... One goal of MPEG-7 is to provide a stan-
dardized method of describing features of multimedia data. For images and video, colors or
â2001 CRC Press LLC
5.5.2 Postprocessing
5.5.3 Shape and Color ... conversational
services, Internetvideo applications, sign language andlip-reading communication, video stor-
age and retrieval services (e.g., VOD), video store and forward services (e.g., video mail), and
multipoint ... Adali
University of Maryland, Baltimore, Maryland
Horst Bunke
Institute für Informatik und Angewandte Mathematik, Universität Bern,
Switzerland
Frank M. Candocia
University of Florida, Gainesville,...
... sim-
plifications for real-time imageandvideo processing, Hardware platforms for real-time image
andvideo processing, Software methods for real-time imageandvideo processing
P1: IML/FFX P2: ... developments in the field of multidimensional signal
processing have all led to the creation of the field of real-time imageandvideo processing.
Here, an overview of the history ofimageprocessing is stated ... a brief overview of the other chapters.
1.2 PARALLELISM IN IMAGE/ VIDEO
PROCESSING OPERATIONS
Real-time imageandvideoprocessing systems involve processing vast amounts ofimage data in
a timely...
... the
Compressed
Form
Figure 1.3 : Processing in the domain of alternative representation.
1.3 Overview of Different ImageandVideo Compression
Techniques and Standards
Various imageandvideo compression techniques ... ofvideo resizing, in particular video downsampling, is discussed in the
next chapter on transcoding.
In chapter six, transcoding of images and videos is discussed. As most of
the imageandvideo ... than their original domains of representation, which
are spatial and spatiotemporal for images and videos, respectively. Two such
popular representations of images and videos are formed by their...
... Y
AVG
is made as a product of brightness
L of the input image, Exposure (integration) Time t
ET
of
the sensor, gain G of the imageprocessing pipeline, and a
constant K,see[9], and computed with Y
AVG
= ... on ImageandVideo Processing
own peculiarities, different behavior, and effect on the image.
The task of the video- level control algorithm is to maintain
the correct average luminance value of ... describe two
basic methods: mixing of images and hard switching between
images. Figure 12(a) depicts a soft switch between long- and
short-exposure images, where two images are mixed in a
transition...
... EURASIP Journal on ImageandVideoProcessing 3
(a) (b) (c)
Figure 1: Examples of images from minimally invasive medical procedures showing specular highlights. (a) Laparoscope imageof the
appendix, ... 4 EURASIP Journal on ImageandVideo Processing
(a) (b)
Figure 3: Example of a colonoscopic image before and after median filtering.
(a) (b)
(c) (d)
Figure 4: Illustration of the area that is used ... transition between c(x)
and c
sm
(x). Figure 5 illustrates the approach by showing the
relevant images and masks.
10 EURASIP Journal on ImageandVideo Processing
Table 3: Performance of the colour channel...
... respect to the center ofimage (a); (d) rectangular axis-oriented patch on polar
image; (e) transformation of the image (d) and its patch o nto the origin al image; (f) examples of rectangular patches ... detector; final detections of (g, h) bare heads andof hard hats
(i, j).
EURASIP Journal on ImageandVideoProcessing 7
True detections (66%)
(a) (b) (c)
False detections out of scale (24%)
False ... unreliable.
Most of the other head detectors approaches that do not
rely on shapes, exploit color features (ty p ically of hair and
EURASIP Journal on ImageandVideoProcessing 3
Domain-specific
video...
... space-varying mean and stationary covariance as a model for the pdf
of the image. Geman and Geman [11] proposed a Gibbsdistribution to model thepdf ofthe image.
Alternatively, if the image is assumed ... non-stationary image mean, R
f
and R
n
are the correlation matrices of the ideal
imageandnoise,respectively,andS
b
isadiagonalmatrixconsistingofthederivativesofs(.) evaluated
c
1999 by CRC Press LLC
ImageandVideoRestoration
A.MuratTekalp
UniversityofRochester
53.1Introduction
53.2Modeling
Intra-FrameObservationModel
ã
MultispectralObserva-
tionModel
ã
MultiframeObservationModel
ã
Regularization
Models
53.3ModelParameterEstimation
BlurIdentication
ã
EstimationofRegularizationParameters
ã
EstimationoftheNoiseVariance
53.4Intra-FrameRestoration
BasicRegularizedRestorationMethods
ã
RestorationofIm-
agesRecordedbyNonlinearSensors
ã
RestorationofImages
DegradedbyRandomBlurs
ã
AdaptiveRestorationforRing-
ingReduction
ã
BlindRestoration(Deconvolution)
ã
Restora-
tionofMultispectralImages
ã
RestorationofSpace-Varying
BlurredImages
53.5MultiframeRestorationandSuperresolution
MultiframeRestoration
ã
Superresolution
ã
Superresolution
withSpace-VaryingRestoration
53.6Conclusion
References
53.1 ... the dataand a constantsum of pixel intensities. This approach requires the
solution of a system of nonlinear equations. The number of equations and unknowns are on the
order of the number of pixels...