▁than ▁their ▁neighb ors . ▁The ▁complexity ▁increases ▁at ▁a ▁rate ▁of ▁ ▁with ▁each ▁additional ▁parameter , ▁where ▁ ▁is ▁the ▁size ▁of ▁the ▁image ▁space ▁and ▁ ▁is ▁the ▁number ▁of ▁parameters . ▁( Sh api ro ▁and ▁Stock man , ▁ 3 1 0 ) ▁Thus , ▁the ▁H ough ▁transform ▁must ▁be ▁used ▁with ▁great ▁care ▁to ▁detect ▁anything ▁other ▁than ▁lines ▁or ▁circles . ▁ ▁Finally , ▁much ▁of ▁the ▁efficiency ▁of ▁the ▁H ough ▁transform ▁is ▁dependent ▁on ▁the ▁quality ▁of ▁the ▁input ▁data : ▁the ▁edges ▁must ▁be ▁detected ▁well ▁for ▁the ▁H ough ▁transform ▁to ▁be ▁efficient . ▁Use ▁of ▁the ▁H ough ▁transform ▁on ▁no isy ▁images ▁is ▁a ▁very ▁del icate ▁matter ▁and ▁generally , ▁a ▁den o ising ▁stage ▁must ▁be ▁used ▁before . ▁In ▁the ▁case ▁where ▁the ▁image ▁is ▁cor rupted ▁by ▁spe ck le , ▁as ▁is ▁the ▁case ▁in ▁rad ar ▁images , ▁the ▁Rad on ▁transform ▁is ▁sometimes ▁preferred ▁to ▁detect ▁lines , ▁because ▁it ▁at ten u ates ▁the ▁noise ▁through ▁summ ation . ▁ ▁See ▁also ▁ ▁General ised ▁H ough ▁transform ▁ ▁Random ized ▁H ough ▁transform ▁ ▁Rad on ▁transform ▁ ▁Fourier ▁transform ▁ ▁References ▁ ▁External ▁links ▁ ▁h ough _ transform . cpp ▁ ▁– ▁C ++ ▁code ▁– ▁example ▁of ▁C Img ▁library ▁( open ▁source ▁library , ▁ ▁C ++ ▁source ▁code , ▁Gray scale ▁images ) ▁ ▁Inter active ▁Dem on str ation ▁on ▁the ▁Bas ics ▁of