Automatic trajectory measurement of large numbers of crowded objects
Automatic trajectory measurement of large numbers of crowded objects
复制标题
大量拥挤物体的自动轨迹测量
DOI：
10.1117/1.oe.52.6.067003
复制
发表时间：
2013-06
影响因子：
1.3
通讯作者：
Yan Qiu Chen
中科院分区：
文献类型：
--
作者：
Hui Li;Ye Liu;Yan Qiu Chen
抽象的。多年来，自然系统的复杂运动模式，如鱼群、鸟群和细胞群，引起了科学家的极大关注。个体的轨迹测量对于定量和高通量地研究其集体行为至关重要。然而，这类数据很少，主要是因为检测和跟踪大量视觉特征相似和频繁遮挡的对象的挑战。我们提出了一个自动和有效的框架来测量大量拥挤的椭圆形物体的轨迹，如鱼和细胞。我们首先使用一种新的双椭圆定位器来检测每个个体的粗略位置，然后提出一种方差最小化活动轮廓法来获得最优的分割结果。对于跟踪，连续帧之间的分配代价矩阵可以通过具有许多空间、纹理和形状特征的随机森林分类器来训练。通过求解两个线性分配问题，找到了整个图像序列的最优轨迹。我们在许多具有挑战性的数据集上对所提出的方法进行了评估。
Abstract. Complex motion patterns of natural systems, such as fish schools, bird flocks, and cell groups, have attracted great attention from scientists for years. Trajectory measurement of individuals is vital for quantitative and high-throughput study of their collective behaviors. However, such data are rare mainly due to the challenges of detection and tracking of large numbers of objects with similar visual features and frequent occlusions. We present an automatic and effective framework to measure trajectories of large numbers of crowded oval-shaped objects, such as fish and cells. We first use a novel dual ellipse locator to detect the coarse position of each individual and then propose a variance minimization active contour method to obtain the optimal segmentation results. For tracking, cost matrix of assignment between consecutive frames is trainable via a random forest classifier with many spatial, texture, and shape features. The optimal trajectories are found for the whole image sequence by solving two linear assignment problems. We evaluate the proposed method on many challenging data sets.
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DOI：
10.1007/978-3-540-75759-7_36
发表时间：
2007-10
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
--
作者：
Kang Li;Mei Chen;T. Kanade
通讯作者：
Kang Li;Mei Chen;T. Kanade
影响因子：
10.6
作者：
Chan, TF;Vese, LA
通讯作者：
Vese, LA
DOI：
10.1023/b:visi.0000029664.99615.94
发表时间：
2004-11-01
影响因子：
19.5
作者：
Lowe, DG
通讯作者：
Lowe, DG
影响因子：
1.3
作者：
Li, Xiaohe;Zhang, Taiyi;Sun, Jiancheng
通讯作者：
Sun, Jiancheng
DOI：
10.1007/978-1-4419-9326-7_5
发表时间：
2012-01-01
期刊：
ENSEMBLE MACHINE LEARNING: METHODS AND APPLICATIONS
影响因子：
--
作者：
Cutler, Adele;Cutler, D. Richard;Stevens, John R.
通讯作者：
Stevens, John R.