图像和视觉计算(Image and Vision Computing)期刊的主要目标是为高质量的理论和应用研究成果提供有效的交流媒介,这些研究成果是图像解释和计算机视觉各个方面的基础。该杂志发表了一些工作,提出了新的图像解释和计算机视觉方法,或者讨论了这些方法在现实世界场景中的应用。它试图通过鼓励对所提议的方法进行定量比较和绩效评估来加强对这一学科的更深层次的理解。覆盖范围包括:图像判读、场景建模、对象识别和跟踪、形状分析、监测和监督,主动视觉和机器人系统,生物计算机视觉、运动分析、立体视觉、文档图像理解和手写文本识别,脸和手势识别,生物识别技术,人机交互,建立人类活动和行为的理解,来自多个传感器的数据融合输入、图像数据库。 官网地址:http://dblp.uni-trier.de/db/journals/ivc/

Let $vc(G)$, $fvs(G)$ and $oct(G)$, respectively, denote the size of a minimum vertex cover, minimum feedback vertex set and minimum odd cycle transversal in a graph $G$. One can ask, when looking for these sets in a graph, how much bigger might they be if we require that they are independent; that is, what is the price of independence? If $G$ has a vertex cover, feedback vertex set or odd cycle transversal that is an independent set, then we let $ivc(G)$, $ifvs(G)$ or $ioct(G)$, respectively, denote the minimum size of such a set. Similar to a recent study on the price of connectivity (Hartinger et al. EuJC 2016), we investigate for which graphs $H$ the values of $ivc(G)$, $ifvs(G)$ and $ioct(G)$ are bounded in terms of $vc(G)$, $fvs(G)$ and $oct(G)$, respectively, when the graph $G$ belongs to the class of $H$-free graphs. We find complete classifications for vertex cover and feedback vertex set and an almost complete classification for odd cycle transversal (subject to three non-equivalent open cases). We also investigate for which graphs $H$ the values of $ivc(G)$, $ifvs(G)$ and $ioct(G)$ are equal to $vc(G)$, $fvs(G)$ and $oct(G)$, respectively, when the graph $G$ belongs to the class of $H$-free graphs. We find a complete classification for vertex cover and almost complete classifications for feedback vertex set (subject to one open case) and odd cycle transversal (subject to three open cases).

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