms and techniques have been developed for image segmentation. Since there is no general solution to the image segmentation problem, these techniques often have to be bined with domain knowledge in order to effectively solve an image segmentation problem for a problem domain.Clustering methodsThe Kmeans algorithm is an iterative technique that is used to partition an image into K clusters. The basic algorithm is:1. Pick K cluster centers, either randomly or based on some heuristic 2. Assign each pixel in the image to the cluster that minimizes the variance between the pixel and the cluster center 3. Repute the cluster centers by averaging all of the pixels in the cluster 4. Repeat steps 2 and 3 until convergence is attained (. no pixels change clusters) In this case, variance is the squared or absolute difference between a pixel and a cluster center. The difference is typically based on pixel color, intensity, texture, and location, or a weighted bination of these factors. K can be selected manually, randomly, or by a heuristic.This algorithm is guaranteed to converge, but it may not return the optimal solution. The quality of the solution depends on the initial set of clusters and the value of K.In statistics and machine learning, the kmeans algorithm is clustering algorithm to partition n objects into k clusters, where k n. It is similar to the expectationmaximization algorithm for mixtures of Gaussians in that they both attempt to find the centers of natural clusters in the data. The model requires that the object attributes correspond to elements of a vector space. The objective it tries to achieve is to minimize total intracluster variance, or, the squared error function The kmeans clustering was invented in 1956. The most mon form of the algorithm uses an iterative refinement heuristic known as Lloyd39。s algorithm. Lloyd39。s algorithm starts by partitioning the input points into k initial sets, either at random or using some heuristic data. It then calculates the mean point, or centroid, of each set. It constructs a new partition by associating each point with the closest centroid. Then the centroids are recalculated for the new clusters, and algorithm repeated by alternate application of these two steps until convergence, which is obtained when the points no longer switch clusters (or alternatively centroids are no longer changed). Lloyd39。s algorithm and kmeans are often used synonymously, but in reality Lloyd39。s algorithm is a heuristic for solving the kmeans problem, as with certain binations of starting points and centroids, Lloyd39。s algorithm can in fact converge to the wrong answer 。Other variations exist, but Lloyd39。s algorithm has remained popular because it converges extremely quickly in practice. In terms of performance the algorithm is not guaranteed to return a global op