中文摘要：

图像阈值化是一种直观有效的图像分割技术,在图像分析、模式识别及计算机视觉中具有重要应用.传统的阈值化方法通常基于某个特定的优化问题,需要在整个灰度范围内搜索最佳阈值（或阈值组合）.最近,基于支持向量回归（SVR）的多阈值分割算法,直接从支持向量（SV）中获得阈值信息,无需对图像施加任何先验假设,并避免了繁琐的优化过程.然而：1.如何从众多SV中获得可靠的阈值尚待解决（SVR阈值方法的公开问题）;2.虽然SVR阈值技术避免了传统多阈值算法可能出现的组合优化问题,但是其中超参数的选择往往需要耗时的交叉验证;3.算法在单峰直方图情形下失效.针对这些问题,并受相关向量机（RVM）方法的启发,提出了一种新的基于RVM的多阈值自动选择技术.由于RVM可以极大地约减＂SV＂数目,并且无需交叉验证进行参数调整,使得最终阈值的确定更加高效、可靠且异常容易;另外所提算法能有效地处理单峰直方图情形,使阈值分割具有更强的适应性.实验表明基于RVM的阈值技术不仅保留了SVR阈值技术的优点,而且解决了其中的公开问题,并显著地提高了算法的效率和适应能力.

英文摘要：

Image thresholding is a simple but effective segmentation approach which is widely applied in image analysis,pattern recognition and computer vision.To obtain the optimal threshold value or more threshold values,classical thresholding methods have to search the whole gray levels of a given image,based on specific optimization strategy.Recently,a novel multi-threshold technique based on support vector regression（SVR） is developed,where threshold values can be determined directly from support vectors（SV）.However：1.There are no reliable methods to select threshold values from abundant SVs;2.The computational cost is generally high due to the cross-validation procedure to estimate the hyper-parameters incurred in SVR;3.It does not well deal with the images with unimodal histogram.Motivated by relevance vector machine（RVM）,a new method called RVM-based threholding algorithm is introduced.Under the proposed framework,the relevance vectors（RVs） are very sparse and thereby the thresholds can be easily found from a few candidate RVs.In addition,the unimodal case can also be handled well.That is,the proposed algorithm not only keeps the advantages of SVR-based thresholding method,but also solves its open problems.Experimental results validate the effectiveness,efficiency and adaptability.