CAMERA-SHAKE, IMAGE DEBLURRING, EXPECTATION-MAXIMIZATION ALGORITHM

justs acceleration parameter to maximize the log-likelihoodfunction at each iteration and uses the Newton-Raphson it-eration to find its new value. It speeds up the conventionalLR method by a factor of 2∼ 5, but requires a prior limiton acceleration parameter to prevent the divergence. In thesteepest[r]

1999 by CRC Press LLCfilter and other LMSE (least mean square error) techniques, on the other hand, would require theknowledge of the blur and noise level.In the MLE, the likelihood function is the pdf evaluated at an observed data sample conditionedon the parameters of interest, e.g., blur filter coe[r]

it would be computationally very costly to directly predict the array ofcamera pixel intensities from a sequence of arrays of previous pixelintensities. It seems much more sensible to attempt to infer the true state ofthe balloon (its position, velocity, and orientation) and decouple theprocess that[r]

1999 by CRC Press LLCfilter and other LMSE (least mean square error) techniques, on the other hand, would require theknowledge of the blur and noise level.In the MLE, the likelihood function is the pdf evaluated at an observed data sample conditionedon the parameters of interest, e.g., blur filter coe[r]

1. INTRODUCTIONBrain-computer interface (BCI) is a communication systemin which the information sent to the external world doesnot pass through the brain’s normal output pathways. It pro-vides a radically new communication option to people withneuromuscular impairments. In the past decade or so, re-[r]

- to get rid of someone or something that is bothering you I was able to shake off my cold after a weekend of resting in bed. shake (someone) down - to get money by threatening someone The gangsters shook the small shop owners down to get some money. shake up (someone) - to sh[r]

object of interest. The tracker follows the supervised object over the camera network, migrating to the camera which observesthe object. Thus, no central coordination is required resulting in an autonomous and scalable tracking approach. We have fullyimplemented this novel multicamera[r]

The algorithm is applied for embedding watermarks like patient identification /source identification or doctors signature in binary image format into host digital radiological image for [r]

clustering methods train themselves using the available data [1,2,12,13].MRF is a statistical model that can be used within segmentation methods.For example, MRFs are often incorporated into clustering segmentation algo-rithms such as the K-means algorithm under a Bayesian prior model. MRFsmo[r]

clustering methods train themselves using the available data [1,2,12,13].MRF is a statistical model that can be used within segmentation methods.For example, MRFs are often incorporated into clustering segmentation algo-rithms such as the K-means algorithm under a Bayesian prior model. MRFsmo[r]

it would be computationally very costly to directly predict the array ofcamera pixel intensities from a sequence of arrays of previous pixelintensities. It seems much more sensible to attempt to infer the true state ofthe balloon (its position, velocity, and orientation) and decouple theprocess that[r]

sequences totally reduce size by 75% at least. Furthermore,the proposed image compressor has lower area and loweroperation frequency according to the comparison results. Itcan fit into the existing designs.AcknowledgmentsThis work was supported in part by Chung-Shan Instituteof Science and Tec[r]

artifacts is discussed in following two articles. In “A study on the impact of AL-FEC techniques on TV over IP Quality of Experience”, by F. Battisti, M. Carli, E. Mammi, and A. Neri, the evaluation of the effectiveness of Application Layer-Forward Error Correction (AL-FEC) scheme in video communica[r]

spatially correlated noise, the noise in original images or residual noise and distortions in filtering images are practically invisible. In the second article entitled “No-reference image quality metric based on image classification”, by H. Choi and C. Lee, the authors present a new n[r]

Hindawi Publishing CorporationEURASIP Journal on Advances in Signal ProcessingVolume 2007, Article ID 60696, 10 pagesdoi:10.1155/2007/60696Research ArticleCalibrating Distributed Camera Networks UsingBelief PropagationDhanya Devarajan and Richard J. RadkeDepartment of Electrical, Computer, an[r]

image and the ability of the scheme to detect changes. An-other drawback of these schemes is the high computationcost during the feature extrac tion and visual hash codingprocesses.Further ways to completely thwart many existing fragilewatermarking schemes are the “cutting and pasting” attack[r]

down to Camera Data and uncheck the following: Software, Date Time, Image Description, and File Source. Figure 3-19. Metadata Preferences. 3. Click OK. 3.5.3. Adding IPTC Core Metadata You can manually add or change the metadata in an image at any time, so long as the metadata i[r]

from broad light sources is used to reduce all shadows. Backgrounds should be flooded with light and overexposed to reduce detail. Increasing exposure beyond ‘correct’ by up to two stops will ensure all tones are predominantly light. Hard edges and fine detail can be reduced by the use of a soft focus[r]

of the sinusoidal patterns. Recently, Lei & Zhang (2009) proposed a promising technologythat realized a sinusoidal phase-shifting algorithm using binary patterns through projectordefocusing. This technique may lead a breakthrough in this field because switching binarystructured images[r]

Sutheebanjard and Premchaiswadi EURASIP Journal on Image and Video Processing 2011, 2011:14http://jivp.eurasipjournals.com/content/2011/1/14© 2011 Sutheebanjard and Premchaiswadi; licensee Springer. This is an Open Access article distributed under the terms of the CreativeCommons Attribution[r]