Do Users Behave Similarly in VR? Investigation of the User Influence on the System Design

With the overarching goal of developing user-centric Virtual Reality (VR) systems, a new wave of studies focused on understanding how users interact in VR environments has recently emerged. Despite the intense efforts, however, current literature still does not provide the right framework to fully interpret and predict users’ trajectories while navigating in VR scenes. This work advances the state-of-the-art on both the study of users’ behaviour in VR and the user-centric system design. In more detail, we complement current datasets by presenting a publicly available dataset that provides navigation trajectories acquired for heterogeneous omnidirectional videos and different viewing platforms—namely, head-mounted display, tablet, and laptop. We then present an exhaustive analysis on the collected data to better understand navigation in VR across users, content, and, for the first time, across viewing platforms. The novelty lies in the user-affinity metric, proposed in this work to investigate users’ similarities when navigating within the content. The analysis reveals useful insights on the effect of device and content on the navigation, which could be precious considerations from the system design perspective. As a case study of the importance of studying users’ behaviour when designing VR systems, we finally propose a user-centric server optimisation. We formulate an integer linear program that seeks the best stored set of omnidirectional content that minimises encoding and storage cost while maximising the user’s experience. This is posed while taking into account network dynamics, type of video content, and also user population interactivity. Experimental results prove that our solution outperforms common company recommendations in terms of experienced quality but also in terms of encoding and storage, achieving a savings up to 70%. More importantly, we highlight a strong correlation between the storage cost and the user-affinity metric, showing the impact of the latter in the system architecture design

On the effect of age perception biases for real age regression

Automatic age estimation from facial images represents an important task in computer vision. This paper analyses the effect of gender, age, ethnic, makeup and expression attributes of faces as sources of bias to improve deep apparent age prediction. Following recent works where it is shown that apparent age labels benefit real age estimation, rather than direct real to real age regression, our main contribution is the integration, in an end-to-end architecture, of face attributes for apparent age prediction with an additional loss for real age regression. Experimental results on the APPA-REAL dataset indicate the proposed network successfully take advantage of the adopted attributes to improve both apparent and real age estimation. Our model outperformed a state-of-the-art architecture proposed to separately address apparent and real age regression. Finally, we present preliminary results and discussion of a proof of concept application using the proposed model to regress the apparent age of an individual based on the gender of an external observer.Comment: Accepted in the 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019

Dynamic adaptive 3D multi-view video streaming over the internet

Increasing throughput rates and technical developments in video streaming over the Internet offer an attractive solution for the distribution of immersive 3D multi-view. Nevertheless, robustness of video streaming is subject to its utilisation of efficient error resiliency and content aware adaptation techniques. Dynamic network characteristics resulting in frequent congestions may prevent video packets from being delivered in a timely manner. Packet delivery failures may become prominent, degrading 3D immersive video experience significantly. In order to overcome this problem, a novel view recovery technique for 3D free-viewpoint video is introduced to maintain 3D video quality in a cost-effective manner. In this concept, the undelivered (discarded) views as a result of adaptation in the network are recovered with high quality at the receiver side, using Side Information (SI) and the delivered frames of neighbouring views. The proposed adaptive 3D multi-view video streaming scheme is tested using Dynamic Adaptive Streaming over HTTP (MPEG-DASH) standard. Tests using the proposed adaptive technique have revealed that the perceptual 3D video quality under adverse network conditions is significantly improved thanks to the utilisation of the extra side information in view recovery. © 2013 ACM

Dynamic adaptive 3D multi-view video streaming over the internet

Increasing throughput rates and technical developments in video streaming over the Internet offer an attractive solution for the distribution of immersive 3D multi-view. Nevertheless, robustness of video streaming is subject to its utilisation of efficient error resiliency and content aware adaptation techniques. Dynamic network characteristics resulting in frequent congestions may prevent video packets from being delivered in a timely manner. Packet delivery failures may become prominent, degrading 3D immersive video experience significantly. In order to overcome this problem, a novel view recovery technique for 3D free-viewpoint video is introduced to maintain 3D video quality in a cost-effective manner. In this concept, the undelivered (discarded) views as a result of adaptation in the network are recovered with high quality at the receiver side, using Side Information (SI) and the delivered frames of neighbouring views. The proposed adaptive 3D multi-view video streaming scheme is tested using Dynamic Adaptive Streaming over HTTP (MPEG-DASH) standard. Tests using the proposed adaptive technique have revealed that the perceptual 3D video quality under adverse network conditions is significantly improved thanks to the utilisation of the extra side information in view recovery. © 2013 ACM

Medical image illumination enhancement and sharpening by using stationary wavelet transform [Kalici Dalgacik Dönüşümü Kullanarak Tibbi Imge Aydinlatma Pekiştirme ve Netleşme]

24th Signal Processing and Communication Application Conference, SIU 2016 -- 16 May 2016 through 19 May 2016 -- -- 122605Medical images captured by various devices have different illumination states based on chemicals used by patient prior to scanning. Consider a MRI image which has low contrast or is too bright, hence the experts cannot analysis that image due to poor representation of data in the image. In this paper we are proposing new medical image illumination enhancement and sharpening technique based on stationary wavelet transform which is addressing the aforementioned problem. The technique decomposes the input medical image into the four frequency subbands by using stationary wavelet transformation and enhances the illumination of the low-low subband image, and then it enhanced edges of image by adding the high frequency subbands to the image. The technique is compared with the conventional and state-of-art image illumination enhancement techniques such as histogram equalisation, local histogram equalisation, singular value equalisation, and discrete wavelet transform followed by singular value decomposition contrast enhancement techniques. The experimental results are showing the superiority of the proposed method over the conventional and the state-of-art techniques. © 2016 IEEE

Selective Conservative Management of Penetrating Hollow Viscus Injuries : a Report of Three Cases

In this manuscript, we report three cases of penetrating abdominal injury : one with pellet injury, one with pellet injury after a bomb explosion and one with gunshot injury. All three patients were successfully managed non-operatively