HANDBOOK OF IMAGE AND VIDEO PROCESSING BY ALAN CONRAD BOVIK

red, green and blue illumination. While this allows for anincrease in image resolution, it has the disadvantage that fastcamera motion leads to misalignment of the colour channels(Figure 1(b)). Consequently, specular highlights can appeareither white or highly saturated red, gre[r]

13.6 Spatio-Temporal SignalsDetection: Known Gains and Known Spatial Covariance•Detection: Unknown Gains andUnknown SpatialCovariance13.7 Signal ClassificationClassifying Individual Signals•Classifying Presence of Multi-ple SignalsReferences13.1 IntroductionDetection and classific[r]

sensors for 3D reconstruction. In the first paper (Yi-pingTang et al., “Design of vertically aligned binocular omnistereovision sensor”), several types of omnidirectional stereosensors are designed based on the combination of hyperbolicand regular resolution mirrors. In the secon[r]

Example of a Large PSF: Illumination Flattening
A common application requiring a large PSF is the enhancement of images with unequal illumination. Convolution by separability is an ideal algorithm to carry out this processing. With only a few exceptions, the images se[r]

transform coding, three separate prediction paths, and sideinformation in order to accommodate all possible scenari osat the decoder. For this reason, three different algorithms forredundancy allocation were implemented, and experimentalresults were presented. An improved algorithm base[r]

bands, each initially interpolated channel is subsampled intosubimages, and then the high-frequency components of theunknown values are projected onto the high-frequency com-ponents of the corresponding known values. In their workentitled “Color image coding by col[r]

(http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC) 222 Rosewood Drive, Danvers, MA01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. Fororganizations that have been granted a photocopy[r]

13.6 Spatio-Temporal SignalsDetection: Known Gains and Known Spatial Covariance•Detection: Unknown Gains andUnknown SpatialCovariance13.7 Signal ClassificationClassifying Individual Signals•Classifying Presence of Multi-ple SignalsReferences13.1 IntroductionDetection and classific[r]

larity on the basis of each region’s dominant colors, which are extracted byapplying color quantization.Some other commonly used color feature representations in image retrievalinclude color moments and color sets. For example, in Ref. [31], Stricker andDimai extracted the first[r]

13.6 Spatio-Temporal SignalsDetection: Known Gains and Known Spatial Covariance•Detection: Unknown Gains andUnknown SpatialCovariance13.7 Signal ClassificationClassifying Individual Signals•Classifying Presence of Multi-ple SignalsReferences13.1 IntroductionDetection and classific[r]

= α∞ηg(l|θH)dl = α.(13.6)Here g(l|θH) is the probability density function of the likelihood ratio statistic L(x) when θ = θH.Itmustalso bementioned thatifthedensity g(l|θH)containsdeltafunctionsasimple randomization [1]of the LRT may be required to meet the false alarm constraint (13.[r]

(which yields a single number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computers to emulate human vision, including learningand being able to make inferences and take actions[r]

and engineering. One only has to view the latest space telescope images or read about thenewest medical image modality to be aware of this.With this introduction,welcome to The Essential Guide to Image Processing! The readerwill find that this Guide covers introduct[r]

adaptation is needed (e.g., face recognition). With the adventof image datasets and benchmarks, machine learning andimage processing have recently received a lot of attention.An innovative integra tion of machine learning in imageprocessing is very likely to have a[r]

(MPEG) are the widely used techniques. No doubt, the lossy techniques normally provide greater compression ratio than the lossless ones. Digital Image Processing – Part II 25 Colour Image ProcessingLossless compression: These methods aim to retain lower compression ratios but p[r]

ment. The output image should have as good SNR as possibleand signal clipping should be avoided, or only introducedin a controllable fashion. The level control strategy shouldprovide a good solution for all types of images/video signals,including low-, medium-, and high-d[r]

mation should thus not prevent normal hearing; they shouldonly become active at the user’s request (unless an alert needsto be conveyed). The audio and haptic modalities can also beused jointly, as is the case with some of the assistive aids thatare presented below.Research aiming at d[r]

(MPEG) are the widely used techniques. No doubt, the lossy techniques normally provide greater compression ratio than the lossless ones. Digital Image Processing – Part II 25 Colour Image ProcessingLossless compression: These methods aim to retain lower compression ratios but p[r]

VNUJournalofScience,EarthSciences23(2007)194‐201194 Quantitativedistributionofgr oundwater chemicalcomponentsintheRedRiverDeltabasedonfrequencyanalysisDangMai*,NguyenThanhLanCollegeofScience,VNUReceived02July2007Abstract. Quantitative di[r]

2.2 LabVIEW For Image Processing 27the block diagram. The results of the operation are then passed back to the frontpanel indicators. A typical block diagram is shown in Fig. 2.1(b). Within the blockdiagram, you have access to a full-featured graphical programming language thati[r]