Eun-Young Elaine Kang

A graph-based global registration for 2D mosaics

We describe a graph-based global registration method for creating 2D mosaic images. When multi-frames overlap in space, global registration is necessary to minimize the accumulated registration errors. We use a graph to represent the temporal and spatial connectivity and show that global registration can be obtained through the search for an optimal path in the constructed graph. The definition of an adequate objective function characterizing the global registration provides a direct manipulation of the graph. The framework presented here allows the automatic construction of the graph, and the construction of a consistent mosaic from a collection of frames using projective transformations.

A handheld mirror simulation

We present the design and construction of a handheld mirror simulation device. The perception of the world reflected through a mirror depends on the viewers position with respect to the mirror and the 3-D geometry of the world. In order to simulate a real mirror on a computer screen, images of the observed world, consistent with the viewers position, must be synthesized and displayed in real- time. Our system is build around a LCD screen manipulated by the user, a single camera fixed on the screen, and a tracking device. The continuous input video stream and tracker data is used to synthesize, in real-time, a continuous video stream displayed on the LCD screen. The synthesized video stream is a close approximation of what the user would see on the screen surface if it were a real mirror. Our system provides a generic interface for applications involving rich, first-person interaction, such as the virtual daguerreotype.

Robust affine motion estimation in joint image space using tensor voting

Robustness of parameter estimation relies on discriminating inliers from outliers within the set of correspondences. In this paper, we present a method using tensor voting to eliminate outliers and estimating affine transformation parameters directly from covariance matrix of selected inliers without additional parameter estimation processing. Our approach is based on the representation of the correspondences in a decoupled joint image space and the use of the metric associated with the affine transformation. We enforce the metric property in a joint image space for tensor voting, detect several inlier groups corresponding distinct affine motions and directly estimate affine parameters from each set of inliers. The proposed approach is illustrated by a set of challenging examples.

A handheld virtual mirror

This sketch presents the design and construction of a handheld virtual mirror device. The perception of the world reflected through a mirror depends on the viewers position with respect to the mirror and the 3-D geometry of the world. In order to simulate a real mirror on a computer screen, images of the observed world, consistent with the viewers position, must be synthesized and displayed in real-time. Our system is build around a flat LCD screen manipulated by the user, a single camera fixed on the screen, and a tracking device. The continuous input video stream and tracker data is used to synthesize, in real-time, a continuous video stream displayed on the LCD screen. The synthesized video stream is a close approximation of what the user would see on the screen surface if it were a real mirror.

A layer extraction system based on dominant motion estimation and global registration

We describe a system that extracts layers from a video sequence based on a method estimating and stabilizing the dominant motion. Our approach performs pair-wise registration of images parameterized by 2D affine or projective transformation as the first step. This pair-wise registration is based on: (1) hierarchical parameter estimation and refinement; (2) feature-matching; (3) FFT (fast Fourier transformation)-based global matching; and (4) RANSAC-based parameter estimation. A global registration is then performed among overlapping multi-frames. The global registration is based on inferring the topology (i.e. spatio-temporal relation among frames) and the characterization of the likelihood within overlapping areas. As the last step, we extract the background layer that consists of static components of the video stream after motion estimation. Our background extraction is based on deriving a pixel-wise color distribution in time and provides a basis for a compact description of a video. The presented approach is illustrated by a set of challenging examples.

Smooth scene transition for virtual tour on the World Wide Web

This paper presents a robust image registration technique for creating smooth scene transitions for a Web-based virtual tour system. Easy and effective virtual tour (EEVT). EEVT has advantages over traditional geometry-based 3D rendering approach in that it does not require labor intensive 3D modeling process or high bandwidth for realistic virtual tour yet provides a virtual tour with free navigation and immersive experience through the WWW. EEVT constructs virtual tour from a set of images. It uses several snap shots of conventional photos without special tools, builds a simple 3D space within each photo using the spidery mesh technique, and expands the virtual spaces by connecting each space together. The connection between images is achieved by image registration, which finds correspondences automatically and estimates transformations. The image registration process is crucial for virtual tour applications in order to compose smooth transitional scenes between two views so that virtual tourists perceive continuous scenes during navigation. Our registration method uses a parametric approach and it includes the following key features for robust and accurate computation: 1) coarse-to-fine hierarchical estimation; 2) fast computation based on image feature-correspondences; 3) FFT-based global matching, 4) automatic outlier removal by RANSAC. The expanded virtual space creates a sense of navigational freedom for virtual tourists with less distorted viewing.

Non-iterative approach to multiple 2D motion estimation

We present an innovative method estimating multiple 2D motions from uncalibrated images. Our approach robustly and non-iteratively estimates multiple 2D parametric motions, affine or homography, from noisy initial matches without pre-specifying the number of motions This approach is based on: (1) a parametric motion model to detect and extract 2D affine or homography motions; (2) the representation of matching points in decoupled joint image spaces; (3) the characterization of the property associated with affine transformation in the defined (4) a non-iterative process to extract multiple 2D motions simultaneously based on tensor-voting; (5) local affine to global homography estimation. The major contribution of our work is the extension to our existing affine estimation method for homography estimation. The robustness of the approach is demonstrated with several results.

A robust and non-iterative estimation method of multiple 2D motions

This paper focuses on extracting 2D parametric motion regions from uncalibrated images. Our approach simultaneously infers and detects multiple image regions characterized by 2D motions, affine or homography transformations, from noisy initial matches. This approach is based on: (1) a parametric method to detect and extract 2D affine or homography motion regions; (2) the representation of the matching points in decoupled joint image spaces; (3) the characterization of the property associated with affine transformation in the defined spaces; (4) a non-iterative process to extract multiple 2D motions simultaneously based on tensor-voting; (5) local affine to global homography estimation; (6) region refinement based on a hybrid property: motion and color homogeneity. The robustness of the approach is demonstrated with several results.