Hui Li

Bio: Hui Li is an academic researcher from University of California, Santa Barbara. The author has contributed to research in topics: Image registration & Second-generation wavelet transform. The author has an hindex of 6, co-authored 6 publications receiving 2409 citations.

A contour-based approach to multisensor image registration

Abstract: Image registration is concerned with the establishment of correspondence between images of the same scene. One challenging problem in this area is the registration of multispectral/multisensor images. In general, such images have different gray level characteristics, and simple techniques such as those based on area correlations cannot be applied directly. On the other hand, contours representing region boundaries are preserved in most cases. The authors present two contour-based methods which use region boundaries and other strong edges as matching primitives. The first contour matching algorithm is based on the chain-code correlation and other shape similarity criteria such as invariant moments. Closed contours and the salient segments along the open contours are matched separately. This method works well for image pairs in which the contour information is well preserved, such as the optical images from Landsat and Spot satellites. For the registration of the optical images with synthetic aperture radar (SAR) images, the authors propose an elastic contour matching scheme based on the active contour model. Using the contours from the optical image as the initial condition, accurate contour locations in the SAR image are obtained by applying the active contour model. Both contour matching methods are automatic and computationally quite efficient. Experimental results with various kinds of image data have verified the robustness of the algorithms, which have outperformed manual registration in terms of root mean square error at the control points. >

Multi-sensor image fusion using the wavelet transform

Abstract: In the image fusion scheme presented in this paper, the wavelet transforms of the input images are appropriately combined, and the new image is obtained by taking the inverse wavelet transform of the fused wavelet coefficients. An area-based maximum selection rule and a consistency verification step are used for feature selection. A performance measure using specially generated test images is also suggested. >

Optical-to-SAR image registration using the active contour model

Abstract: An elastic contour matching method for the registration of the optical images with synthetic aperture radar (SAR) images is proposed. Using the contours from the optical image as the initial condition, accurate contour locations in the SAR image are obtained by applying an active contour model (Snake). The centroids of the closed contours and the salient points along the open contours are used as control points, through which the transformation parameters between the images can be estimated. The proposed algorithm has outperformed manual registration in terms of root mean square error at the control points. >

Registration of 3-D multimodality brain images by curve matching

Abstract: The 3D multimodality brain image registration algorithm proposed here is based on the matching of feature curves which are defined by the intersections of the interhemispherical fissure plane (IFP) and the skull surface The IFP as detected by a principal axes technique while the skull boundary in each scan is extracted by tracing the outermost closed contour The feature curves care rotated to lie on 3D planes which are parallel to each other so that the problem is reduced to a 2D curve matching task An MRI-PET image registration result is presented >

Objective image fusion performance measure

Abstract: A measure for objectively assessing the pixel level fusion performance is defined. The proposed metric reflects the quality of visual information obtained from the fusion of input images and can be used to compare the performance of different image fusion algorithms. Experimental results clearly indicate that this metric is perceptually meaningful.

Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images

Abstract: Methods have been proposed to produce multispectral images with enhanced spatial resolution using one or more images of the same scene of better spatial resolution. Assuming that the main concern of the user is the quality of the transformation of the multispectral content when increasing the spatial resolution, this paper defines the properties of such enhanced multispectral images. It then proposes both a formal approach and some criteria to provide a quantitative assessment of the spectral quality of these products. Five sets of criteria are defined. They measure the pe$ormance of a method to synthesize the radiometry in a single spectral band as well as the multispectral information when increasing the spatial resolution. The influence of the type of landscape present in the scene upon the assessment of the quality is underlined, as well as its dependence with scale. The whole approach is illustrated by the case of a SPOT image and three different standard methods to enhance the spatial resolution.