本文選題：圖像處理 + 圖像分割�。� 參考：《農(nóng)業(yè)工程學(xué)報(bào)》2017年16期

【摘要】：生豬圖像分割為生豬行為特征提取、參數(shù)測量、圖像分析、模式識別等提供易于理解和分析的圖像表示,準(zhǔn)確有效的生豬圖像分割是生豬行為理解和分析的基礎(chǔ)。針對傳統(tǒng)Graph Cut算法分割精度差、分割效率低及不能準(zhǔn)確分割特定目標(biāo)的問題,該文結(jié)合交互分水嶺算法,提出基于改進(jìn)Graph Cut算法的生豬圖像分割方法。采用交互分水嶺算法對圖像進(jìn)行區(qū)域劃分,劃分的各個(gè)區(qū)域塊看作超像素,用超像素替代傳統(tǒng)加權(quán)圖中的像素點(diǎn),構(gòu)造新的網(wǎng)絡(luò)圖替代傳統(tǒng)加權(quán)圖,重新構(gòu)造能量函數(shù)以完成前景背景的有效分割。試驗(yàn)結(jié)果表明:該方法峰值信噪比平均范圍為[30,40],結(jié)構(gòu)相似度平均范圍為[0.9,1],兩種評價(jià)準(zhǔn)則的結(jié)果與主觀評價(jià)一致,圖像分割質(zhì)量、精度得到明顯提升;平均耗時(shí)縮短到傳統(tǒng)Graph Cut算法的33.7%,提高了分割效率;在復(fù)雜背景、噪聲干擾、光照強(qiáng)度弱等條件下可以快速分割出特定目標(biāo)生豬,具有較高魯棒性。
[Abstract]:Pig image segmentation is the basis of pig behavior understanding and analysis, such as extraction of pig behavior characteristics, parameter measurement, image analysis, pattern recognition and so on, which provide easy to understand and analyze the image representation. Accurate and effective pig image segmentation is the basis of pig behavior understanding and analysis. Aiming at the problems of poor segmentation accuracy, low segmentation efficiency and inaccurate segmentation of specific targets in traditional Graph Cut algorithm, this paper proposes an improved Graph Cut algorithm for pig image segmentation based on the interactive watershed algorithm. The interactive watershed algorithm is used to divide the region of the image. Each area block is regarded as super pixel, and the pixel points in the traditional weighted map are replaced by the super pixel, and a new network graph is constructed to replace the traditional weighted map. The energy function is reconstructed to complete the efficient segmentation of foreground background. The experimental results show that the average range of peak signal-to-noise ratio (PSNR) of this method is [30 ~ 40] and the average range of structural similarity is [0.9 ~ 1]. The results of the two evaluation criteria are consistent with subjective evaluation, and the image segmentation quality and accuracy are improved obviously. The average time consuming is shortened to 33.7 of the traditional Graph Cut algorithm, and the segmentation efficiency is improved. Under the conditions of complex background, noise interference and weak illumination intensity, the hogs can be quickly segmented with high robustness.
【作者單位】： 中國農(nóng)業(yè)大學(xué)信息與電氣工程學(xué)院;
【基金】：國家高技術(shù)研究發(fā)展計(jì)劃(863計(jì)劃)資助項(xiàng)目(2013AA102306)
【分類號】：S828;TP391.41

【相似文獻(xiàn)】

相關(guān)博士學(xué)位論文 前1條

1 侯振杰;動物骨髓細(xì)胞圖像分割方法的研究[D];內(nèi)蒙古農(nóng)業(yè)大學(xué);2005年

，

本文編號：1954241