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ispartof: pages:229-237 ispartof: 2022 Sixth IEEE International Conference on Robotic Computing (IRC) pages:229-237 ispartof: 2022 Sixth IEEE International Conference on Robotic Computing (IRC) location:Italy date:5 Dec - 7 Dec 2022 status: published

We describe a novel method for video outpainting. The goal of outpainting is to fill in missing regions at the edges of video frames. Our focus lies on converting portrait (9:16) to landscape (16:9) video. In contrast, most video comple- tion research is focused on inpainting: filling a masked sec- tion within the frame based on the remaining, known pixels. Our proposed method consists of three main aspects: (1) We form a background estimation using video object seg- mentation and video inpainting methods, (2) we use optical flow to form temporal consistency, and (3) we propose im- age shifting to improve individual frame completions. Our method is able to successfully broaden the aspect ratio of a video. On most videos, we achieve realistic results. Only on videos with complex camera motion and foreground objects leaving the frame, the quality is less. In contrast to other state-of-the-art methods, our method is able to reconstruct the full frame, including unseen image parts. Moreover, it is temporally consistent. We evaluate our method on the DAVIS and YouTube-VOS datasets. The code is publicly available. ispartof: pages:687-695 ispartof: Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) pages:687-695 ispartof: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) location:New Orleans, USA date:19 Jun - 24 Jun 2022 status: published

© 2014 IEEE. While a wide range of disparity estimation algorithms exists, only few methods are available to analyze the quality of a disparity map. We present a new two-dimensional approach to disparity map evaluation using the stereo matching error and the temporal error. These measures give a good indication of stereo or multi-view performance on a 3D display. Results are illustrated on a ground truth disparity map with multiple distortions as well as on a commercially available disparity estimator using varying post-processing settings on a real stereoscopic video sequence. ispartof: 2014 International Conference on 3D Imaging, IC3D 2014 - Proceedings ispartof: International Conference on 3D Imaging (IC3D) location:BELGIUM, Liege date:9 Dec - 10 Dec 2014 status: published

It is generally understood that interpreting depth from monoscopic images inherently induces the problem of object segmentation and context recognition. A common way to deal with this complexity is to focus on one aspect (e.g. segmentation) and largely constrain or skip the other (e.g. scene classification). In our approach, we practically reconcile the two paths. We start by defining several depth profiles, providing rough estimates for both background and foreground, while reflecting common photographic and cinematic practices and rules. The idea is to assign one or more of these profiles to an image - based on the output of multiple classifiers, and apply an image-adaptive bilateral filter to align the depth with object edges. From extensive trials, we conclude that robust classification and visual performance can be achieved with this approach on a variety of content. ©2009 IEEE. ispartof: pages:2365-2368 ispartof: Proceedings - International Conference on Image Processing, ICIP pages:2365-2368 ispartof: 16th IEEE International Conference on Image Processing location:EGYPT, Cairo date:7 Nov - 10 Nov 2009 status: published

We propose an interface for creating a depth map for a 2D picture. The image and depth map can be used for 3D display on an auto-stereoscopic photo frame. Our new interface does not require the user to draw on the picture or point at an object in the picture. Instead, semantic questions are asked about a given indicated position in the picture. This semantic information is then translated automatically into a depth map. ©2009 IEEE. ispartof: pages:13-+ ispartof: 3DTV-CON 2009 - 3rd 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, Proceedings pages:13-+ ispartof: 3DTV Conference - True Vision - Capture, Transmission and Display of 3D Video location:GERMANY, Potsdam date:4 May - 6 May 2009 status: published

Super-resolution algorithms combine multiple low resolution images into a single high resolution image. They have received a lot of attention recently in various application domains such as HDTV, satellite imaging, and video surveillance. These techniques take advantage of the aliasing present in the input images to reconstruct high frequency information of the resulting image. One of the major challenges in such algorithms is a good alignment of the input images: subpixel precision is required to enable accurate reconstruction. In this paper, we give an overview of some subspace techniques that address this problem. We first formulate super-resolution in a multichannel sampling framework with unknown offsets. Then, we present three registration methods: one approach using ideas from variable projections, one using a Fourier description of the aliased signals, and one using a spline description of the sampling kernel. The performance of the algorithms is evaluated in numerical simulations. © 2008 IEEE. ispartof: pages:645-648 ispartof: Proceedings - International Conference on Image Processing, ICIP pages:645-648 ispartof: 15th IEEE International Conference on Image Processing (ICIP 2008) location:CA, San Diego date:12 Oct - 15 Oct 2008 status: published

How often have you been able to implement an algorithm as it is described in a paper? And when you did, were you confident that you had exactly the same parameter values and results as the authors of the paper? All too often, articles do not describe all the details of an algorithm and thus prohibit an implementation by someone else. In this paper, we describe our experience with reproducible research, a paradigm to allow other people to reproduce with minimal effort the results we have obtained. We discuss both the reproducibility of data and algorithms, and give examples for each of them. The effort required to make research reproducible is compensated by a higher visibility and impact of the results. © 2007 IEEE. ispartof: pages:1253-1256 ispartof: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings vol:4 pages:1253-1256 ispartof: 32nd IEEE International Conference on Acoustics, Speech and Signal Processing location:HI, Honolulu date:15 Apr - 20 Apr 2007 status: published

Aliasing artifacts in images are visually very disturbing. Therefore, most imaging devices apply a low-pass filter before sampling. This removes all aliasing from the image, but it also creates a blurred image. Actually, all the image information above half the sampling frequency is removed. In this paper, we present a new method for the reconstruction of a high resolution image from a set of highly under-sampled and thus aliased images. We use the information in the entire frequency spectrum, including the aliased part, to create a sharp, high resolution image. The unknown relative shifts between the images are computed using a subspace projection approach. We show that the projection can be decomposed into multiple projections onto smaller subspaces. This allows for a considerable reduction of the overall computational complexity of the algorithm. A high resolution image can then be reconstructed from the registered low resolution images. Simulation results show the validity of our algorithm. © 2005 IEEE. ispartof: pages:889-892 ispartof: Proceedings - International Conference on Image Processing, ICIP vol:1 pages:889-892 ispartof: IEEE International Conference on Image Processing (ICIP 2005) location:ITALY, Genoa date:11 Sep - 14 Sep 2005 status: published