Assessment of 3D mesh watermarking techniques

With the increasing usage of three-dimensional meshes in Computer-Aided Design (CAD), medical imaging, and entertainment fields like virtual reality, etc., the authentication problems and awareness of intellectual property protection have risen since the last decade. Numerous watermarking schemes have been suggested to protect ownership and prevent the threat of data piracy. This paper begins with the potential difficulties that arose when dealing with three-dimension entities in comparison to two-dimensional entities and also lists possible algorithms suggested hitherto and their comprehensive analysis. Attacks, also play a crucial role in deciding a watermarking algorithm so an attack based analysis is also presented to analyze resilience of watermarking algorithms under several attacks. In the end, some evaluation measures and potential solutions are brooded over to design robust and oblivious watermarking schemes in the future

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

User-appropriate viewer for high resolution interactive engagement with 3D digital cultural artefacts.

The core mission of museums and cultural institutions is the preservation, study and presentation of cultural heritage content. In this technological age, the creation of digital datasets and archives has been widely adopted as one way of seeking to achieve some or all of these goals. However, there are many challenges with the use of these data, and in particular the large numbers of 3D digital artefacts that have been produced using methods such as non- contact laser scanning. As public expectation for more open access to information and innovative digital media increases, there are many issues that need to be rapidly addressed. The novel nature of 3D datasets and their visualisation presenting unique issues that impede use and dissemination. Key questions include the legal issues associated with 3D datasets created from cultural artefacts; the complex needs of users who are interacting with them; a lack of knowledge to texture and assess the visual quality of the datasets; and how the visual quality of the presented dataset relates to the perceptual experience of the user. This engineering doctorate, based on an industrial partnership with the National Museums of Liverpool and Conservation Technologies, investigates these questions and offers new ways of working with 3D cultural heritage datasets. The research outcomes in the thesis provide an improved understanding of the complexity of intellectual property law in relation to 3D cultural heritage datasets and how this impacts dissemination of these types of data. It also provides tools and techniques that can be used to understand the needs of a user when interacting with 3D cultural content. Additionally, the results demonstrate the importance of the relationship between texture and polygonal resolution and how this can affect the perceived visual experience of a visitor. It finds that there is an acceptable cost to texture and polygonal resolution to offer the best perceptual experience with 3D digital cultural heritage. The results also demonstrate that a non-textured mesh may be as highly received as a high resolution textured mesh. The research presented provides methodologies and guidelines to improve upon the dissemination and visualisation of 3D cultural content; enhancing and communicating the significance of their 3D collections to their physical and virtual visitors. Future opportunities and challenges for disseminating and visualising 3D cultural content are also discussed

ONLINE HIERARCHICAL MODELS FOR SURFACE RECONSTRUCTION

Applications based on three-dimensional object models are today very common, and can be found in many fields as design, archeology, medicine, and entertainment. A digital 3D model can be obtained by means of physical object measurements performed by using a 3D scanner. In this approach, an important step of the 3D model building process consists of creating the object's surface representation from a cloud of noisy points sampled on the object itself. This process can be viewed as the estimation of a function from a finite subset of its points. Both in statistics and machine learning this is known as a regression problem. Machine learning views the function estimation as a learning problem to be addressed by using computational intelligence techniques: the points represent a set of examples and the surface to be reconstructed represents the law that has generated them. On the other hand, in many applications the cloud of sampled points may become available only progressively during system operation. The conventional approaches to regression are therefore not suited to deal efficiently with this operating condition. The aim of the thesis is to introduce innovative approaches to the regression problem suited for achieving high reconstruction accuracy, while limiting the computational complexity, and appropriate for online operation. Two classical computational intelligence paradigms have been considered as basic tools to address the regression problem: namely the Radial Basis Functions and the Support Vector Machines. The original and innovative aspect introduced by this thesis is the extension of these tools toward a multi-scale incremental structure, based on hierarchical schemes and suited for online operation. This allows for obtaining modular, scalable, accurate and efficient modeling procedures with training algorithms appropriate for dealing with online learning. Radial Basis Function Networks have a fast configuration procedure that, operating locally, does not require iterative algorithms. On the other side, the computational complexity of the configuration procedure of Support Vector Machines is independent from the number of input variables. These two approaches have been considered in order to analyze advantages and limits of each of them due to the differences in their intrinsic nature

Efficient image duplicate detection based on image analysis

This thesis is about the detection of duplicated images. More precisely, the developed system is able to discriminate possibly modified copies of original images from other unrelated images. The proposed method is referred to as content-based since it relies only on content analysis techniques rather than using image tagging as done in watermarking. The proposed content-based duplicate detection system classifies a test image by associating it with a label that corresponds to one of the original known images. The classification is performed in four steps. In the first step, the test image is described by using global statistics about its content. In the second step, the most likely original images are efficiently selected using a spatial indexing technique called R-Tree. The third step consists in using binary detectors to estimate the probability that the test image is a duplicate of the original images selected in the second step. Indeed, each original image known to the system is associated with an adapted binary detector, based on a support vector classifier, that estimates the probability that a test image is one of its duplicate. Finally, the fourth and last step consists in choosing the most probable original by picking that with the highest estimated probability. Comparative experiments have shown that the proposed content-based image duplicate detector greatly outperforms detectors using the same image description but based on a simpler distance functions rather than using a classification algorithm. Additional experiments are carried out so as to compare the proposed system with existing state of the art methods. Accordingly, it also outperforms the perceptual distance function method, which uses similar statistics to describe the image. While the proposed method is slightly outperformed by the key points method, it is five to ten times less complex in terms of computational requirements. Finally, note that the nature of this thesis is essentially exploratory since it is one of the first attempts to apply machine learning techniques to the relatively recent field of content-based image duplicate detection

Efficient image duplicate detection based on image analysis

This thesis is about the detection of duplicated images. More precisely, the developed system is able to discriminate possibly modified copies of original images from other unrelated images. The proposed method is referred to as content-based since it relies only on content analysis techniques rather than using image tagging as done in watermarking. The proposed content-based duplicate detection system classifies a test image by associating it with a label that corresponds to one of the original known images. The classification is performed in four steps. In the first step, the test image is described by using global statistics about its content. In the second step, the most likely original images are efficiently selected using a spatial indexing technique called R-Tree. The third step consists in using binary detectors to estimate the probability that the test image is a duplicate of the original images selected in the second step. Indeed, each original image known to the system is associated with an adapted binary detector, based on a support vector classifier, that estimates the probability that a test image is one of its duplicate. Finally, the fourth and last step consists in choosing the most probable original by picking that with the highest estimated probability. Comparative experiments have shown that the proposed content-based image duplicate detector greatly outperforms detectors using the same image description but based on a simpler distance functions rather than using a classification algorithm. Additional experiments are carried out so as to compare the proposed system with existing state of the art methods. Accordingly, it also outperforms the perceptual distance function method, which uses similar statistics to describe the image. While the proposed method is slightly outperformed by the key points method, it is five to ten times less complex in terms of computational requirements. Finally, note that the nature of this thesis is essentially exploratory since it is one of the first attempts to apply machine learning techniques to the relatively recent field of content-based image duplicate detection

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Groupwise non-rigid registration for automatic construction of appearance models of the human craniofacial complex for analysis, synthesis and simulation

Finally, a novel application of 3D appearance modelling is proposed: a faster than real-time algorithm for statistically constrained quasi-mechanical simulation. Experiments demonstrate superior realism, achieved in the proposed method by employing statistical appearance models to drive the simulation, in comparison with the comparable state-of-the-art quasi-mechanical approaches.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Groupwise non-rigid registration for automatic construction of appearance models of the human craniofacial complex for analysis, synthesis and simulation

Finally, a novel application of 3D appearance modelling is proposed: a faster than real-time algorithm for statistically constrained quasi-mechanical simulation. Experiments demonstrate superior realism, achieved in the proposed method by employing statistical appearance models to drive the simulation, in comparison with the comparable state-of-the-art quasi-mechanical approaches

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity