本文選題：邊緣檢測(cè) + 二維最大類(lèi)間方差算法　； 參考：《青島理工大學(xué)》2016年碩士論文

【摘要】：圖像邊緣檢測(cè)是圖像分析和理解的基礎(chǔ),是圖像特征提取與目標(biāo)識(shí)別的重要前提,其結(jié)果的完整性直接影響到計(jì)算機(jī)對(duì)目標(biāo)圖像的理解和認(rèn)知,因此圖像邊緣檢測(cè)在計(jì)算機(jī)視覺(jué)及圖像處理領(lǐng)域中具有至關(guān)重要的作用。通過(guò)研究一階、二階的微分算子和Canny算子,重點(diǎn)分析了它們對(duì)懸浮微粒的邊緣提取效果,依據(jù)懸浮微粒的水下成像特點(diǎn),提出以下三個(gè)方面的創(chuàng)新。(1)針對(duì)在復(fù)雜背景下Sobel算子提取懸浮微粒邊緣時(shí)存在邊緣粗糙和閾值精確度低的問(wèn)題,提出了一種基于閾值分割與梯度算子相結(jié)合的圖像邊緣檢測(cè)算法,該算法采用牛頓迭代算法實(shí)現(xiàn)快速收斂至最佳閾值。實(shí)驗(yàn)表明,該算法對(duì)懸浮微粒邊緣的檢測(cè)精度較高,且邊緣線清晰準(zhǔn)確。(2)由于水下照明導(dǎo)致懸浮微粒的成像過(guò)程中存在水平條紋噪聲,針對(duì)傳統(tǒng)梯度算子敏感性高、易檢測(cè)出噪聲的問(wèn)題,提出了一種基于圖像邊緣增強(qiáng)的二維最大類(lèi)間方差(2D-Otsu)邊緣檢測(cè)算法,該算法采用梯度銳化算子消除水平條紋噪聲。實(shí)驗(yàn)結(jié)果表明,該算法可在一定程度上濾除水平條紋并檢測(cè)出懸浮微粒的邊緣特征,對(duì)水平條紋噪聲具有較強(qiáng)的魯棒性。(3)針對(duì)傳統(tǒng)Canny算子需人工設(shè)定閾值范圍、自適應(yīng)能力較弱的問(wèn)題,提出了一種基于二維Otsu自適應(yīng)閾值的Canny邊緣檢測(cè)算法。實(shí)驗(yàn)結(jié)果表明,該算法解決了噪聲濾除問(wèn)題和邊緣有效信息保留之間的矛盾,具有較強(qiáng)的魯棒性和適應(yīng)性。本文提出的邊緣檢測(cè)算法解決了傳統(tǒng)的梯度算子提取懸浮微粒邊緣時(shí)的部分問(wèn)題,比傳統(tǒng)算法具有檢測(cè)精度高、定位準(zhǔn)確度高、適應(yīng)性和魯棒性強(qiáng)的優(yōu)點(diǎn)。
[Abstract]:Image edge detection is the basis of image analysis and understanding and an important premise of image feature extraction and target recognition. The integrity of the result directly affects the understanding and cognition of the target image by computer. Therefore, image edge detection plays an important role in computer vision and image processing. By studying the first and second order differential operators and Canny operators, the effect of edge extraction on suspension particles is analyzed, according to the underwater imaging characteristics of suspended particles. In view of the problem of rough edges and low threshold accuracy, the Sobel operator can extract the edge of suspended particles in complex background. An image edge detection algorithm based on the combination of threshold segmentation and gradient operator is proposed. Newton iterative algorithm is used to rapidly converge to the optimal threshold. Experimental results show that the proposed algorithm has a high accuracy for the edge detection of suspended particles, and the edge lines are clear and accurate. (2) because of the horizontal fringe noise in the imaging process of suspended particles caused by underwater illumination, the traditional gradient operator is highly sensitive. The problem of noise detection is easy to be detected. A 2D maximum inter-class variance 2D-Otsu-based edge detection algorithm based on image edge enhancement is proposed. Gradient sharpening operator is used to eliminate horizontal fringe noise. The experimental results show that the algorithm can filter out horizontal stripes to some extent and detect the edge features of suspended particles. The algorithm is robust to horizontal fringe noise. For the problem of weak adaptive ability, a Canny edge detection algorithm based on 2-D Otsu adaptive threshold is proposed. The experimental results show that the algorithm solves the contradiction between the noise filtering problem and the edge effective information retention, and has strong robustness and adaptability. The edge detection algorithm proposed in this paper solves part of the problem when the traditional gradient operator is used to extract the edge of suspended particles, which has the advantages of high detection accuracy, high localization accuracy, adaptability and robustness.
【學(xué)位授予單位】：青島理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.41
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本文編號(hào)：1878755