The illegal distribution of a digital movie is a common and significant threat to the film industry. With the advent of high-speed broadband Internet access, a pirated copy of a digital video can now be easily distributed to a global audience. A possible means of limiting this type of digital theft is digital video watermarking whereby additional information, called a watermark, is embedded in the host video. This watermark can be extracted at the decoder and used to determine whether the video content is watermarked. This paper presents a review of digital video watermarking techniques in which their applications, challenges and important properties are discussed, and categorizes them based on the domain in which they embed the watermark. It then provides an overview of a few emerging innovative solutions using watermarks. Protecting a 3D video by watermarking is an emerging area of research. The relevant 3D video watermarking techniques in the literature are classified based on the image-based representations of a 3D video in stereoscopic, depth-image-based rendering and multi-view video watermarking. We discuss each technique and then present a survey of the literature. Finally, we provide a summary of this study and propose some future research directions.

Illegal distribution of a digital movie is a significant threat to the film industries. With the advent of high-speed broadband Internet access, a pirated copy of a digital video can be easily distributed to a global audience. Digital video watermarking is a possible means of limiting this type of digital distribution. In existing watermarking methods, the watermark is usually embedded into the luminance channel of a video frame, which affects imperceptibility. In addition, none of the existing techniques are robust to the combination of commonly used attacks, such as compression, upscaling, rotation, cropping, downscaling in resolution, frame rate conversion, and camcording. In this paper, we initially propose a basic blind digital video watermarking algorithm, where the watermark is embedded into one level of the dual-tree complex wavelet transform of the chrominance channel to provide high quality watermarked video and extracted using the same key that was used for embedding. This algorithm is robust to compression, upscaling, rotation, and cropping. An extension of this method extracts the watermark from any level(s) of the dual-tree complex wavelet transform depending on the resolution of the downscaled version of the watermarked frame rather than only from the embedding level to survive downscaling to an arbitrary resolution. Finally, the watermark of a frame is extracted from the information of that frame without using the key that was used during watermark embedding to provide robustness to temporal synchronization attacks, such as frame rate conversion. This scheme is also robust to compression, camcording, watermark estimation remodulation, temporal frame averaging, multiple watermark embedding, downscaling in resolution, and other geometric attacks, such as upscaling, rotation, and cropping.

The popularity of 3D video is increasing daily due to the availability of low-cost 3D televisions and high-speed Internet access. However, currently the contents of 3D video can be distributed illegally without any protection. For views generated using a depth-image-based rendering technique, not only the left and right views can be distributed as 3D content, but also the center, left, or right views individually as 2D content. As digital video watermarking is a possible way of protecting these views from unauthorized distribution, in this paper, we propose a digital watermarking method for depth-image-based rendered 3D video. In this method, the watermark is embedded in both of the chrominance channels of a YUV representation of the center view using the dual-tree complex wavelet transform. Then, the left and right views are generated from the watermarked center view and depth map using a depth-image based rendering technique. Finally, the watermark can be extracted from the center, left, and right views in a blind fashion without using the original unwatermarked center, left, or right views. This watermark is robust to geometric distortions, such as upscaling, rotation and cropping, downscaling to an arbitrary resolution, and the most common video distortions, including lossy compression and additive noise. Due to the approximate shift invariance characteristic of the dual-tree complex wavelet transform, the technique is robust against distortions in the left and right views generated using depth-image based rendering. The proposed method can also survive baseline distance adjustment and both 2D and 3D camcording.

Text recognition in natural scene images is a challenging problem in computer vision. Different than the optical character recognition (OCR), text recognition in natural scene images is more complex due to variations in text size, colors, fonts, orientations, complex backgrounds, occlusion, illuminations and uneven lighting conditions. In this paper, we propose a segmentation-free method based on a deep convolutional recurrent neural network to solve the problem of cursive text recognition, particularly focusing on Urdu text in natural scenes. Compared to the non-cursive scripts, Urdu text recognition is more complex due to variations in the writing styles, several shapes of the same character, connected text, ligature overlapping, stretched, diagonal and condensed text. The proposed model gets a whole word image as an input without pre-segmenting into individual characters, and then transforms into the sequence of the relevant features. Our model is based on three components: a deep convolutional neural network (CNN) with shortcut connections to extract and encode the features, a recurrent neural network (RNN) to decode the convolutional features, and a connectionist temporal classification (CTC) to map the predicted sequences into the target labels. To increase the text recognition accuracy further, we explore deeper CNN architectures like VGG-16, VGG-19, ResNet-18 and ResNet-34 to extract more appropriate Urdu text features, and compare the recognition results. To conduct the experiments, a new large-scale benchmark dataset of cropped Urdu word images in natural scenes is developed. The experimental results show that the proposed deep CRNN network with shortcut connections outperform than other network architectures. The dataset is publicly available and can be downloaded from https://data.mendeley.com/datasets/k5fz57zd9z/1 .

A structural health monitoring (SHM) system is an approach for identifying the damages caused to various kinds of structures using different system functions and providing the necessary feedback about structure’s conditions. As civil structures are the backbone of our society, to determine its daily operations is a very important issue. The performance measurement of those structures is manual whereas a computer-based monitoring system could automatically assess the structural damages and identify its exact location. Recently, wireless sensor networks (WSNs) have attracted a great deal of attention for remote sensing applications due to flexibility to measure of various activity of large scale network. Since technology is advancing day by day, the overall cost of a monitoring system is also decreased. However, the major challenging fact of a WSNs is to provide scalability for covering a large area. The main question is arisen how much capable have of a monitoring system to turn off unnecessary nodes to save energy while there are no events detected. To support the scalability required of an existing network and save the node energy for future use, we propose a topology maintenance protocol integrated with construction to address the issue of a node’s energy consumption by placing it optimally and extending the monitoring system’s lifetime. As per the authors’ acknowledgement that, a little attention has been paid to developing such a hybrid approach. To mitigate node energy consumption issue with large scale support, an Internet of Things (IoT)-based maintenance approach is the best candidate for obtaining better system lifetime responses. Therefore, the main goal of this work is to develop an ‘on-the-fly’-based topology maintenance monitoring system, which can maintain a network’s infrastructure while gathering a node’s information to switch its state regularly when the present network is no longer optimal.

In this review, we discuss state-of-the-art developments in 3D models for small and rigid structures. This includes the pros and cons of cutting-edge range cameras used as active 3D scanners, while also considering passive image reconstruction schemes by means of the well-known structure-from-motion (SfM) algorithms. Furthermore, we discuss the issue of how data fusion algorithms can be used to optimally fuse 2D contour information onto 3D models for several different applications. Considering the benefits of 3D range sensors, we also review current trends in optimum data fusion of point clouds from 3D range sensors. We present the benefits and the limitations of each algorithm against various design considerations. To highlight the pros and cons, we also perform a comparative study of the performance of a 3D range sensor, represented by an iPad structure sensor, with respect to the well-known SfM software packages, namely, Bundler, Microsoft PhotoSynth, Agisoft PhotoScan, and Smart3DCapture. Last, we highlight several research opportunities and potential research challenges associated with each technique.