【摘要】：為了提高顯著性算法對(duì)不同類圖像的適用性以及結(jié)果的完整性,該文提出一種基于自適應(yīng)閾值合并的分割過程與新的背景選擇方法相結(jié)合的顯著性檢測(cè)算法。在分割過程中,生成相鄰區(qū)塊的RGB以及LAB共六通道融合的顏色差值序列,采用區(qū)塊面積參數(shù)的反比例模型生成自適應(yīng)閾值與顏色差值序列進(jìn)行對(duì)比合并。在背景選擇過程中,根據(jù)局部區(qū)域背景-主體-背景的相對(duì)位置關(guān)系線索,得到背景區(qū)域,再對(duì)結(jié)果進(jìn)行邊緣優(yōu)化。該算法與其它算法相比得到的顯著圖不需要外接其他閾值算法即生成二值圖,自適應(yīng)閾值合并能排除復(fù)雜環(huán)境中的物體細(xì)節(jié),專注于同等級(jí)大小物體的顯著性對(duì)比。
[Abstract]:In order to improve the applicability of salience algorithm to different kinds of images and the integrity of the results, this paper proposes a salience detection algorithm based on adaptive threshold combining segmentation process and new background selection method. In the process of segmentation, the RGB and LAB color difference sequences of adjacent blocks are generated, and the adaptive threshold and color difference sequences are generated by using the inverse scale model of block area parameters. In the process of background selection, the background region is obtained according to the clues of the relative position relationship between the local region background and the subject background, and then the edge of the result is optimized. Compared with other algorithms, the salient map obtained by this algorithm does not need to be added to other threshold algorithms to generate binary graphs. Adaptive threshold merging can eliminate the details of objects in complex environments and focus on the salience comparison of objects of the same size.
【作者單位】： 河北工業(yè)大學(xué)電子信息工程學(xué)院;河北工業(yè)大學(xué)計(jì)算機(jī)科學(xué)與軟件學(xué)院;
【基金】：天津市科技計(jì)劃項(xiàng)目(14RCGFGX00846,15ZCZDNC 00130) 河北省自然科學(xué)基金面上項(xiàng)目(F2015202239)~~
【分類號(hào)】：TP391.41

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2 ;[J];;年期

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