5 research outputs found
Saliency-aware Stereoscopic Video Retargeting
Stereo video retargeting aims to resize an image to a desired aspect ratio.
The quality of retargeted videos can be significantly impacted by the stereo
videos spatial, temporal, and disparity coherence, all of which can be impacted
by the retargeting process. Due to the lack of a publicly accessible annotated
dataset, there is little research on deep learning-based methods for stereo
video retargeting. This paper proposes an unsupervised deep learning-based
stereo video retargeting network. Our model first detects the salient objects
and shifts and warps all objects such that it minimizes the distortion of the
salient parts of the stereo frames. We use 1D convolution for shifting the
salient objects and design a stereo video Transformer to assist the retargeting
process. To train the network, we use the parallax attention mechanism to fuse
the left and right views and feed the retargeted frames to a reconstruction
module that reverses the retargeted frames to the input frames. Therefore, the
network is trained in an unsupervised manner. Extensive qualitative and
quantitative experiments and ablation studies on KITTI stereo 2012 and 2015
datasets demonstrate the efficiency of the proposed method over the existing
state-of-the-art methods. The code is available at
https://github.com/z65451/SVR/.Comment: 8 pages excluding references. CVPRW conferenc
Stereoscopic high dynamic range imaging
Two modern technologies show promise to dramatically increase immersion in
virtual environments. Stereoscopic imaging captures two images representing
the views of both eyes and allows for better depth perception. High dynamic
range (HDR) imaging accurately represents real world lighting as opposed to
traditional low dynamic range (LDR) imaging. HDR provides a better contrast
and more natural looking scenes. The combination of the two technologies in
order to gain advantages of both has been, until now, mostly unexplored due to
the current limitations in the imaging pipeline. This thesis reviews both fields,
proposes stereoscopic high dynamic range (SHDR) imaging pipeline outlining the
challenges that need to be resolved to enable SHDR and focuses on capture and
compression aspects of that pipeline.
The problems of capturing SHDR images that would potentially require two
HDR cameras and introduce ghosting, are mitigated by capturing an HDR and
LDR pair and using it to generate SHDR images. A detailed user study compared
four different methods of generating SHDR images. Results demonstrated that
one of the methods may produce images perceptually indistinguishable from the
ground truth.
Insights obtained while developing static image operators guided the design
of SHDR video techniques. Three methods for generating SHDR video from an
HDR-LDR video pair are proposed and compared to the ground truth SHDR
videos. Results showed little overall error and identified a method with the least
error.
Once captured, SHDR content needs to be efficiently compressed. Five SHDR
compression methods that are backward compatible are presented. The proposed
methods can encode SHDR content to little more than that of a traditional single
LDR image (18% larger for one method) and the backward compatibility property
encourages early adoption of the format.
The work presented in this thesis has introduced and advanced capture and
compression methods for the adoption of SHDR imaging. In general, this research
paves the way for a novel field of SHDR imaging which should lead to improved
and more realistic representation of captured scenes
Gaze-Based Human-Robot Interaction by the Brunswick Model
We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered
Naval Postgraduate School Catalog 2016
Approved for public release; distribution is unlimited
Naval Postgraduate School Catalog 2015
Approved for public release; distribution is unlimited