A Robust Image Watermarking Based on Image Restoration Using SIFT

This paper introduces a novel robust watermarking scheme for digital images, which is robust against common signal processing and geometric distortion attacks. In order to be resistant to geometric distortion attacks, the matched feature points determined by the scale-invariance feature transform (SIFT) are used for image restoration to reduce the synchronization errors caused by geometric distortion attacks. An adaptive embedding scheme is applied in discrete Fourier transform (DFT) domain of each subimage. The watermark detection decision is based on the number of matched bits between the retrieved and original watermark in the embedding image blocks. Experimental results show that the proposed watermarking is robust to common signal processing attacks and geometric distortion attacks, including rotation, scaling, cropping, shearing and some combined attacks

Local Geometric Distortions Resilient Watermarking Scheme Based on Symmetry

As an efficient watermark attack method, geometric distortions destroy the synchronization between watermark encoder and decoder. And the local geometric distortion is a famous challenge in the watermark field. Although a lot of geometric distortions resilient watermarking schemes have been proposed, few of them perform well against local geometric distortion like random bending attack (RBA). To address this problem, this paper proposes a novel watermark synchronization process and the corresponding watermarking scheme. In our scheme, the watermark bits are represented by random patterns. The message is encoded to get a watermark unit, and the watermark unit is flipped to generate a symmetrical watermark. Then the symmetrical watermark is embedded into the spatial domain of the host image in an additive way. In watermark extraction, we first get the theoretically mean-square error minimized estimation of the watermark. Then the auto-convolution function is applied to this estimation to detect the symmetry and get a watermark units map. According to this map, the watermark can be accurately synchronized, and then the extraction can be done. Experimental results demonstrate the excellent robustness of the proposed watermarking scheme to local geometric distortions, global geometric distortions, common image processing operations, and some kinds of combined attacks

Digital Watermarking of Images towards Content Protection.

With the rapid growth of the internet and digital media techniques over the last decade, multimedia data such as images, video and audio can easily be copied, altered and distributed over the internet without any loss in quality. Therefore, protection of ownership of multimedia data has become a very significant and challenging issue. Three novel image watermarking algorithms have been designed and implemented for copyright protection. The first proposed algorithm is based on embedding multiple watermarks in the blue channel of colour images to achieve more robustness against attacks. The second proposed algorithm aims to achieve better trade-offs between imperceptibility and robustness requirements of a digital watermarking system. It embeds a watermark in adaptive manner via classification of DCT blocks with three levels: smooth, edges and texture, implemented in the DCT domain by analyzing the values of AC coefficients. The third algorithm aims to achieve robustness against geometric attacks, which can desynchronize the location of the watermark and hence cause incorrect watermark detection. It uses geometrically invariant feature points and image normalization to overcome the problem of synchronization errors caused by geometric attacks. Experimental results show that the proposed algorithms are robust and outperform related techniques found in literature

Rank-based image watermarking method with high embedding capacity and robustness

This paper presents a novel rank-based method for image watermarking. In the watermark embedding process, the host image is divided into blocks, followed by the 2-D discrete cosine transform (DCT). For each image block, a secret key is employed to randomly select a set of DCT coefficients suitable for watermark embedding. Watermark bits are inserted into an image block by modifying the set of DCT coefficients using a rank-based embedding rule. In the watermark detection process, the corresponding detection matrices are formed from the received image using the secret key. Afterward, the watermark bits are extracted by checking the ranks of the detection matrices. Since the proposed watermarking method only uses two DCT coefficients to hide one watermark bit, it can achieve very high embedding capacity. Moreover, our method is free of host signal interference. This desired feature and the usage of an error buffer in watermark embedding result in high robustness against attacks. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed method

Design, implementation, and evaluation of scalable content-based image retrieval techniques.

Wong, Yuk Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 95-100).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgement --- p.vChapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Contribution --- p.3Chapter 1.3 --- Organization of This Work --- p.5Chapter 2 --- Literature Review --- p.6Chapter 2.1 --- Content-based Image Retrieval --- p.6Chapter 2.1.1 --- Query Technique --- p.6Chapter 2.1.2 --- Relevance Feedback --- p.7Chapter 2.1.3 --- Previously Proposed CBIR systems --- p.7Chapter 2.2 --- Invariant Local Feature --- p.8Chapter 2.3 --- Invariant Local Feature Detector --- p.9Chapter 2.3.1 --- Harris Corner Detector --- p.9Chapter 2.3.2 --- DOG Extrema Detector --- p.10Chapter 2.3.3 --- Harris-Laplacian Corner Detector --- p.13Chapter 2.3.4 --- Harris-Affine Covariant Detector --- p.14Chapter 2.4 --- Invariant Local Feature Descriptor --- p.15Chapter 2.4.1 --- Scale Invariant Feature Transform (SIFT) --- p.15Chapter 2.4.2 --- Shape Context --- p.17Chapter 2.4.3 --- PCA-SIFT --- p.18Chapter 2.4.4 --- Gradient Location and Orientation Histogram (GLOH) --- p.19Chapter 2.4.5 --- Geodesic-Intensity Histogram (GIH) --- p.19Chapter 2.4.6 --- Experiment --- p.21Chapter 2.5 --- Feature Matching --- p.27Chapter 2.5.1 --- Matching Criteria --- p.27Chapter 2.5.2 --- Distance Measures --- p.28Chapter 2.5.3 --- Searching Techniques --- p.29Chapter 3 --- A Distributed Scheme for Large-Scale CBIR --- p.31Chapter 3.1 --- Overview --- p.31Chapter 3.2 --- Related Work --- p.33Chapter 3.3 --- Scalable Content-Based Image Retrieval Scheme --- p.34Chapter 3.3.1 --- Overview of Our Solution --- p.34Chapter 3.3.2 --- Locality-Sensitive Hashing --- p.34Chapter 3.3.3 --- Scalable Indexing Solutions --- p.35Chapter 3.3.4 --- Disk-Based Multi-Partition Indexing --- p.36Chapter 3.3.5 --- Parallel Multi-Partition Indexing --- p.37Chapter 3.4 --- Feature Representation --- p.43Chapter 3.5 --- Empirical Evaluation --- p.44Chapter 3.5.1 --- Experimental Testbed --- p.44Chapter 3.5.2 --- Performance Evaluation Metrics --- p.44Chapter 3.5.3 --- Experimental Setup --- p.45Chapter 3.5.4 --- Experiment I: Disk-Based Multi-Partition Indexing Approach --- p.45Chapter 3.5.5 --- Experiment II: Parallel-Based Multi-Partition Indexing Approach --- p.48Chapter 3.6 --- Application to WWW Image Retrieval --- p.55Chapter 3.7 --- Summary --- p.55Chapter 4 --- Image Retrieval System for IND Detection --- p.60Chapter 4.1 --- Overview --- p.60Chapter 4.1.1 --- Motivation --- p.60Chapter 4.1.2 --- Related Work --- p.61Chapter 4.1.3 --- Objective --- p.62Chapter 4.1.4 --- Contribution --- p.63Chapter 4.2 --- Database Construction --- p.63Chapter 4.2.1 --- Image Representations --- p.63Chapter 4.2.2 --- Index Construction --- p.64Chapter 4.2.3 --- Keypoint and Image Lookup Tables --- p.67Chapter 4.3 --- Database Query --- p.67Chapter 4.3.1 --- Matching Strategies --- p.68Chapter 4.3.2 --- Verification Processes --- p.71Chapter 4.3.3 --- Image Voting --- p.75Chapter 4.4 --- Performance Evaluation --- p.76Chapter 4.4.1 --- Evaluation Metrics --- p.76Chapter 4.4.2 --- Results --- p.77Chapter 4.4.3 --- Summary --- p.81Chapter 5 --- Shape-SIFT Feature Descriptor --- p.82Chapter 5.1 --- Overview --- p.82Chapter 5.2 --- Related Work --- p.83Chapter 5.3 --- SHAPE-SIFT Descriptors --- p.84Chapter 5.3.1 --- Orientation assignment --- p.84Chapter 5.3.2 --- Canonical orientation determination --- p.84Chapter 5.3.3 --- Keypoint descriptor --- p.87Chapter 5.4 --- Performance Evaluation --- p.88Chapter 5.5 --- Summary --- p.90Chapter 6 --- Conclusions and Future Work --- p.92Chapter 6.1 --- Conclusions --- p.92Chapter 6.2 --- Future Work --- p.93Chapter A --- Publication --- p.94Bibliography --- p.9

A Novel Inpainting Framework for Virtual View Synthesis

Multi-view imaging has stimulated significant research to enhance the user experience of free viewpoint video, allowing interactive navigation between views and the freedom to select a desired view to watch. This usually involves transmitting both textural and depth information captured from different viewpoints to the receiver, to enable the synthesis of an arbitrary view. In rendering these virtual views, perceptual holes can appear due to certain regions, hidden in the original view by a closer object, becoming visible in the virtual view. To provide a high quality experience these holes must be filled in a visually plausible way, in a process known as inpainting. This is challenging because the missing information is generally unknown and the hole-regions can be large. Recently depth-based inpainting techniques have been proposed to address this challenge and while these generally perform better than non-depth assisted methods, they are not very robust and can produce perceptual artefacts. This thesis presents a new inpainting framework that innovatively exploits depth and textural self-similarity characteristics to construct subjectively enhanced virtual viewpoints. The framework makes three significant contributions to the field: i) the exploitation of view information to jointly inpaint textural and depth hole regions; ii) the introduction of the novel concept of self-similarity characterisation which is combined with relevant depth information; and iii) an advanced self-similarity characterising scheme that automatically determines key spatial transform parameters for effective and flexible inpainting. The presented inpainting framework has been critically analysed and shown to provide superior performance both perceptually and numerically compared to existing techniques, especially in terms of lower visual artefacts. It provides a flexible robust framework to develop new inpainting strategies for the next generation of interactive multi-view technologies

Dynamical Systems

Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...

Sparse image approximation with application to flexible image coding

Natural images are often modeled through piecewise-smooth regions. Region edges, which correspond to the contours of the objects, become, in this model, the main information of the signal. Contours have the property of being smooth functions along the direction of the edge, and irregularities on the perpendicular direction. Modeling edges with the minimum possible number of terms is of key importance for numerous applications, such as image coding, segmentation or denoising. Standard separable basis fail to provide sparse enough representation of contours, due to the fact that this kind of basis do not see the regularity of edges. In order to be able to detect this regularity, a new method based on (possibly redundant) sets of basis functions able to capture the geometry of images is needed. This thesis presents, in a first stage, a study about the features that basis functions should have in order to provide sparse representations of a piecewise-smooth image. This study emphasizes the need for edge-adapted basis functions, capable to accurately capture local orientation and anisotropic scaling of image structures. The need of different anisotropy degrees and orientations in the basis function set leads to the use of redundant dictionaries. However, redundant dictionaries have the inconvenience of giving no unique sparse image decompositions, and from all the possible decompositions of a signal in a redundant dictionary, just the sparsest is needed. There are several algorithms that allow to find sparse decompositions over redundant dictionaries, but most of these algorithms do not always guarantee that the optimal approximation has been recovered. To cope with this problem, a mathematical study about the properties of sparse approximations is performed. From this, a test to check whether a given sparse approximation is the sparsest is provided. The second part of this thesis presents a novel image approximation scheme, based on the use of a redundant dictionary. This scheme allows to have a good approximation of an image with a number of terms much smaller than the dimension of the signal. This novel approximation scheme is based on a dictionary formed by a combination of anisotropically refined and rotated wavelet-like mother functions and Gaussians. An efficient Full Search Matching Pursuit algorithm to perform the image decomposition in such a dictionary is designed. Finally, a geometric image coding scheme based on the image approximated over the anisotropic and rotated dictionary of basis functions is designed. The coding performances of this dictionary are studied. Coefficient quantization appears to be of crucial importance in the design of a Matching Pursuit based coding scheme. Thus, a quantization scheme for the MP coefficients has been designed, based on the theoretical energy upper bound of the MP algorithm and the empirical observations of the coefficient distribution and evolution. Thanks to this quantization, our image coder provides low to medium bit-rate image approximations, while it allows for on the fly resolution switching and several other affine image transformations to be performed directly in the transformed domain