453 research outputs found
Trans-Inpainter: Wireless Channel Information- Guided Image Restoration via Multimodal Transformer
Image inpainting is a critical computer vision task to restore missing or
damaged image regions. In this paper, we propose Trans-Inpainter, a novel
multimodal image inpainting method guided by Channel State Information (CSI)
data. Leveraging the power of transformer architectures, Trans-Inpainter
effectively extracts visual information from CSI time sequences, enabling
high-quality and realistic image inpainting. To evaluate its performance, we
compare Trans-Inpainter with RF-Inpainter, the state-of-the-art radio frequency
(RF) signal-based image inpainting technique. Through comprehensive
experiments, Trans-Inpainter consistently demonstrates superior performance in
various scenarios. Additionally, we investigate the impact of CSI data
variations on Trans-Inpainter's imaging ability, analyzing individual sensor
data, fused data from multiple sensors, and altered CSI matrix dimensions.
These insights provide valuable references for future wireless sensing and
computer vision studies
Image enhancement from a stabilised video sequence
The aim of video stabilisation is to create a new video sequence where the motions (i.e. rotations, translations) and scale differences between frames (or parts of a frame) have effectively been removed. These stabilisation effects can be obtained via digital video processing techniques which use the information extracted from the video sequence itself, with no need for additional hardware or knowledge about camera physical motion.
A video sequence usually contains a large overlap between successive frames, and regions of the same scene are sampled at different positions. In this paper, this multiple sampling is combined to achieve images with a higher spatial resolution. Higher resolution imagery play an important role in assisting in the identification of people, vehicles, structures or objects of interest captured by surveillance cameras or by video cameras used in face recognition, traffic monitoring, traffic law reinforcement, driver assistance and automatic vehicle guidance systems
STRUCTURE AND TEXTURE SYNTHESIS
An approach for filling-in blocks of missing data in wireless image transmission is presented in this paper. When compression algorithms such as JPEG are used as part of the wireless transmission process, images are first tiled into blocks of 8x8 pixels. When such images are transmitted over fading channels, the effects of noise can destroy entire blocks of the image. Instead of using common retransmission query protocols, we aim to reconstruct the lost data using correlation between the lost block and its neighbours. If the lost block contained structure, it is reconstructed using an image inpainting algorithm, while texture synthesis is used for the textured blocks. The switch between the two schemes is done in a fully automatic fashion based on the surrounding available blocks. The performance of this method is tested for various images and combinations of lost blocks
Structure Preserving Large Imagery Reconstruction
With the explosive growth of web-based cameras and mobile devices, billions
of photographs are uploaded to the internet. We can trivially collect a huge
number of photo streams for various goals, such as image clustering, 3D scene
reconstruction, and other big data applications. However, such tasks are not
easy due to the fact the retrieved photos can have large variations in their
view perspectives, resolutions, lighting, noises, and distortions.
Fur-thermore, with the occlusion of unexpected objects like people, vehicles,
it is even more challenging to find feature correspondences and reconstruct
re-alistic scenes. In this paper, we propose a structure-based image completion
algorithm for object removal that produces visually plausible content with
consistent structure and scene texture. We use an edge matching technique to
infer the potential structure of the unknown region. Driven by the estimated
structure, texture synthesis is performed automatically along the estimated
curves. We evaluate the proposed method on different types of images: from
highly structured indoor environment to natural scenes. Our experimental
results demonstrate satisfactory performance that can be potentially used for
subsequent big data processing, such as image localization, object retrieval,
and scene reconstruction. Our experiments show that this approach achieves
favorable results that outperform existing state-of-the-art techniques
Image Mapping and Object Removal Using ADM in Image Inpainting: Review
Image inpainting is a technology for restoring the damaged parts of an image by referring to the information from the undamaged parts to make the restored image look “complete”, “continuous” and “natural”. Inpainting traditionally has been done by professional restorers. For instance, in the valuable painting such as in the museum world would be carried out by a skilled art conservator or art restorer. But this process is manual so it is time consuming. Digital Image Inpainting tries to imitate this process and perform the Inpainting automatically. The aim of this work is to develop an automatic system that can remove unwanted objects from the image and restore the image in undetectable way. Among various image inpainting algorithms Alternating Direction Method (ADM) is used for image restoration. ADM works well for solving inverse problem. In this paper, various applications of ADM method for image restoration are discussed.
DOI: 10.17762/ijritcc2321-8169.15030
Video Manipulation Techniques for the Protection of Privacy in Remote Presence Systems
Systems that give control of a mobile robot to a remote user raise privacy
concerns about what the remote user can see and do through the robot. We aim to
preserve some of that privacy by manipulating the video data that the remote
user sees. Through two user studies, we explore the effectiveness of different
video manipulation techniques at providing different types of privacy. We
simultaneously examine task performance in the presence of privacy protection.
In the first study, participants were asked to watch a video captured by a
robot exploring an office environment and to complete a series of observational
tasks under differing video manipulation conditions. Our results show that
using manipulations of the video stream can lead to fewer privacy violations
for different privacy types. Through a second user study, it was demonstrated
that these privacy-protecting techniques were effective without diminishing the
task performance of the remote user.Comment: 14 pages, 8 figure
Automatic Objects Removal for Scene Completion
With the explosive growth of web-based cameras and mobile devices, billions
of photographs are uploaded to the internet. We can trivially collect a huge
number of photo streams for various goals, such as 3D scene reconstruction and
other big data applications. However, this is not an easy task due to the fact
the retrieved photos are neither aligned nor calibrated. Furthermore, with the
occlusion of unexpected foreground objects like people, vehicles, it is even
more challenging to find feature correspondences and reconstruct realistic
scenes. In this paper, we propose a structure based image completion algorithm
for object removal that produces visually plausible content with consistent
structure and scene texture. We use an edge matching technique to infer the
potential structure of the unknown region. Driven by the estimated structure,
texture synthesis is performed automatically along the estimated curves. We
evaluate the proposed method on different types of images: from highly
structured indoor environment to the natural scenes. Our experimental results
demonstrate satisfactory performance that can be potentially used for
subsequent big data processing: 3D scene reconstruction and location
recognition.Comment: 6 pages, IEEE International Conference on Computer Communications
(INFOCOM 14), Workshop on Security and Privacy in Big Data, Toronto, Canada,
201
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