外文翻譯---圖像的邊緣檢測(編輯修改稿)
2025-02-13 23:24 本頁面
【文章內(nèi)容簡介】 hick characteristic is extremely useful in the pattern recognition, we may be called this thick outline the image the main will be able an image main edge clear integrity extraction, this to the goal division, the recognition and so on following processing to bring the enormous speaking, the above method all is the work which does based on the image luminance information. In the multitudinous scientific research worker under, has obtained the very good effect , because the image edge receives physical condition and so on the illumination influences quite to be big above, often enables many to have a mon shorting based on brightness edge detection method, that is the edge is not continual, does not seal the phase information in the image importance as well as its stable characteristic, causes using the phase information to carry on the imagery processing into new research topic. In this paper soon introduces one kind based on the phase image characteristic examination method phase uniform is not uses the image the luminance information, but is its phase characteristic, namely supposition image Fourier ponent phase most consistent spot achievement characteristic only it can examine brightness characteristics and so on step characteristic, line characteristic, moreover can examine Mach belt phenomenon which produces as a result of the human vision sensation the phase uniformity does not need to carry on any supposition to the image characteristic type, therefore it has the very strong versatility.第一章 緒論167。 圖像邊緣檢測概論圖像邊緣是圖像最基本的特征之一, 往往攜帶著一幅圖像的大部分信息. 而邊緣存在于圖像的 不規(guī)則結(jié)構(gòu)和不平穩(wěn)現(xiàn)象中,也即存在于信號的突變點處,這些點給出了圖像輪廓的位置,這些輪 廓常常是我們在圖像處理時所需要的非常重要的一些特征條件, 這就需要我們對一幅圖像檢測并提 取出它的邊緣. 而邊緣檢測算法則是圖像處理問題中經(jīng)典技術難題之一, 它的解決對于我們進行高 層次的特征描述, 識別和理解等有著重大的影響。 又由于邊緣檢測在許多方面都有著非常重要的使 用價值, 所以人們一直在致力于研究和解決如何構(gòu)造出具有良好性質(zhì)及好的效果的邊緣檢測算子的 ,我們可以將信號中 的奇異點和突變點認為是圖像中的邊緣點,其附近灰度的 變化情況可從它相鄰像素灰度分布的梯度來反映.根據(jù)這一特點,我們提出了多種邊緣檢測算子:如 Robert 算子,Sobel 算子,Prewitt 算子, Laplace ,實現(xiàn)對圖像 邊緣的提取并已經(jīng)取得了較好的處理效果. 但這類方法同時也存在有邊緣像素寬, 噪聲干擾較嚴重 等缺點,即使采用一些輔助的方法加以去噪, 波分析的出現(xiàn), 其良好的時頻局部特性被廣泛的應用在圖像處理和模式識別領域中, 成為信號處理 中常用的手段和有力的工具. 通過小波分析, 可以將交織在一起的各種混合信號分解成不同頻率的 塊信號,而通過小波變換進行邊緣檢測,可以充分利用其多尺度和多分辨率的性質(zhì),真實有效的表 ,對圖像的細節(jié)更加敏感。而
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