Object-based 2D-to-3D video conversion for effective stereoscopic content generation in 3D-TV applications

Three-dimensional television (3D-TV) has gained increasing popularity in the broadcasting domain, as it enables enhanced viewing experiences in comparison to conventional two-dimensional (2D) TV. However, its application has been constrained due to the lack of essential contents, i.e., stereoscopic videos. To alleviate such content shortage, an economical and practical solution is to reuse the huge media resources that are available in monoscopic 2D and convert them to stereoscopic 3D. Although stereoscopic video can be generated from monoscopic sequences using depth measurements extracted from cues like focus blur, motion and size, the quality of the resulting video may be poor as such measurements are usually arbitrarily defined and appear inconsistent with the real scenes. To help solve this problem, a novel method for object-based stereoscopic video generation is proposed which features i) optical-flow based occlusion reasoning in determining depth ordinal, ii) object segmentation using improved region-growing from masks of determined depth layers, and iii) a hybrid depth estimation scheme using content-based matching (inside a small library of true stereo image pairs) and depth-ordinal based regularization. Comprehensive experiments have validated the effectiveness of our proposed 2D-to-3D conversion method in generating stereoscopic videos of consistent depth measurements for 3D-TV applications

Interactive product browsing and configuration using remote augmented reality sales services

Real-time remote sales assistance is an underdeveloped component of online sales services. Solutions involving web page text chat, telephony and video support prove problematic when seeking to remotely guide customers in their sales processes, especially with configurations of physically complex artefacts. Recently, there has been great interest in the application of virtual worlds and augmented reality to create synthetic environments for remote sales of physical artefacts. However, there is a lack of analysis and development of appropriate software services to support these processes. We extend our previous work with the detailed design of configuration context services to support the management of an interactive sales session using augmented reality. We detail the context and configuration services required, presenting a novel data service streaming configuration information to the vendor for business analytics. We expect that a fully implemented configuration management service, based on our design, will improve the remote sales experience for both customers and vendors alike via analysis of the streamed information

Pointing Gesture Recognition Using Stereo Vision for Video Conferencing

Tohoku University青木孝

Characteristics of flight simulator visual systems

The physical parameters of the flight simulator visual system that characterize the system and determine its fidelity are identified and defined. The characteristics of visual simulation systems are discussed in terms of the basic categories of spatial, energy, and temporal properties corresponding to the three fundamental quantities of length, mass, and time. Each of these parameters are further addressed in relation to its effect, its appropriate units or descriptors, methods of measurement, and its use or importance to image quality

Egocentric Hand Detection Via Dynamic Region Growing

Egocentric videos, which mainly record the activities carried out by the users of the wearable cameras, have drawn much research attentions in recent years. Due to its lengthy content, a large number of ego-related applications have been developed to abstract the captured videos. As the users are accustomed to interacting with the target objects using their own hands while their hands usually appear within their visual fields during the interaction, an egocentric hand detection step is involved in tasks like gesture recognition, action recognition and social interaction understanding. In this work, we propose a dynamic region growing approach for hand region detection in egocentric videos, by jointly considering hand-related motion and egocentric cues. We first determine seed regions that most likely belong to the hand, by analyzing the motion patterns across successive frames. The hand regions can then be located by extending from the seed regions, according to the scores computed for the adjacent superpixels. These scores are derived from four egocentric cues: contrast, location, position consistency and appearance continuity. We discuss how to apply the proposed method in real-life scenarios, where multiple hands irregularly appear and disappear from the videos. Experimental results on public datasets show that the proposed method achieves superior performance compared with the state-of-the-art methods, especially in complicated scenarios

Spott : on-the-spot e-commerce for television using deep learning-based video analysis techniques

Spott is an innovative second screen mobile multimedia application which offers viewers relevant information on objects (e.g., clothing, furniture, food) they see and like on their television screens. The application enables interaction between TV audiences and brands, so producers and advertisers can offer potential consumers tailored promotions, e-shop items, and/or free samples. In line with the current views on innovation management, the technological excellence of the Spott application is coupled with iterative user involvement throughout the entire development process. This article discusses both of these aspects and how they impact each other. First, we focus on the technological building blocks that facilitate the (semi-) automatic interactive tagging process of objects in the video streams. The majority of these building blocks extensively make use of novel and state-of-the-art deep learning concepts and methodologies. We show how these deep learning based video analysis techniques facilitate video summarization, semantic keyframe clustering, and (similar) object retrieval. Secondly, we provide insights in user tests that have been performed to evaluate and optimize the application's user experience. The lessons learned from these open field tests have already been an essential input in the technology development and will further shape the future modifications to the Spott application