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In this paper, we present a robust multibit image watermarking scheme to undertake the common image-processing attacks as well as affine distortions. This scheme combines contrast modulation and effective synchronization for large payload and high robustness. We analyze the robustness, payload, and the lower bound of fidelity. Regarding watermark resynchronization under affine distortions, we develop a self-referencing rectification method to detect the distortion parameters for reconstruction by the center of mass in affine covariant regions. The effectiveness and advantages of the proposed scheme are confirmed by experimental results, which show the superior performance as comparing against several state-of-the-art watermarking methods.

This paper presents a stereoscopic video compression scheme using a novel fast affine motion estimation technique. A temporal scalable approach is used to achieve backward compatibility with a standard definition TV. We use an adaptive mode selection scheme from three temporal locations in both channels. Both block-based and affine-motion based approaches are used to achieve two levels of improvements with different complexities. An innovative motion estimation technique using Gauss–Newton optimization and pyramid processing is implemented to efficiently estimate affine parameters. Unlike other Gauss–Newton approaches, our search technique uses only addition, subtraction and multiplication and it converges within four iterations, which implies great complexity reduction. An efficient and robust affine motion prediction yields significant over the disparity-based approach. Part of the disparity-based approach has been tested in the rigorous MPEG-2 bitstream exchange process, and adopted in the MPEG-2 Multi-View Profile (MVP).

Conventional techniques for security of data, designed by using only one of the security mechanisms, cryptography or steganography, are suitable for limited applications only. In this paper, we propose a crypto-stego system that would be appropriate for secure transmission of different forms of data. In the proposed crypto-stego system, we present a mechanism to provide secure transmission of data by multiple safety measures, firstly by applying encryption using Affine Transform and Discrete Cosine Transform (DCT) and then merging this encrypted data with an image, randomly chosen from a set of available images, and sending the image so obtained to the receiver at the other end through the network. The data to be sent over a communication channel may be a gray-scale or colored image, or a text document (doc, .txt, or .pdf file). As it is encrypted and sent hidden in an image, it avoids any attention to itself by the observers in the network. At the receiver’s side, reverse transformations are applied to obtain the original information. The experimental results, security analysis and statistical analysis for gray-scale images, RGB images, text documents (.doc, .txt, .pdf files), show robustness and appropriateness of the proposed crypto-stego system for secure transmission of the data through unsecured network. The security analysis and key space analysis demonstrate that the proposed technique is immune from cryptanalysis.

The extraction of affine invariant features plays an important role in many fields of computer vision. Contour-based wavelet methods are unapplicable to objects with several separable components. In this paper, a method is proposed by converting the object into a closed curve, which is called radial centroid curve (RCC). Point on this curve is the centroid of radial line segment from centroid of the object. It is shown that the RCC derived from the affine transformed object is the same affine transformed version as that of the original object. An affine invariant function (AIF) is constructed by applying stationary wavelet transform (SWT) to the derived RCC. This scheme is applicable to objects with several separable components. Several experiments have been conducted to evaluate the performance of the proposed method.

Affine transform is to describe the same target at different viewpoints to obtain the relationship between images of approximate model. Affine invariant feature extraction plays an important role in object recognition and image registration. Firstly, the definition of polar radius integral transform (PRIT) is put forward by means of the characterization of affine transform mapping straight line into straight line, where PRIT computes the integral along the polar radius direction and converts images into closed curves which keep the same affine transform with original images. Secondly, in order to extract affine invariant feature, an affine invariant feature extraction algorithm is also given based on PRIT. The proposed algorithm can be used to combine contour-based methods with region-based methods. It has some advantages of fewer amounts of computations and feasibility of feature extraction for objects with several components. Finally, the capability of anti-noise (Gaussian noise, salt and pepper noise) of PRIT is discussed. The simulation experiment results show that PRIT can effectively extract the affine invariant features, but also the low order PRIT has very strong robustness to noise.

Object Recognition and Tracking has been one of the top research interests in the field of Computer Vision. Methodologies such as Background Reduction, Color Thresholds, Filters, Contours and others have been used to detect these objects as well as determine their movement based on chronological data and events. However these methodologies are usually limited to and are typically used only on fixed-point cameras. Having a moving camera would present multiple difficulties in terms of background reduction and object tracking as well as determining if the object being tracked is actually moving or only due to the movement of the camera that the object seems to be moving. This research investigates a method to allow tracking of the direction of the camera, which may be used for object recognition and tracking, using a single moving camera.