Research efforts on 3DTV technology have been strengthened worldwide recently, covering the whole media processing chain from capture to display. Different 3DTV systems rely on different 3D scene representations that integrate various types of data. Efficient coding of these data is crucial for the success of 3DTV. Compression of pixel-type data including stereo video, multiview video, and associated depth or disparity maps extends available principles of classical video coding. Powerful algorithms and open international standards for multiview video coding and coding of video plus depth data are available and under development, which will provide the basis for introduction of various 3DTV systems and services in the near future. Compression of 3D mesh models has also reached a high level of maturity. For static geometry, a variety of powerful algorithms are available to efficiently compress vertices and connectivity. Compression of dynamic 3D geometry is currently a more active field of research. Temporal prediction is an important mechanism to remove redundancy from animated 3D mesh sequences. Error resilience is important for transmission of data over error prone channels, and multiple description coding (MDC) is a suitable way to protect data. MDC of still images and 2D video has already been widely studied, whereas multiview video and 3D meshes have been addressed only recently. Intellectual property protection of 3D data by watermarking is a pioneering research area as well. The 3D watermarking methods in the literature are classified into three groups, considering the dimensions of the main components of scene representations and the resulting components after applying the algorithm. In general, 3DTV coding technology is maturating. Systems and services may enter the market in the near future. However, the research area is relatively young compared to coding of other types of media. Therefore, there is still a lot of room for improvement and new development of algorithms.

Coding Algorithms for 3DTV—A Survey

Cyber situational awareness – A systematic review of the literature

An architecture and supporting methods are presented for the implementation of a resilient condition assessment monitoring system that can adaptively accommodate both cyber and physical anomalies to a monitored system under observation. In particular, the architecture includes three layers: information, assessment, and sensor selection. The information layer estimates probability distributions of process variables based on sensor measurements and assessments of the quality of sensor data. Based on these estimates, the assessment layer then employs probabilistic reasoning methods to assess the plant health. The sensor selection layer selects sensors so that assessments of the plant condition can be made within desired time periods. Resilient features of the developed system are then illustrated by simulations of a simplified power plant model, where a large portion of the sensors are under attack.

/pdf/a-resilient-condition-assessment-monitoring-system-46d1qs9rvt.pdf

A resilient condition assessment monitoring system

Through the propagation of technology in recent years, people communicate in a range of ways via multimedia. The use of multimedia technique in healthcare system also makes it possible to store, process and transfer the patient’s data presented in variety of forms such as images, text and audio through online using various smart objects. Healthcare organizations around the world are transforming themselves into more efficient, coordinated and user-centered systems through various multimedia techniques. However, the management of huge amount data such as reports and images of every person leads to increase the human efforts and security risks. In order to overcome these issues, IoT in healthcare enhances the quality of patients care and reduce the cost by allocating the medical resources in an efficient way. However, a number of threats can occur in IoT devices initiated by various intruders. Sometimes, in order to make their personal profit, even though the medical shop or pathology labs are not of good reputation, the doctors forced the patients to do the lab tests, or buy the medicines from those organizations only. Therefore, security should be at the staple of outlook in IoT elucidations. In order to prevent these issues, Blockchain technology has been encountered as the best technique that provides the secrecy and protection of control system in real time conditions. In this manuscript, we will provide a security framework of healthcare multimedia data through blockchain technique by generating the hash of each data so that any change or alteration in data or breaching of medicines may be reflected in entire blockchain network users. The results have been analyzed against conventional approach and validated with improved simulated results that offer 86% success rate over product drop ratio, falsification attack, worm hole attack and probabilistic authentication scenarios because of Blockchain technique.

A hybrid framework for multimedia data processing in IoT-healthcare using blockchain technology

Model-Data Fusion

Recent research on computational modeling of visual attention has demonstrated that a bottom-up approach to identifying salient regions within an image can be applied to diverse and practical problems for which conventional machine vision techniques have not succeeded in producing robust solutions. This paper proposes a new method for extracting regions of interest (ROIs) from images using models of visual attention. It is presented in the context of improving content-based image retrieval (CBIR) solutions by implementing a biologically-motivated, unsupervised technique of grouping together images whose salient ROIs are perceptually similar. In this paper we focus on the process of extracting the salient regions of an image. The excellent results obtained with the proposed method have demonstrated that the ROIs of the images can be independently indexed for comparison against other regions on the basis of similarity for use in a CBIR solution.

/pdf/using-visual-attention-to-extract-regions-of-interest-in-the-52ytve0v71.pdf

Using visual attention to extract regions of interest in the context of image retrieval

A method and system for fingerprint template synthesis from multiple fingerprint images is provided. A first set of minutiae points is extracted from a first fingerprint image. A second set of minutiae points is extracted from a second fingerprint image. The orientation is calculated for a plurality of minutiae points selected from the first set of minutiae points based on the first fingerprint image. Simulated points are added to the first set of minutiae points, wherein simulated points are created based on the location and orientation of minutiae points in the plurality of minutiae points. The first set of minutiae points and the second set of minutiae points are registered and the first set of minutiae points and the second set of minutiae points are combined.

A method for fingerprint template synthesis and fingerprint mosaicing using a point matching algorithm

Recent work in the computational modeling of visual attention has demonstrated that a purely bottom-up approach to identifying salient regions within an image can be successfully applied to diverse and practical problems from target recognition to the placement of advertisement This paper proposes an application of a combination of computational models of visual attention to the image retrieval problem We demonstrate that certain shortcomings of existing content-based image retrieval solutions can be addressed by implementing a biologically motivated, unsupervised way of grouping together images whose salient regions of interest (ROIs) are perceptually similar regardless of the visual contents of other (less relevant) parts of the image We propose a model in which only the salient regions of an image are encoded as ROIs whose features are then compared against previously seen ROIs and assigned cluster membership accordingly Experimental results show that the proposed approach works well for several combinations of feature extraction techniques and clustering algorithms, suggesting a promising avenue for future improvements, such as the addition of a top-down component and the inclusion of a relevance feedback mechanism

https://link.springer.com/content/pdf/10.1155%2F2007%2F43450.pdf

An attention-driven model for grouping similar images with image retrieval applications

Biometrics are more usable than lengthy text passwords on mobile devices, but their use must be tempered against security needs until authentication can be proven secure and reliable.

Biometric Authentication on Mobile Devices

This paper explores the challenges opportunities in developing and deploying 3D TV services. The 3D TV services can be seen as a general case of the multi-view video that has been receiving significant attention lately. The keys to a successful 3D TV experience are the availability of content, the ease of use, the quality of experience, and the cost of deployment. Recent technological advances have made possible experimental systems that can be used to evaluate the 3D TV services. We have developed a 3D TV prototype and have currently conducting our first user study to evaluate the quality and experience. These experiences have allowed us to identify challenges and opportunities in developing 3D TV services.

/pdf/challenges-and-opportunities-in-video-coding-for-3d-tv-48rjzp3395.pdf

Liam M. Mayron

Papers

Using visual attention to extract regions of interest in the context of image retrieval

A method for fingerprint template synthesis and fingerprint mosaicing using a point matching algorithm

An attention-driven model for grouping similar images with image retrieval applications

Biometric Authentication on Mobile Devices

Challenges and Opportunities in Video Coding for 3D TV