Maximization of Mutual Information routines proved to be suitable for registration of multimodal images. Here a
method is proposed to select, in a set of candidates, the image which has a closer resemblance with a given external
one. Such algorithm is intended to serve within a wider scope procedure for the automatic texturing of 3D models,
where the initial 2D-3D registration problem is shifted to a 2D-2D registration challenge. In order to improve its
performance a number of variations in the way the Mutual Information is computed are introduced and a method to
judge its reliability is proposed.
A new automatic method capable of registering multimodal images like terrain maps and multispectral data is presented.
In order to speed up the processing, given the large amount of data typical of such settings, the method exploits a multi-resolution
approach, which may select different similarity measures in consideration of image resolution and size. In
fact, the performances of Cross Correlation and Maximization of Mutual Information (MMI) on images of different
resolution and size have been evaluated and are described The adaptive strategy adopted is designed to exploit the
strengths and to overcome the limitations of the similarity criteria employed. In case multimodal views are to be
registered on 3D models, MMI is to be preferred. The strategies to improve its performance also on smaller images are
presented.
3D models are often lacking a photorealistic appearance, due to low quality of the acquired texture, or to the complete
absence of it. Moreover, especially in case of reality based models, it is often of specific interest to texture with images
different from photos, like multispectral/multimodal views (InfraRed, X-rays, UV fluorescence etc), or images taken in
different moments in time. In this work, a fully automatic approach for texture mapping is proposed. The method relies
on the automatic extraction from the model geometry of appropriate depth maps, in form of images, whose pixels
maintain an exact correspondence with vertices of the 3D model. A multiresolution greedy method is then proposed to
generate the candidate depth maps which could be related with the given texture. In order to select the best match, a
suited similarity measure is computed, based on Maximixation of Mutual Information (MMI). 3D texturing is then
applied to the portion of the model which is visualized in the texture.
3D models are often lacking a photorealistic appearance, due to low quality of the acquired texture, or to the complete
absence of it. Moreover, especially in case of reality based models, it is often of specific interest to texture with images
different from photos, like multispectral/multimodal views (InfraRed, X-rays, UV fluorescence etc), or images taken in
different moments in time. In this work, a fully automatic approach for texture mapping is proposed. The method relies
on the automatic extraction from the model geometry of appropriate depth maps, in form of images, whose pixels
maintain an exact correspondence with vertices of the 3D model. A multiresolution method is here proposed to speed up
the automatic texturing phase. Maximization of Mutual Information (MMI) is used as similarity measure as it proved to
optimally exploit shared information, discarding unrelated features. 3D texturing is then applied to the portion of the
model which is visualized in the texture.
Though the current state of the art of image forensics permits to acquire very interesting information about
image history, all the instruments developed so far focus on the analysis of single images. It is the aim of this
paper to propose a new approach that moves the forensics analysis further, by considering groups of images
instead of single images. The idea is to discover dependencies among a group of images representing similar or
equal contents in order to construct a graph describing image relationships. Given the pronounced effect that
images posted on the Web have on opinions and bias in the networked age we live in, such an analysis could be
extremely useful for understanding the role of pictures in the opinion forming process. We propose a theoretical
framework for the analysis of image dependencies and describe a simple system putting the theoretical principles
in practice. The performance of the proposed system are evaluated on a few practical examples involving both
images created and processed in a controlled way, and images downloaded from the web.
KEYWORDS: 3D modeling, Digital watermarking, Visual process modeling, Visualization, 3D image processing, Distortion, Magnetorheological finishing, Venus, Head, Video
A novel pre-warping technique for 3D meshes is presented to prevent collusion attacks on fingerprinted 3D
models. By extending a similar technique originally proposed for still images, the surface of watermarked 3D
meshes is randomly and imperceptibly pre-distorted to protect embedded fingerprints against collusion attacks.
The peculiar problems set by the 3D nature of the data are investigated and solved by preserving the perceptual
quality of warped meshes. The proposed approach is independent of the chosen fingerprinting system. The
proposed algorithm can be implemented inside a watermarking chain, as an independent block, before performing
features extraction and watermark embedding. It follows that the detection algorithm is not influenced by the
anti-collusion block. The application of different collusion strategies has revealed the dificulty for colluders to
inhibit watermark detection while ensuring an acceptable quality of the attacked model.
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