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Image Registration

Research on image registration addresses issues related to multiscale registration, integrating robust segmentation tools with the contour matching approach, and in developing novel image representation schemes for maintaining large databases of various image datasets.

Registration with Fit Assessment

Image registration is an important operation in remote sensing applications that basically involves the identification of many control points in the images. The increased volume of satellite images has reinforced the need for automatic image registration methods. This project introduces innovative techinque for satelite imagery and aerial photography. more...

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Mosaicking based framework for local image processing

Here we present a framework for various local image processing. Locality is achieved by using tiles and novelty is in the way the tiles are assembled back together to constitute an image. Our approach is robust to acquisition parameters and temporal changes. Since we blend the images using pixel data from the spatial domain (as opposed to fusing the information in a transform domain), the resulting images have fewer artifacts. The results presented for microscopy and hand held consumer cameras demonstrate good quality and computational efficiency. Different applications of our approach are presented in the following. more...

Corner Detection

(2003-now) In this project we developed a unified framework to analyze different corner detectors that are based on the spectral properties of the image autocorrelation matrix. The main mathematical tool used to carry out our analysis is based on the condition theory. Using this approach we were able to show that some of the most commonly used corner detectors can be unified by choosing an appropriate matrix norm. The mathematical framework we developed allowed us to easily extend some of the commonly used detectors to images with pixel dimension greater than 2 (i.e. tomographic images) and intensity dimension greater than one (like RGB or multispectral images). We are now using these tools to identify which relevant properties are shared by some of the commonly used corner detectors and which ones make them unique. Moreover we are interested in applying the condition theory framework to detect the intrinsic structure of a neighborhood of an image point. This is leading us to develop a multiscale condition theory based point detector framework. more...

Curves

(2003-2005) In this project we are investigating efficient methods to detect, describe and match curves, despite the geometric distortions that arise when the scene is imaged from different points of view. In this context we developed a normalization procedure that allows to extract the shape of a curve independently from an affine transformations of the curve itself. We also introduced a physically motivated descriptor based on the Helmholtz equation to label the curves for matching and retrieval purposes. Current research is focused on alleviating the computational burden for the detection and description of the curves and in combining together shape and content of image patches. We are also intrested in extending our methods for 3D surfaces. more...

Ransac

(2004-now) In this project we are trying to construct a RANSAC framework that will enable us to perform parameter estimation robustly in different scenarios characterized by the presence of large quantities of outliers. We are developing methods that will speed up the convergence of the traditional algorithm, that will allow us to perform the fusion of information coming from different sources and that can cope with the presence of multiple models. We are also interested in characterizing the stability of the solutions found by RANSAC. more...

Multiview Curve Database

(2004) The Multiview Curve Database (MCD) has been constructed  to test the performance of  shape/contour descriptors in the presence of perspective distortions. The images of 40 shapes (extracted from the MPEG-7 shape database) have been taken under seven different points of view. The contour of each object has been extracted, an arbitrary rotation has been applied and 7 new contours have been generated by mirroring the original ones. The database consists of 14 contours for each of the 40 objects. more...

Multisensor Image Registration and Fusion

This research project include multiscale registration using robust image segmentation and matching algorithms with the aim of developing faster registration algorithms. In addition, efficient data representation schemes are being investigated by integrating segmentation, registration, fusion and data decorrelation in a single framework. more...