16,148 research outputs found
Wavelet Based Data-Hiding of DEM in the Context of Real Time 3D Visualization
International audienceThe use of aerial photographs, satellite images, scanned maps and digital elevation models necessitates the setting up of strategies for the storage and visualization of these data in an interactive way. In order to obtain a three dimensional visualization it is necessary to map the images, called textures, onto the terrain geometry computed with Digital Elevation Model (DEM). Practically, all of these informations are stored in three different files: DEM, texture and geo-localization of the data. In this paper we propose to save all this information in a single file for the purpose of synchronization. For this, we have developed a wavelet-based embedding method for hiding the data in a color image. The texture images containing hidden DEM data can then be sent from the server to a client in order to effect 3D visualization of terrains. The embedding method is integrable with the JPEG2000 coder to accommodate compression and multi-resolution visualization
Scanning medical images for screen presentation
The purpose of this article is to remove the myths and black art surrounding
electronic imaging and prove beyond doubt that any mind competent in the workings
of the human body is capable of producing good on-screen images from adequate
equipment. Note that in this day and age of SI units imaging still uses inches to
describe diagonal screen size and pixels per inch to describe resolution.peer-reviewe
The feasibility study for electronic imaging system with the photoheliograph
The development of the electronic subsystems used for the photoheliograph and its application for a high resolution study of the sun are discussed. Basic considerations are as follows: (1) determination of characteristics of solar activity within the spectral response of the photoheliograph, (2) determination of the space vehicles capable of carrying the photoheliograph, (3) analysis of the capability of the ground based data gathering network to assimilate the generated information, and (4) the characteristics of the photoheliograph and the associated spectral filters
In the quest of vision-sensors-on-chip: Pre-processing sensors for data reduction
This paper shows that the implementation of vision systems benefits from the usage of sensing front-end chips with embedded pre-processing capabilities - called CVIS. Such embedded pre-processors reduce the number of data to be delivered for ulterior processing. This strategy, which is also adopted by natural vision systems, relaxes system-level requirements regarding data storage and communications and enables highly compact and fast vision systems. The paper includes several proof-o-concept CVIS chips with embedded pre-processing and illustrate their potential advantages. © 2017, Society for Imaging Science and Technology.Office of Naval Research (USA) N00014-14-1-0355Ministerio de Economía y Competitiviad TEC2015-66878-C3-1-R, TEC2015-66878-C3-3-RJunta de Andalucía 2012 TIC 233
Autoencoder with recurrent neural networks for video forgery detection
Video forgery detection is becoming an important issue in recent years,
because modern editing software provide powerful and easy-to-use tools to
manipulate videos. In this paper we propose to perform detection by means of
deep learning, with an architecture based on autoencoders and recurrent neural
networks. A training phase on a few pristine frames allows the autoencoder to
learn an intrinsic model of the source. Then, forged material is singled out as
anomalous, as it does not fit the learned model, and is encoded with a large
reconstruction error. Recursive networks, implemented with the long short-term
memory model, are used to exploit temporal dependencies. Preliminary results on
forged videos show the potential of this approach.Comment: Presented at IS&T Electronic Imaging: Media Watermarking, Security,
and Forensics, January 201
LiDAR-assisted Large-scale Privacy Protection in Street-view Cycloramas
Recently, privacy has a growing importance in several domains, especially in
street-view images. The conventional way to achieve this is to automatically
detect and blur sensitive information from these images. However, the
processing cost of blurring increases with the ever-growing resolution of
images. We propose a system that is cost-effective even after increasing the
resolution by a factor of 2.5. The new system utilizes depth data obtained from
LiDAR to significantly reduce the search space for detection, thereby reducing
the processing cost. Besides this, we test several detectors after reducing the
detection space and provide an alternative solution based on state-of-the-art
deep learning detectors to the existing HoG-SVM-Deep system that is faster and
has a higher performance.Comment: Accepted at Electronic Imaging 201
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