Scalable Video Watermarking

In recent times, enormous advancement in communication as well as hardware technologies makes the video communication very popular. With the increasing diversity among the end using media players and its associated network bandwidth, the requirement of video streams with respect to quality, resolution, frame rate becomes more heterogeneous. This increasing heterogeneity make the scalable adaptation of the video stream in the receiver end, a real problem. Scalable video coding (SVC) has been introduced as a countermeasure of this practical problem where the main video stream is designed in such a hierarchical fashion that a set of independent bit streams can be produced as per requirement of different end using devices. SVC becomes very popular in recent time and consequently, efficient and secure transmission of scalable video stream becomes a requirement. Watermarking is being considered as an efficient DRM tool for almost a decade. Although video watermarking is regarded as a well focused research domain, a very less attention has been paid on the scalable watermarking in recent times. In this book chapter, a comprehensive survey on the scalable video watermarking has been done. The main objective of this survey work is to analyse the robustness of the different existing video watermarking scheme against scalable video adaptation and try to define the research problems for the same. Firstly, few existing scalable image watermarking schemes are discussed to understand the advantages and limitations of the direct extension of such scheme for frame by frame video watermarking. Similarly few video watermarking and some recent scalable video watermarking are also narrated by specifying their pros and cons. Finally, a summary of this survey is presented by pointing out the possible countermeasure of the existing problems.

Visual Attention Guided Object Detection and Tracking

Scene understanding and object recognition heavily depend on the success of visual attention guided salient region detection in images and videos. Therefore, summarizing computer vision techniques that take the help of visual attention models to accomplish video object recognition and tracking, can be helpful to the researchers of computer vision community. In this chapter, it is aimed to present a philosophical overview of the possible applications of visual attention models in the context of object recognition and tracking. At the beginning of this chapter, a brief introduction to various visual saliency models suitable for object recognition is presented, that is followed by discussions on possible applications of attention models on video object tracking. The chapter also provides a commentary on the existing techniques available on this domain and discusses some of their possible extensions. It is believed that, prospective readers will benefit since the chapter comprehensively guides a reader to understand the pros and cons of this particular topic.

Digital Image Watermarking Based on Fractal Image Coding

This chapter proposes a watermarking technique using Ridgelet and Discrete Wavelet Transform (DWT) techniques. A wavelet transform is the wavelet function representation. A wavelet is a mathematical function which divides a continuous time signal into different scale components, where each scale components is assigned with a frequency range. Wavelets represent objects with point singularities, while ridgelets represents objects with line singularities. The Ridgelet transform Technique is a multi-scale representation for functions on continuous spaces that are smooth away from discontinuities along lines. The proposed technique applies Ridgelet transform on the cover image to obtain ridgelet coefficients. These coefficients are transformed by using 2-level DWT to get low frequency sub-bands – LL1 and LL2. The mutual similarities between LL1 and LL2 sub-bands are considered for embedding watermark. The obtained watermarked image has better quality when compared to a few exiting methods.

A Survey on Palmprint-Based Biometric Recognition System

The automatic use of physiological or behavioral characteristics to determine or verify identity of individual's is regarded as biometrics. Fingerprints, Iris, Voice, Face, and palmprints are considered as physiological biometrics whereas voice and signature are behavioral biometrics. Palmprint recognition is one of the popular methods which have been investigated over last fifteen years. Palmprint have very large internal surface and contain several unique stable characteristic features used to identify individuals. Several palmprint recognition methods have been extensively studied. This chapter is an attempt to review current palmprint research, describing image acquisition, preprocessing palmprint feature extraction and matching, palmprint related fusion and techniques used for real time palmprint identification in large databases. Various palmprint recognition methods are compared.

Passive Video Tampering Detection Using Noise Features

With increasing availability of low-cost video editing softwares and tools, the authenticity of digital video can no longer be trusted. Active video tampering detection technique utilize digital signature or digital watermark for the video tampering detection, but when the videos do not include such signature then it is very challenging to detect tampering in such video. To detect tampering in such video, passive video tampering detection techniques are required. In this chapter we have explained passive video tampering detection by using noise features. When video is captured with camera it passes through a Camera processing pipeline and this introduces noise in the video. Noise changes abruptly from authentic to forged frame blocks and provides a clue for video tampering detection. For extracting the noise we have considered different techniques like denoising algorithms, wavelet based denoising filter, and neighbor prediction.

Study of Loss of Alertness and Driver Fatigue Using Visibility Graph Synchronization

Electroencephalogram (EEG) is widely used to predict performance degradation of human subjects due to mental or physical fatigue. Lack of sleep or insufficient quality or quantity of sleep is one of the major reasons of fatigue. Analysis of fatigue due to sleep deprivation using EEG synchronization is a promising field of research. The present chapter analyses advancing levels of fatigue in human drivers in a sleep-deprivation experiment by studying the synchronization between EEG data. A Visibility Graph Similarity-based method has been employed to quantify the synchronization, which has been formulated in terms of a complex network. The change in the parameters of the network has been analyzed to find the variation of connectivity between brain areas and hence to trace the increase in fatigue levels of the subjects. The parameters of the brain network have been compared with those of a complex network with a random degree of connectivity to establish the small-world nature of the brain network.

Content-Aware Image Retargeting

Displaying a large image in a small screen of a handheld gadget is a challenging task. Simple down-scaling of the image may reduce some objects too small to be perceptible. This gives rise to content-aware retargeting of the image. Important contents are allotted more screen space as compared to relatively less important contents of the image. Various types of content-aware image retargeting approaches have been proposed in a span of just over a decade. Another challenging area is to estimate importance of importance of the contents. Lot of researches has been carried out in this direction too to identify the important contents in the context of image retargeting. Equally important aspect is evaluation of these retargeting methods. This article contains a brief survey of related research in all of these aspects.

Biologically-Inspired Models for Attentive Robot Vision

A rich stream of visual data enters the cameras of a typical artificial vision system (e.g., a robot) and considering the fact that processing this volume of data in real-rime is almost impossible, a clever mechanism is required to reduce the amount of trivial visual data. Visual Attention might be the solution. The idea is to control the information flow and thus to improve vision by focusing the resources merely on some special aspects instead of the whole visual scene. However, does attention only speed-up processing or can the understanding of human visual attention provide additional guidance for robot vision research? In this chapter, first, some basic concepts of the primate visual system and visual attention are introduced. Afterward, a new taxonomy of biologically-inspired models of attention, particularly those that are used in robotics applications (e.g., in object detection and recognition) is given and finally, future research trends in modelling of visual attention and its applications are highlighted.

2D and 3D Visual Attention for Computer Vision

Visual attention is one of the most important mechanisms deployed in the human visual system (HVS) to reduce the amount of information that our brain needs to process. An increasing amount of efforts has been dedicated to the study of visual attention, and this chapter proposes to clarify the advances achieved in computational modeling of visual attention. First the concepts of visual attention, including the links between visual salience and visual importance, are detailed. The main characteristics of the HVS involved in the process of visual perception are also explained. Next we focus on eye-tracking, because of its role in the evaluation of the performance of the models. A complete state of the art in computational modeling of visual attention is then presented. The research works that extend some visual attention models to 3D by taking into account of the impact of depth perception are finally explained and compared.

Facial Expression Analysis Using 3D Range Images

This proposed work deals with the uses and techniques of 3D range images for facial expression recognition. A 3D range image is basically a depth image (also called a 2.5D image), which contains depth information at each (x, y) pixel of the image. In the future, computer vision will become a part of our everyday life because of all of its extensive applications. Hence, the interactions between users and computers need to be more natural, and emphasizing as well as enumerating human-to-human communication to a larger extent. That is the reason why facial expressions find importance. Facial expression is an important factor of communication, and they reveal unknown facts about a person's feelings and emotions. There comes the need of a real facial expression detection system. Also, changes in expression are of great importance for the interpretation of human facial behavior as well as face recognition.