This paper presents a novel approach to automatically detect the end-diastolic (ED) and end-systolic (ES) frames from
an X-ray left ventricular angiographical image sequence. ED and ES image detection is the first step for widely used
left ventricular analysis in catheterization lab. However, due to the inherent difficulties of X-ray angiographical image,
automatic ED and ES frame selection is a challenging task and still remains unsolved. The current clinical practice
uses manual selection, which is not only time consuming but also sensitive to different persons at different time. In
this paper, we propose to formulate the X-ray angiogram by a dynamical graphical model. Then the posterior density of
the left ventricular state is estimated by using Bayesian probability density propagation and adaptive background modeling.
Preliminary experimental results have demonstrated the superior performance of the proposed algorithm on clinical data.
Existing common medical image segmentation algorithms such as snake or graph cut usually could not generate
satisfying results for noisy medical images such as X-ray angiographical and ultrasound images where the image
quality is very poor including substantial background noise, low contrast, clutter, etc. In this paper, we present a
robust segmentation method for noisy medical image analysis using Principle Component Analysis (PCA) based
particle filtering. It exploits the prior clinical knowledge of desired object's shape through a PCA model. The
preliminary results have shown the effectiveness and efficiency of the proposed approach on both synthetic and
real clinical data.
Proc. SPIE. 6508, Visual Communications and Image Processing 2007
KEYWORDS: Detection and tracking algorithms, Data modeling, Video, 3D modeling, Monte Carlo methods, Particle filters, Algorithm development, Motion models, Systems modeling, Expectation maximization algorithms
In this paper, we propose two novel articulated object tracking approaches. The Decentralized Articulated Object Tracking
approach avoids the common practice of using a high-dimensional joint state representation for articulated object tracking.
Instead, it introduces a decentralized scheme and models the inter-part interaction within an innovative Bayesian framework.
To handle severe self-occlusions, we further extend the first approach by modeling high-level inter-unit interactions
and develop the Hierarchical Articulated Object Tracking algorithm within a consistent hierarchical framework. Preliminary
experimental results have demonstrated the superior performance of the proposed approaches for real-world videos
In this paper, we present a parallel multiple target tracking framework using multiple cooperative trackers. The multiple target occlusion problem is handled by modeling the interaction among different targets' observations and solving the data association through a recursive estimation. The computational complexity of the proposed approach increases linearly with the number of targets which yields a much faster implementation than existing multiple target tracking algorithms. Experimental results have been demonstrated on real-world videos.
We present a novel detection based particle filtering framework for real-time multi-object tracking (MOT). It integrates object detection and motion information with particle filter detecting and tracking the multiple objects dynamically and simultaneously. To demonstrate the approach, we concentrate on the complex multi-head tracking while the framework is general for any kind of objects. Three novel contributions are made: 1) Distinct with the conventional particle filter which generates particles from the prior density, we propose a novel importance function based on up to date detection and motion observation which is much closer to the desired posterior. 2) By integrating detection, the tracker can do the initialization automatically, handle new object appearance and hard occlusion for MOT. By using motion estimation, it can track fast motion activities. 3) Hybrid observations including color and detection information are used to calculate the likelihood which makes the approach more stable. The proposed method is superior to the available tracking methods for multi-head tracking and can handle not only the changes of scale, lighting, zooming, and orientation, but also fast motion, appearance, and hard occlusion.