Wildfire smoke detection based on local extremal region segmentation and surveillance

A novel video-based method is proposed for long-distance wildfire smoke detection. Since the long-distance wildfire smoke usually moves slowly and lacks salient features in the video, the detection is still a challenging problem. Unlike many traditional video-based methods that usually rely on the smoke color or motion for initial smoke region segmentation, we use the Maximally Stable Extremal Region (MSER) detection method to extract local extremal regions of the smoke. This makes the initial segmentation of possible smoke region less dependent on the motion and color information. Potential smoke regions are then selected from all the possible regions by using some static visual features of the smoke, helping to eliminate the non-smoke regions as many as possible. Once a potential smoke region is found, we keep tracking it by searching the best-matched extremal regions in the subsequent frames. At the same time, the propagating motions of the potential smoke region are monitored based on a novel cumulated region approach, which can be effectively used to identify the distinctive expanding and rising motions of smoke. This approach can also make the final smoke motion identification insensitive to image shaking. It was proved that the proposed method is able to reliably detect the long-distance wildfire smoke and simultaneously produce very few false alarms in actual applications.     

基于MSER的红外与可见光图像关联特征提取算法

关联特征提取是红外与可见光图像配准、融合和变化检测等应用中的关键步骤。针对同一场景红外与可见光图像间关联特征难以正确提取的问题,基于最稳定极值区域(MSER)算法,提出了一个仿射不变的关联特征提取方法。该方法主要包括3个步骤:(1)提取红外与可见光图像中的最稳定极值区域;(2)对特征区域进行椭圆拟合;(3)规则化处理,消除形变干扰,输出便于描述和匹配的一致性特征。实验结果证明了该算法在红外与可见光图像关联特征提取中的有效性。     

Automatic detection and analysis of cell motility in phase‐contrast time‐lapse images using a combination of maximally stable extremal regions and Kalman filter approaches

Phase‐contrast illumination is simple and most commonly used microscopic method to observe nonstained living cells. Automatic cell segmentation and motion analysis provide tools to analyze single cell motility in large cell populations. However, the challenge is to find a sophisticated method that is sufficiently accurate to generate reliable results, robust to function under the wide range of illumination conditions encountered in phase‐contrast microscopy, and also computationally light for efficient analysis of large number of cells and image frames. To develop better automatic tools for analysis of low magnification phase‐contrast images in time‐lapse cell migration movies, we investigated the performance of cell segmentation method that is based on the intrinsic properties of maximally stable extremal regions (MSER). MSER was found to be reliable and effective in a wide range of experimental conditions. When compared to the commonly used segmentation approaches, MSER required negligible preoptimization steps thus dramatically reducing the computation time. To analyze cell migration characteristics in time‐lapse movies, the MSER‐based automatic cell detection was accompanied by a Kalman filter multiobject tracker that efficiently tracked individual cells even in confluent cell populations. This allowed quantitative cell motion analysis resulting in accurate measurements of the migration magnitude and direction of individual cells, as well as characteristics of collective migration of cell groups. Our results demonstrate that MSER accompanied by temporal data association is a powerful tool for accurate and reliable analysis of the dynamic behaviour of cells in phase‐contrast image sequences. These techniques tolerate varying and nonoptimal imaging conditions and due to their relatively light computational requirements they should help to resolve problems in computationally demanding and often time‐consuming large‐scale dynamical analysis of cultured cells.     

基于改进MSER算法的电力设备红外故障区域提取方法

红外图像处理中因目标边界模糊、区域灰度变化等因素,导致传统的极大稳态区域方法区域提取效果低下。为此,提出一种基于改进极大稳态区域方法的电力设备红外故障区域提取机制,提升区域提取效果。首先,从灰度相似度聚类出发,采用Meanshift算法对分割区域的邻域像素进行聚类。其次,结合阈值分割机制,快速将相似像素进行分割,最终通过迭代得到电力设备故障所呈现的亮度区域信息。实验结果表明该提取区域方法性能优于极大稳态区域算法,具有较低的误分类错误,且相比于Mean shift算法,具有高效的处理速度。     

一种基于稳定区域的图像特征描述子

当不同成像条件下图像局部内容的特征存在明显差异时,会导致特征描述子难以正确匹配。为此,提出一种基于稳定区域的图像特征描述子。利用最大稳定极值区域检测算法提取图像的稳定区域,结合图像特征点的位置和尺度信息,根据稳定区域计算特征主方向,在适应于特征点所属稳定区域尺度的较大邻域内,生成基于对数-极坐标系的特征描述子。应用结果表明,该描述子在图像局部内容变化较大时仍具有旋转不变性和稳定的匹配性能,能有效解决印刷电路板CT图像的配准问题。     

基于MSER与SVM算法的车牌定位识别方法

针对实际车牌识别系统中车牌位置定位难、字符识别率低等问题,提出了一种基于MSER与SVM算法的车牌定位识别。该方法分为定位和识别两步,输入图像经过预处理,通过MSER与SVM算法直接提取出车牌的字符区域,然后将车牌字符图像裁剪送入识别阶段,识别阶段同样利用SVM算法对车牌字符进行识别。经验证,该车牌定位识别方法识别速度快、准确率高,能够适用于实际生活中较为复杂的交通环境。     

基于树修剪和多特征融合的场景文本检测

为了解决最大稳定极值区（MSER）提取过程中产生的大量重复文本区域和非文本区域难以被剔除影响算法精度的问题,提出了一种基于树修剪和多特征融合的场景文本检测方法。首先提取出边缘叠加的MSER作为文本候选区域;其次设计了一种MSER树修剪算法剔除重复文本区域;然后采用贝叶斯分类器融合多特征剔除非文本区域;最后设定了一系列相似性标准合并文本区域。ICDAR 2011数据集（f=76.8%）上的实验结果低于目前最好的算法\[19\],但算法在速度上具有明显的优势。     

基于对象建议算法的自然场景文本检测

对象建议算法(object proposals)是对象检测中的常用算法,用于快速定位物体区域。根据自然场景文本的特点,将对象建议算法应用到文本检测中,并与经典的最稳定极值区域算法相结合;然后,通过贝叶斯模型融合了笔画宽度特征、视觉散度特征和边缘梯度特征,并将文本和非文本区域的区分问题转换成一个二值标记问题,通过最小化能量函数寻找最佳标记;最后,通过均值漂移聚类寻找文本区域的中心生成文本行。经实验证明,本算法在常用的自然场景文本检测数据集上速度得到了提高,并且一定程度上解决了传统最稳定极值区域算法对光照敏感的问题,获得了较高的查全率。     

多摄像机间基于最稳定极值区域的人体跟踪方法

为实现多摄像机视频监控系统中的人体跟踪,提出了一种以最稳定极值区域作为匹配特征的人体跟踪方法.该方法使用图像的灰度信息,在一定程度上减小了摄像机增益及光谱特性对人体跟踪造成的影响.通过把人体跟踪转化为椭圆形区域的匹配,算法将最稳定极值区域拟合成椭圆形区域,在此基础上,选出符合约束条件的候选椭圆形区域,并将其归一化为单位圆形区域,通过在圆形区域内计算旋转不变量、进行直方图密度估计和计算加权平均距离实现椭圆形区域的正确匹配,从而实现多摄像机间的人体跟踪.实验结果表明:该算法可有效实现多摄像机间的人体跟踪.