Kalman filter image processing

We have two distinct set of equations. Processing Forum Recent Topics. First we describe the Kalman filter and motivate its use as a mechanism for fault-tolerant. How are Kalman filters used in image processing. The algorithm presented here uses the extra information to predict the position and size of the bounding box. Both equation sets are applied at each kth state.

The objective is to construct a filter that will reduce the noise and in particular remain insensitive to sudden spike errors that can easily fool other types of filters such as a mean-square-error MSE filter.

Time Update prediction and Measurement Update correction. Using the Kalman filter in image processing tasks thus requires to extend the concept of the Kalman filter from random processes to two-dimensional random fields. Suppose that the image is captured by one or more cameras and the image is processed for display andor for further algorithmic analysis and processing. The Kalman Filter The Kalman filter is a computationally efficient recursive discrete linear filter. The contributions of this paper therefore are a set of recipes for implementation of the Kalman filter to a variety of real-time imaging settings the presentation of a set of object-oriented requirements and a design for a class of Kalman filters suitable for real-time image processing. Then the uncertainty gets bigger and bigger. The re-sult is a centroid area of the image. Time Update prediction and Measurement Update correction. W k is the process noise which is assumed to be drawn from a zero mean multivariate normal distribution with. Typical image processing application. Images are often modeled as random signals or collections of random variables.