DECOLOR
Moving Object Detection by Detecting Contiguous Outliers in the Low-rank Representation
This is a joint work with Dr. Xiaowei Zhou and Prof. Weichuan Yu.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3): 597-610, 2013.
Abstract
Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based methods are usually limited when coping with complex scenarios such as nonrigid motion, illumination change and dynamic background. In this paper, we show that above challenges can be addressed in a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, and it can naturally model complex background and avoid the complicated computation of foreground motion. It turns out that the optimization can be solved by an alternating algorithm efficiently. Also, we explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios.
A free copy of this paper is available on Arxiv.
Our review paper on low-rank approximation.
X, Zhou, C. Yang, H. Zhao and W. Yu. Low-Rank Modeling and Its Applications in Image Analysis. ACM Computing Surveys. 2014. [The Matlab code to produce the results presented in this paper]
Results
Application of DECOLOR for Automatic Mitral Leaflet Tracking in Echocardiography
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012
Joint work with Xiaowei Zhou and Weichuan Yu.
Abstract
Tracking the mitral valve leaflet in an ultrasound sequence is a challenging task because of the poor image quality and fast and irregular leaflet motion. Previous algorithms usually applied standard segmentation methods based on edges, object intensity and anatomical information to segment the mitral leaflet in static frames. However, they are limited in practical applications due to the requirement of manual input for initialization or large annotated datasets for training. In this paper we present a completely automatic and unsupervised algorithm for mitral leaflet detection and tracking. We demonstrate that the image sequence of a cardiac cycle can be well approximated with a low-rank matrix, except for the mitral leaflet region with fast motion and tissue deformation. Based on this difference, we propose to track the mitral leaflet by detecting contiguous outliers in the low-rank representation. With this formulation, the leaflet is tracked using the motion cue, but the complicated motion computation is avoided. To the best of our knowledge, the proposed algorithm is the first unsupervised method for mitral leaflet tracking.