KEYWORDS: Video, Surgery, Motion analysis, Simulation of CCA and DLA aggregates, Motion models, Cameras, Sensors, Visualization, Canonical correlation analysis, Visual process modeling
Analysis ofmotionexpertiseisanimportantprobleminmanydomainsincludingsportsandsurgery.Inrecent
years,surgicalsimulationhasemergedattheforefrontofnewtechnologiesforimprovingtheeducationand
training ofsurgicalresidents.Insimulation-basedsurgicaltraining,akeytaskistoratetheperformanceofthe
operators,whichisdonecurrentlybyseniorsurgeons.Thispaperintroducesanovelsolutiontothisproblem
through employingvision-basedtechniques.Wedevelopanautomatic,video-basedapproachtoanalyzingthe
motion skillsofasurgeoninsimulation-basedsurgicaltraining,whereasurgicalactioniscapturedbymultiple
video cameraswithlittleornocalibration,resultinginmultiplevideostreamsofheterogeneousproperties.
Typicalmultiple-viewvisiontechniquesareinadequateforprocessingsuchdata.Weproposeanovelapproach
that employsbothcanonicalcorrelationanalysis(CCA)andthebag-of-wordsmodeltoclassifytheexpertise
levelofthesubjectbasedontheheterogeneousvideostreamscapturingboththemotionofthesubject'shands
and theresultantmotionofthetools.Experimentsweredesignedandperformedtovalidatetheproposed
approachusingrealisticdatacapturedfromresidentsurgeonsinlocalhospitals.Theresultssuggestthatthe
proposedapproachmayprovideapromisingpracticalsolutiontotherealworldproblemevaluatingmotionskills
in simulation-basedsurgicaltraining.
We propose a method for defect detection based on taking the sign information of Walsh Hadamard Transform (WHT) coefficients. The core of the proposed algorithm involves only three steps that can all be implemented very efficiently: applying the forward WHT, taking the sign of the transform coefficients, and taking an inverse WHT using only the sign information. Our implementation takes only 7 milliseconds for a 512 × 512 image on a PC platform. As a result, the proposed method is more efficient than the PHase Only Transform (PHOT) method and other methods in literature. In addition, the proposed approach is capable of detecting defects of varying shapes, by combining the 2-dimensional WHT and 1-dimensional WHT; and can detect defects in images with strong object boundaries by utilizing a reference image. The proposed algorithm is robust over different background image patterns and varying illumination conditions. We evaluated the proposed method both visually and quantitatively and obtained good results on images from various defect detection applications.
Optical and structural properties of InAs/InAsSb type-II superlattices (T2SL) and their feasibility for mid- and longwavelength
infrared (MWIR and LWIR) photodetector applications are investigated. The InAs/InAsSb T2SL structures
with a broad bandgap range covering 4 μm to 12 μm are grown by molecular beam epitaxy and characterized by highresolution
x-ray diffraction and photoluminescence (PL) spectroscopy. All of the samples have excellent structural
properties and strong PL signal intensities of the same order of magnitude, indicating that non-radiative recombination is
not dominant and the material system is promising for high performance MWIR and LWIR detectors and multiband
FPAs.
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