计算机工程与设计
計算機工程與設計
계산궤공정여설계
COMPUTER ENGINEERING AND DESIGN
2014年
10期
3563-3567
,共5页
粗糙理论%图像分割%知识粒度%变精度%等价关系
粗糙理論%圖像分割%知識粒度%變精度%等價關繫
조조이론%도상분할%지식립도%변정도%등개관계
rough set theory%image segmentation%knowledge granulation%variable precision%equipollent relations
为有效解决粗糙理论在边界域不能够变化因而无法适应图像信息复杂空间的相关性和不确定性的问题,提出基于变精度分层粒度模型的图像分割算法。以知识粒度为基础,引入分类误差精度,构造出具有不同置信阈值和分类质量的图像粒度结构;根据分割精度要求,确定单元粒度层,在该粒度层分析不同灰度级的重要度,进行相应的灰度核计算;通过差异度指数定义等价关系,实现相似区域合并,完成图像分割。分割实验结果表明,该算法降低了图像信息和时间的复杂度,提高了图像分割的并行性,为知识粒度在图像处理中的应用提供了新思路。
為有效解決粗糙理論在邊界域不能夠變化因而無法適應圖像信息複雜空間的相關性和不確定性的問題,提齣基于變精度分層粒度模型的圖像分割算法。以知識粒度為基礎,引入分類誤差精度,構造齣具有不同置信閾值和分類質量的圖像粒度結構;根據分割精度要求,確定單元粒度層,在該粒度層分析不同灰度級的重要度,進行相應的灰度覈計算;通過差異度指數定義等價關繫,實現相似區域閤併,完成圖像分割。分割實驗結果錶明,該算法降低瞭圖像信息和時間的複雜度,提高瞭圖像分割的併行性,為知識粒度在圖像處理中的應用提供瞭新思路。
위유효해결조조이론재변계역불능구변화인이무법괄응도상신식복잡공간적상관성화불학정성적문제,제출기우변정도분층립도모형적도상분할산법。이지식립도위기출,인입분류오차정도,구조출구유불동치신역치화분류질량적도상립도결구;근거분할정도요구,학정단원립도층,재해립도층분석불동회도급적중요도,진행상응적회도핵계산;통과차이도지수정의등개관계,실현상사구역합병,완성도상분할。분할실험결과표명,해산법강저료도상신식화시간적복잡도,제고료도상분할적병행성,위지식립도재도상처리중적응용제공료신사로。
To solve the problem that the rough theory in the edge boundaries is not able to adapt the correlation of image infor-mation complex space and the defect of uncertainty ,a new image segmentation algorithm was proposed based on the variable pre-cision hierarchy granular model .Firstly ,the classifying error precision was introduced into the knowledge granulation ,and the granular structure of the image with various levels of confidence and the classification quality was constructed .Next ,on the basis of the requirement of the segmentation precision ,the unit granular layer was chosen and the importance of different grey levels on the layer was analyzed further .Finally ,the equivalent relations defined by using the dissimilarities were used to implement the combination of the similar regions and the image segmentation was accomplished .The algorithm was applied to image seg-mentation tests .The experimental results indicate that it not only improves the parallel computation of the image and reduces the complexity of the space and time ,but also provides new thoughts on applying the knowledge granulation to the image process .