Robust, fast object detection systems are critical to the success of next-generation automotive vision systems. An important criteria is that the detection system be easily config...
When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of training data to avoid overfitting. In such cases dimensional...
Maximum likelihood (ML) estimation is widely used in many computer vision problems involving the estimation of geometric parameters, from conic fitting to bundle adjustment for s...
Traditional background modeling and subtraction methods have a strong assumption that the scenes are of static structures with limited perturbation. These methods will perform poo...
Treating visual object tracking as foreground and background classification problem has attracted much attention in the past decade. Most methods adopt mean shift or brute force s...