Projection-based image registration in the presence of fixed-pattern noise.

A computationally efficient method for image registration is investigated that can achieve an improved performance over the traditional two-dimensional (2-D) cross-correlation-based techniques in the presence of both fixed-pattern and temporal noise. The method relies on transforming each image in the sequence of frames into two vector projections formed by accumulating pixel values along the rows and columns of the image. The vector projections corresponding to successive frames are in turn used to estimate the individual horizontal and vertical components of the shift by means of a one-dimensional (1-D) cross-correlation-based estimator.

Euclidean distance transform for binary images on reconfigurable mesh-connected computers.

The distance calculation in an image is a basic operation in computer vision, pattern recognition, and robotics. Several parallel algorithms have been proposed for calculating the Euclidean distance transform (EDT). Recently, Chen and Chuang proposed a parallel algorithm for computing the EDT on mesh-connected SIMD computers (1995).

Joint MAP registration and high-resolution image estimation using a sequence of undersampled images.

In many imaging systems, the detector array is not sufficiently dense to adequately sample the scene with the desired field of view. This is particularly true for many infrared focal plane arrays. Thus, the resulting images may be severely aliased.

Extended permutation filters and their application to edge enhancement.

Extended permutation (EP) filters are defined and analyzed. In particular, we focus on extended permutation rank selection (EPRS) filters. These filters are constrained to output an order statistic from an extended observation vector.

Gradient-based edge detection using nonlinear edge enhancing prefilters.

This correspondence examines the use of nonlinear edge enhancers as prefilters for edge detectors. The filters are able to convert smooth edges to step edges and suppress noise simultaneously. Thus, false alarms due to noise are minimized and edge gradient estimates tend to be large and localized.

Rank conditioned rank selection filters for signal restoration.

A class of nonlinear filters called rank conditioned rank selection (RCRS) filters is developed and analyzed in this paper. The RCRS filters are developed within the general framework of rank selection (RS) filters, which are filters constrained to output an order statistic from the observation set. Many previously proposed rank order based filters can be formulated as RS filters.