简介：ConventionalGaborrepresentationanditsextractedfeaturesoftenyieldafairlypoorperformanceinextractingtheinvariancefeaturesofobjects.Toaddressthisissue,aglobalGaborrepresentationmethodforraisedcharacterspressedonlabelisproposedinthispaper,wheretherepresentationonlyrequiresfewsummationsontheconventionalGaborfilterresponses.Featuresarethenextractedfromthesenewrepresentationstoconstructtheinvariantfeatures.ExperimentalresultsclearlyshowthattheobtainedglobalGaborfeaturesprovidegoodperformanceinrotation,translation,andscaleinvariance.Also,theyareinsensitivetoilluminationconditionsandnoisechanges.ItisprovedthatGaborfilterscanbereliablyusedinlow-levelfeatureextractioninimageprocessingandtheglobalGaborfeaturescanbeusedtoconstructrobustinvariantrecognitionsystem.

简介：Aninfraredimagedetailenhancementmethodbasedonlocaladaptivegammacorrection(LAGC)isproposed.ThelocaladaptivegammavaluesaredesignedbasedontheWebercurvetoenhanceeffectivelytheimagedetails.Subsequently,theactivegrayscalerangeoftheimageprocessedbyLAGCisfurtherextendedbyusingourproposedhistogramstatisticalstretching.Theexperimentalresultsshowthattheproposedalgorithmcouldconsiderablyincreasetheimagedetailsandimprovethecontrastoftheentireimage.Thus,ithassignificantpotentialforpracticalapplications.