▁it ▁possible ▁to ▁perform ▁group ings ▁of ▁edge ▁points ▁into ▁object ▁candidates ▁by ▁performing ▁an ▁explicit ▁voting ▁procedure ▁over ▁a ▁set ▁of ▁parameter ized ▁image ▁objects ▁( Sh api ro ▁and ▁Stock man , ▁ 3 0 4 ). ▁ ▁The ▁simplest ▁case ▁of ▁H ough ▁transform ▁is ▁detect ing ▁straight ▁lines . ▁In ▁general , ▁the ▁straight ▁line ▁y   =   mx   +   b ▁can ▁be ▁represented ▁as ▁a ▁point ▁( b , ▁m ) ▁in ▁the ▁parameter ▁space . ▁However , ▁vertical ▁lines ▁pose ▁a ▁problem . ▁They ▁would ▁give ▁rise ▁to ▁un bounded ▁values ▁of ▁the ▁slope ▁parameter ▁m . ▁Thus , ▁for ▁computational ▁reasons , ▁D uda ▁and ▁Hart ▁proposed ▁the ▁use ▁of ▁the ▁H esse ▁normal ▁form ▁▁ ▁, ▁ ▁where ▁ ▁is ▁the ▁distance ▁from ▁the ▁origin ▁to ▁the ▁closest ▁point ▁on ▁the ▁straight ▁line , ▁and ▁ ▁( theta ) ▁is ▁the ▁angle ▁between ▁the ▁ ▁axis ▁and ▁the ▁line ▁connecting ▁the ▁origin ▁with ▁that ▁closest ▁point . ▁ ▁It ▁is ▁therefore ▁possible ▁to ▁associate ▁with ▁each ▁line ▁of ▁the ▁image ▁a ▁pair ▁. ▁The ▁ ▁plane ▁is ▁sometimes ▁referred ▁to ▁as ▁H ough ▁space ▁for ▁the ▁set ▁of ▁straight ▁lines ▁in ▁two ▁dimensions . ▁This ▁representation ▁makes ▁the ▁H ough ▁transform ▁concept ually ▁very ▁close ▁to ▁the ▁two - dimensional ▁Rad on ▁transform . ▁In ▁fact , ▁the ▁H ough ▁transform ▁is ▁mathemat ically ▁equivalent ▁to ▁the ▁Rad on ▁transform , ▁but ▁the ▁two ▁transformations ▁have ▁different ▁computational ▁interpret ations ▁tradition ally