In this paper, a multipurpose watermarking scheme is proposed. The meaning of the word multipurpose is to make the proposed scheme as single watermarking scheme (SWS) or multiple watermarking scheme (MWS)
according to our requirement and convenience. We first segment the host image into blocks by means of Hilbert space filling curve and based on amount of DCT energy in the blocks, the threshold values are selected which make proposed scheme multipurpose. For embedding of n watermarks (n - 1) thresholds are selected. If the
amount of DCT energy of the block is less than the threshold value then ENOPV decomposition is performed and watermark is embedded in either low or high or all frequency sub-bands by modifying the singular values. If the amount of DCT energy of the block is greater than the threshold value then embedding is done by modifying
the singular values. This process of embedding through ENOPV-SVD and SVD is applied alternatively to all (n - 1) threshold values. Finally, modified blocks are mapped back to their original positions using inverse Hilbert space filling curve to get the watermarked image. A reliable extraction process is developed for extracting all
watermarks from attacked image. Experiments are done on different standard gray scale images and robustness is carried out by a variety of attacks.
KEYWORDS: Reflectivity, Monte Carlo methods, Edge detection, 3D image processing, Machine vision, Computer vision technology, 3D modeling, Image segmentation, Light sources, Visual process modeling
There are many objects in the real world, especially, man made objects often having a polyhedral shape. Shape
from shading (SFS) is a well known and the most robust technique of Computer vision. SFS is a first order
nonlinear, ill-posed problem. The main idea for solving ill-posed problems is to restrict the class of admissible
solution by introducing suitable a priori knowledge. To overcome the ill-posedness in SFS techniques, Bayesian
estimation of geometrical constraints are used. The Lambertian reflectance model is used in this method due to
its wide applicability in SFS techniques. The priori or the constraints are represented in the form of probability
distribution function, so that the Bayesian approach can be applied. The Monte Carlo method is applied
for generating the sample fields from the distribution so that the model can represent our priori knowledge and
constraints. The optimal estimators are also computed by using Monte Carlo method. The geometric constraints
for lines and planes are used in probabilistic manner to eliminate the rank deficiency to get the unique solution.
In case of incorrect line drawings, it is not always possible to reconstruct the object shape uniquely. To deal with
this problem, we have processed each planar face separately. Hence, the proposed method is applicable in case
of slight error in computation of vertex positions in the images of polyhedral objects. The proposed method is
used on various synthetic and real images and satisfactory results are obtained.
For stereo imaging, it is a general practice to use two cameras of same focal lengths, with their viewing axis
normal to the line joining the camera centres. This paper analyses the result of difference in orientations and
focal lengths of two arbitrary prespective viewing cameras, by deriving the epipolar lines and its correspoinding
equations. This enables one to find the correspondence search space in terms of focal length accuracies as well
as camera orientation parameteres. Relevant numerically simulated results are also given.
The process of reconstruction of a parabola in 3-D space from a pair of arbitrary perspective views obtains the set of parameters which represent the parabola. This method is widely used in many applications of 3-D object recognition, machine inspection and trajectory tracing. However in certain applications which require a large degree of accuracy, a study of errors in the process of reconstruction, with the help of a rigorous performance analysis is necessary. In this paper, the reconstruction of a 3D parabola from two perspective projections is described. In this process, the two end points and the vertex of the two pair of projections of the parabola are considered as feature points to reconstruct the parabola in 3-D. Simulation studies have been conducted to observe the effect of noise on errors in the process of reconstruction. The performance analysis illustrating the effect of noise, loss of accuracy due to mathematical calculations and parameters of imaging setup, on errors in reconstruction are presented. The angle between the reconstructed and original parabola in 3-D space has been used as a one of the criterion for the measurement of error. Smaller resolution of the image, certain geometric conditions and imaging setup produce poor performance in reconstruction. Results of this study are useful for the design of an optimal stereo-based imaging system, for best reconstruction with minimum error.
Reconstruction of a line in 3-D space using arbitrary perspective views involves the problem of obtaining the set of parameters representing the line. This is widely used for many applications of 3-D object recognition and machine inspection. A performance analysis of the reconstruction process in the presence of noise in the image planes is necessary in certain applications which require a large degree of accuracy. In this paper, a methodology, which is based on the concept of epipolar line, for the reconstruction of a 3-D line, from two arbitrary perspective views is given. In this problem the points in the second image plane, which correspond to points in the first image plane are found by using epipolar line method, by considering all the points in the first image plane. Then triangulation law is used to find the points in 3-D space. Using least square regression in 3-D, the parameters of a line in 3-D space are found. This least square regression problem is solved by two different methods. Simulation study results of this epipolar line based method, in presence of noise, as well as results of error analysis are given.
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