169,949 research outputs found
Efficient depth image compression using accurate depth discontinuity detection and prediction
This paper presents a novel depth image compression algorithm for both 3D Television (3DTV) and Free Viewpoint Television (FVTV) services. The proposed scheme adopts the K-means clustering algorithm to segment the depth image into K segments. The resulting segmented image is losslessly compressed and transmitted to the decoder. The depth image is then compressed using a bi-modal block encoder, where the smooth blocks are predicted using direct spatial prediction. On the other hand, blocks containing depth discontinuities are approximated using a novel depth discontinuity predictor. The residual information is then compressed using a lossy compression strategy and transmitted to the receiver. Simulation results indicate that the proposed scheme outperforms the state of the art spatial video coding systems available today such as JPEG and H.264/AVC Intra. Moreover, the proposed scheme manages to outperform specialized depth image compression algorithms such as the one proposed by Zanuttigh and Cortelazzo.peer-reviewe
EEG Signal Processing and Classification for the Novel Tactile-Force Brain-Computer Interface Paradigm
The presented study explores the extent to which tactile-force stimulus
delivered to a hand holding a joystick can serve as a platform for a brain
computer interface (BCI). The four pressure directions are used to evoke
tactile brain potential responses, thus defining a tactile-force brain computer
interface (tfBCI). We present brain signal processing and classification
procedures leading to successful interfacing results. Experimental results with
seven subjects performing online BCI experiments provide a validation of the
hand location tfBCI paradigm, while the feasibility of the concept is
illuminated through remarkable information-transfer rates.Comment: 6 pages (in conference proceedings original version); 6 figures,
submitted to The 9th International Conference on Signal Image Technology &
Internet Based Systems, December 2-5, 2013, Kyoto, Japan; to be available at
IEEE Xplore; IEEE Copyright 201
Identifying a Criminal's Network of Trust
Tracing criminal ties and mining evidence from a large network to begin a
crime case analysis has been difficult for criminal investigators due to large
numbers of nodes and their complex relationships. In this paper, trust networks
using blind carbon copy (BCC) emails were formed. We show that our new shortest
paths network search algorithm combining shortest paths and network centrality
measures can isolate and identify criminals' connections within a trust
network. A group of BCC emails out of 1,887,305 Enron email transactions were
isolated for this purpose. The algorithm uses two central nodes, most
influential and middle man, to extract a shortest paths trust network.Comment: 2014 Tenth International Conference on Signal-Image Technology &
Internet-Based Systems (Presented at Third International Workshop on Complex
Networks and their Applications,SITIS 2014, Marrakesh, Morocco, 23-27,
November 2014
Consensus as a Nash Equilibrium of a Dynamic Game
Consensus formation in a social network is modeled by a dynamic game of a
prescribed duration played by members of the network. Each member independently
minimizes a cost function that represents his/her motive. An integral cost
function penalizes a member's differences of opinion from the others as well as
from his/her own initial opinion, weighted by influence and stubbornness
parameters. Each member uses its rate of change of opinion as a control input.
This defines a dynamic non-cooperative game that turns out to have a unique
Nash equilibrium. Analytic explicit expressions are derived for the opinion
trajectory of each member for two representative cases obtained by suitable
assumptions on the graph topology of the network. These trajectories are then
examined under different assumptions on the relative sizes of the influence and
stubbornness parameters that appear in the cost functions.Comment: 7 pages, 9 figure, Pre-print from the Proceedings of the 12th
International Conference on Signal Image Technology and Internet-based
Systems (SITIS), 201
Foreground-Background Segmentation Based on Codebook and Edge Detector
Background modeling techniques are used for moving object detection in video.
Many algorithms exist in the field of object detection with different purposes.
In this paper, we propose an improvement of moving object detection based on
codebook segmentation. We associate the original codebook algorithm with an
edge detection algorithm. Our goal is to prove the efficiency of using an edge
detection algorithm with a background modeling algorithm. Throughout our study,
we compared the quality of the moving object detection when codebook
segmentation algorithm is associated with some standard edge detectors. In each
case, we use frame-based metrics for the evaluation of the detection. The
different results are presented and analyzed.Comment: to appear in the 10th International Conference on Signal Image
Technology & Internet Based Systems, 201
Role of CMOS Image Sensors based Surveillance Systems in Demanding Fields
Our research currently focusing on image sensors predominantly the sensors implemented using CMOS (Complementary Metal Oxide Semiconductor) technology. Recent technology advances in CMOS image sensors (CIS) enable their utilization in the most demanding surveillance fields, especially visual surveillance and intrusion detection in intelligent surveillance systems, aerial surveillance in war zones, Earth environmental surveillance by the satellites in space monitoring, agricultural monitoring using wireless sensor networks and internet of things and driver assistance in automotive fields. We present an overview of CMOS image sensor-based surveillance applications over the last decade by tabulating the design characteristics related to image quality such as resolution, frame rate, dynamic range, signal-to-noise ratio, and also processing technology. Year wise usage of CIS models are represented
CMOS Image Sensors in Surveillance System Applications
Recent technology advances in CMOS image sensors (CIS) enable their utilization in the most demanding of surveillance fields, especially visual surveillance and intrusion detection in intelligent surveillance systems, aerial surveillance in war zones, Earth environmental surveillance by satellites in space monitoring, agricultural monitoring using wireless sensor networks and internet of things and driver assistance in automotive fields. This paper presents an overview of CMOS image sensor-based surveillance applications over the last decade by tabulating the design characteristics related to image quality such as resolution, frame rate, dynamic range, signal-to-noise ratio, and also processing technology. Different models of CMOS image sensors used in all applications have been surveyed and tabulated for every year and application.https://doi.org/10.3390/s2102048
Map online system using internet-based image catalogue
Digital maps carry along its geodata information such as coordinate that is important in one particular topographic and thematic map. These geodatas are meaningful especially in military field. Since the maps carry along this information, its makes the size of the images is too big. The bigger size, the bigger storage is required to allocate the image file. It also can cause longer loading time. These conditions make it did not suitable to be applied in image catalogue approach via internet environment. With compression techniques, the image size can be reduced and the quality of the image is still guaranteed without much changes. This report is paying attention to one of the image compression technique using wavelet technology. Wavelet technology is much batter than any other image compression technique nowadays. As a result, the compressed images applied to a system called Map Online that used Internet-based Image Catalogue approach. This system allowed user to buy map online. User also can download the maps that had been bought besides using the searching the map. Map searching is based on several meaningful keywords. As a result, this system is expected to be used by Jabatan Ukur dan Pemetaan Malaysia (JUPEM) in order to make the organization vision is implemented
Learning from Profession Knowledge: Application on Knitting
Knowledge Management is a global process in companies. It includes all the
processes that allow capitalization, sharing and evolution of the Knowledge
Capital of the firm, generally recognized as a critical resource of the
organization. Several approaches have been defined to capitalize knowledge but
few of them study how to learn from this knowledge. We present in this paper an
approach that helps to enhance learning from profession knowledge in an
organisation. We apply our approach on knitting industry
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