Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Biological videos are very different from conventional videos. Automatic spatiotemporal mining of moving cells from in vivo microscopy videos is extremely difficult because of the severe noises, camera/subject movements, deformations, and strong dependencies on microscopy operators. In this paper, we present an automatic spatiotemporal mining system of rolling and adherent leukocytes for intravital videos. The magnitude of leukocyte adhesion and decrease in rolling velocity are common interests in inflammation response studies. Currently, there is no existing system which is perfect for such purposes. Several approaches have been proposed for tracking leukocytes. However, these approaches can either only track leukocytes that roll along the centerline of the blood vessel, or can only handle leukocytes with fixed morphologies. In addition, the camera/subject movement is a severe problem which occurs frequently while analyzing in vivo microscopy videos. In this paper, we proposed a new method for automatic recognition of non-adherent and adherent leukocytes. The proposed method includes three steps: (1) camera/subject movement alignment; (2) moving leukocytes detection; (3) adherent leukocytes detection. The experimental results demonstrate the effectiveness of the proposed method.