Yasaroglu, Yagiz
Alatan, Abdullah Aydın
A novel watermarking method is presented in which the data embedded into a 3D model is extracted from an arbitrary 2D view by using a perspective projective invariant. The data is embedded into 3D positions of selected interest points on a 3D mesh. Determining the interest point modification vectors for ensuring watermark detection constitutes an important part of the proposed method. Different watermark embedding schemes based on optimization of the watermark function are implemented and evaluated. Another important contribution of the proposed method is selection of the interest points to ensure that they remain detectable after data embedding and rendering. A novel method to identify such repeatable interest points is also presented. Simulations are performed on random 3D point sets as well as realistic 3D models. The results indicate that data embedding in 3D and detection in 2D promises a new direction in watermarking research.


Yasaroglu, Yagiz; Alatan, Abdullah Aydın (2011-05-18)
A 3D-2D watermarking method using perspective projective invariance is proposed. Data is embedded in relative positions of six points on a 3D mesh by translating one of them, and extracted from any 2D view generated as long as the points remain visible. To evaluate the performance of the perspective invariant, a watermarking system with a very simple interest point detection method is implemented. Simulations are made on six 3D meshes with different watermark strengths and view angles. Very promising result...
3D Object Recognition by Geometric Hashing
Eskizara, Omer; Akagündüz, Erdem; Ulusoy, İlkay (2009-01-01)
Using transform invariant 3D fatures obtained from a database of 3D range images, geometric hashing is applied for the purpose of 3D object recognition. Mean (H) and Gaussian (K) curvature values within a scale-space of the surface is used Since H and K values are used and a scale-space of the surface is constructed the method is independent of transformation and resolution. The method is tested on the Stuttgart 3D range image database [1].
3D object representation using transform and scale invariant 3D features
AKAGÜNDÜZ, Erdem; Ulusoy, İlkay (2007-10-21)
An algorithm is proposed for 3D object representation using generic 3D features which are transformation and scale invariant. Descriptive 3D features and their relations are used to construct a graphical model for the object which is later trained and then used for detection purposes. Descriptive 3D features are the fundamental structures which are extracted from the surface of the 3D scanner output. This surface is described by mean and Gaussian curvature values at every data point at various scales and a ...
First-order and second-order statistical analysis of 3d and 2d image structure
Kalkan, Sinan; Kruger, N. (Informa UK Limited, 2007-06-01)
In the first part of this article, we analyze the relation between local image structures (i.e., homogeneous, edge-like, corner-like or texture-like structures) and the underlying local 3D structure (represented in terms of continuous surfaces and different kinds of 3D discontinuities) using range data with real-world color images. We find that homogeneous image structures correspond to continuous surfaces, and discontinuities are mainly formed by edge-like or corner-like structures, which we discuss regard...
Continuous dimensionality characterization of image structures
Felsberg, Michael; Kalkan, Sinan; Kruger, Norbert (Elsevier BV, 2009-05-04)
Intrinsic dimensionality is a concept introduced by statistics and later used in image processing to measure the dimensionality of a data set. In this paper, we introduce a continuous representation of the intrinsic dimension of an image patch in terms of its local spectrum or, equivalently, its gradient field. By making use of a cone structure and barycentric co-ordinates, we can associate three confidences to the three different ideal cases of intrinsic dimensions corresponding to homogeneous image patche...
Citation Formats
Y. Yasaroglu and A. A. Alatan, “E3D-D2D: EMBEDDING IN 3D, DETECTION IN 2D THROUGH PROJECTIVE INVARIANTS,” 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53268.