TIRF microscopy is becoming increasingly popular in cell and molecular biology and opens a challenging computer
vision application domain. However, time-lapse images, acquired by TIRF microscopy imaging system suffer the
problem of intensity loss due to photobleaching. In this paper, TIRF images were segmented by a Gaussian mixture
model into foreground and background. Parameters of the model were estimated through the expectation maximization.
Finally, the restored image was wrapped backward to reference frame with the help of foreground parameters. The
experimental results showed that the corrected images were effectively compensated and maintained a relatively constant
intensity along time.
GLUT4 is responsible for insulin-stimulated glucose uptake into fat cells and description of the dynamic behavior of it
can give insight in some working mechanisms and structures of these cells. Quantitative analysis of the dynamical
process requires tracking of hundreds of GLUT4 vesicles characterized as bright spots in noisy image sequences. In this
paper, a 3D tracking algorithm built in Bayesian probabilistic framework is put forward, combined with the unique
features of the TIRF microscopy. A brightness-correction procedure is firstly applied to ensure that the intensity of a
vesicle is constant along time and is only affected by spatial factors. Then, tracking is formalized as a state estimation
problem and a developed particle filter integrated by a sub-optimizer that steers the particles towards a region with high
likelihood is used. Once each tracked vesicle is located in image plane, the depth information of a granule can be
indirectly inferred according to the exponential relationship between its intensity and its vertical position. The
experimental results indicate that the vesicles are tracked well under different motion styles. More, the algorithm
provides the depth information of the tracked vesicle.
To track the joints of the upper limb of stroke sufferers for rehabilitation assessment, a new tracking algorithm which
utilizes a developed color-based particle filter and a novel strategy for handling occlusions is proposed in this paper.
Objects are represented by their color histogram models and particle filter is introduced to track the objects within a
probability framework. Kalman filter, as a local optimizer, is integrated into the sampling stage of the particle filter that
steers samples to a region with high likelihood and therefore fewer samples is required. A color clustering method and
anatomic constraints are used in dealing with occlusion problem. Compared with the general basic particle filtering
method, the experimental results show that the new algorithm has reduced the number of samples and hence the
computational consumption, and has achieved better abilities of handling complete occlusion over a few frames.