This paper presents an on-line Statistical Process Control (SPC) technique, based on a Generalized Likelihood Ratio Test (GLRT), for detecting and estimating mean shifts in autocorrelated processes that follow a normally distributed Autoregressive Integrated Moving Average (ARIMA) model. The GLRT is applied to the uncorrelated residuals of the appropriate time-series model. The performance of t...

Context. Fortunately, the most commonly used in parametric statistics assumptions such as normality, linearity, independence, are not always fulfilled real practice. The main reason for this is appearance of observations data samples that differ from bulk data, a result which sample becomes heterogeneous. application conditions generally accepted estimation procedures, example, mean, entails bi...

We propose a non-intrusive eye tracking system intended for the use of everyday gaze typing using web cameras. We argue that high precision in gaze tracking is not needed for on-screen typing due to natural language redundancy. This facilitates the use of low-cost video components for advanced multi-modal interactions based on video tracking systems. Robust methods are needed to track the eyes ...

We propose a novel adaptive test of goodness-of-fit, with computational cost linear in the number of samples. We learn the test features that best indicate the differences between observed samples and a reference model, by minimizing the false negative rate. These features are constructed via Stein’s method, meaning that it is not necessary to compute the normalising constant of the model. We a...

This paper proposes a general Kernel-Bayesian framework for object tracking. In this framework, the kernel based method—mean shift algorithm is embedded into the Bayesian framework seamlessly to provide a heuristic prior information to the state transition model, aiming at effectively alleviating the heavy computational load and avoiding sample degeneracy suffered by the conventional Bayesian t...

Object tracking is one of the fundamental problems in computer vision, but existing efficientmethodsmay not be suitable for spatial object tracking.Therefore, it is necessary to propose a more intelligent mathematical model. In this paper, we present an intelligent modeling method using an enhanced mean shift method based on a perceptual spatial-space generation model. We use a series of basic ...

In this paper, we demonstrate an effective tracking system for moving face and hand objects in real time. Our approach contains of three substages: adaptive background subtraction, face and hand detection using skin color and tracking them with pixels correlations respectively. There are famous robust tracking methods such as mean shift and active shape model in the literature. Despite providin...

In this paper, we propose real-time hand tracking with a depth camera by using a Kalman Filter and an improved DAM-Shift(Depthbased adaptive mean shift) algorithm for occlusion handling. DAM-Shift is a useful algorithm for hand tracking, but difficult to track when occlusion occurs. To detect the hand region, we use a classifier that combines a boosting and a cascade structure. To verify occlus...

We develop a mixture procedure for multi-sensor systems to monitor data streams for a change-point that causes a gradual degradation to a subset of the streams. Observations are assumed to be initially normal random variables with known constant means and variances. After the change-point, observations in the subset will have increasing or decreasing means. The subset and the rate-of-changes ar...

In this paper we present a new method to enhance object detection by removing false alarms and merging multiple detections in a principled way with few parameters. The method models the output of an object classifier which we consider as the context. A hierarchical model is built using the detection distribution around a target sub-window to discriminate between false alarms and true detections...