2004 - Paper Copilot

A wavelet-based image fusion tutorial

Gonzalo Pajares, Jesus Manuel de la Cruz

Universidad Complutense de Madrid

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The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more suitable for human and machine perception or further image-processing tasks such as segmentation, feature extraction and object recognition. Di4erent fusion methods have been proposed in literature, including multiresolution analysis. This paper is an image fusion tutorial based on wavelet decomposition, i.e. a multiresolution image fusion approach. We can fuse images with the same or di4erent resolution level, i.e. range sensing, visual CCD, infrared, thermal or medical. The tutorial performs a synthesis between the multiscale-decomposition-based image approach (Proc. IEEE 87 (8) (1999) 1315), the ARSIS concept (Photogramm. Eng. Remote Sensing 66 (1) (2000) 49) and a multisensor scheme (Graphical Models Image Process. 57 (3) (1995) 235). Some image fusion examples illustrate the proposed fusion approach. A comparative analysis is carried out against classical existing strategies, including those of multiresolution. ? 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

SSIM Image Quality Assessment: From Error Visibility to Structural Similarity

Zhou Wang; A.C. Bovik; H.R. Sheikh; E.P. Simoncelli

New York University; The University of Texas at Austin

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Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000.

Animatable Facial Reflectance Fields

Tim Hawkins, Andreas Wenger, Chris Tchou, Andrew Gardner, Fredrik Goransson, Paul Debevec

University of Southern California Institute for Creative Technologies; Linköping University Norrköping Visualization and Interaction Studio

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We present a technique for creating an animatable image-based appearance model of a human face, able to capture appearance variation over changing facial expression, head pose, view direction, and lighting condition. Our capture process makes use of a specialized lighting apparatus designed to rapidly illuminate the subject sequentially from many different directions in just a few seconds. For each pose, the subject remains still while six video cameras capture their appearance under each of the directions of lighting. We repeat this process for approximately 60 different poses, capturing different expressions, visemes, head poses, and eye positions. The images for each of the poses and camera views are registered to each other semi-automatically with the help of fiducial markers. The result is a model which can be rendered realistically under any linear blend of the captured poses and under any desired lighting condition by warping, scaling, and blending data from the original images. Finally, we show how to drive the model with performance capture data, where the pose is not necessarily a linear combination of the original captured poses.

Tag: 2004

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