A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Infrared and visible image fusion methods and applications: A survey

Multifocus image fusion using the nonsubsampled contourlet transform

Our framework is the synthesis of multispectral images (MS) at higher spatial resolution, which should be as close as possible to those that would have been acquired by the corresponding sensors if they had this high resolution. This synthesis is performed with the help of a high spatial but low spectral resolution image: the panchromatic (Pan) image. The fusion of the Pan and MS images is classically referred as pan-sharpening. A fused product reaches good quality only if the characteristics and differences between input images are taken into account. Dissimilarities existing between these two data sets originate from two causes-different times and different spectral bands of acquisition. Remote sensing physics should be carefully considered while designing the fusion process. Because of the complexity of physics and the large number of unknowns, authors are led to make assumptions to drive their development. Weaknesses and strengths of each reported method are raised and confronted to these physical constraints. The conclusion of this critical survey of literature is that the choice in the assumptions for the development of a method is crucial, with the risk to drastically weaken fusion performance. It is also shown that the Amelioration de la Resolution Spatiale par Injection de Structures concept prevents from introducing spectral distortion into fused products and offers a reliable framework for further developments.

/pdf/synthesis-of-multispectral-images-to-high-spatial-resolution-48x8fvro0n.pdf

Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics

A review of remote sensing image fusion methods

Color transfer based remote sensing image fusion using non-separable wavelet frame transform

This paper describes a region-based multiresolution (MR) image fusion algorithm. The MR segmentations of source images to be fused are obtained by fuzzy c-means clustering. From these MR region representations of all the input images, we obtain a shared MR region representation to label all the input images. The fusion process is guided by these MR region representations. Experiment results using real data show that the proposed algorithm works well in multisensor image fusion.

A region-based image fusion algorithm using multiresolution segmentation

In this paper, a goal-driven net-enabled distributed data fusion system is described for CanCoastWatch (CCW) project. Multiple sensors are deployed and managed to achieve the goals of situation assessment using a net-enabled architecture. The local tracks reported by multiple sensors are first integrated into global tracks. Decision making is then performed on basic sub-goals that can be directly derived from the fused global tracks. Finally, a goal-driven rule-based expert system uses the basic sub-goal decisions for goal reasoning.

High Level data fusion system for CanCoastWatch

Due to the limited depth-of-field of optical lenses, it is difficult to get an image with all objects in focus. One way to overcome this problem is to take several images with different focus points and combine them into a single composite which contains all the regions full focused. This paper describes a pixel visibility based multifocus image fusion algorithm. For each pixel in the source multifocus images, the pixel visibility is calculated. The fusion procedure is performed by a selection mode according to the magnitude of pixel visibility. Experiments show that the proposed algorithm works well in multifocus image fusion.

Pixel visibility based multifocus image fusion

Multiple radars are often used to form a radar network in target tracking applications. Through fusing the complementary and redundant information obtained from multiple radars, we can improve the performance of target tracking. In multisensor target tracking, sensor registration is an essential process to eliminate the tracking errors caused by each sensor's systematic biases. In this paper, we present an expectation maximization (EM) based simultaneous registration and fusion algorithm for multiple radars network. This simultaneous registration and fusion approach has advantages over other track registration techniques such as augmented Kalman filtering approach. Systematic biases (including radar time bias, radar two angular biases, and radar range bias) are estimated using the EM algorithm. The EM algorithm can guarantee that the estimated systematic biases can converge to a stationary point of the maximum likelihood function. In order to track manoeuvering targets, three kinematic models are used to have a more complete description of the motion of a target: the constant velocity (CV), the constant acceleration (CA), and the coordinated turn (CT) models. The conditional expectations and covariances of the system states can be effectively computed by interactive multiple model (IMM) approach. The IMM method is a recursive hybrid filtering technique that provides a good balance between performance and complexity. We employ a fixed-interval smoother-based IMM approach to estimate the system states. The forward and backward Kalman filters are used to obtain a smooth estimate. The IMM approach is combined with the EM algorithm for simultaneous registration and fusion.

Zhenhua Li

Papers

Color transfer based remote sensing image fusion using non-separable wavelet frame transform

A region-based image fusion algorithm using multiresolution segmentation

High Level data fusion system for CanCoastWatch

Pixel visibility based multifocus image fusion

An Expectation Maximization Based Simultaneous Registration and Fusion Algorithm for Radar Networks