▁the ▁highest ▁values , ▁typically ▁by ▁looking ▁for ▁local ▁maxim a ▁in ▁the ▁accum ulator ▁space , ▁the ▁most ▁likely ▁lines ▁can ▁be ▁extracted , ▁and ▁their ▁( appro xim ate ) ▁geometric ▁definitions ▁read ▁off . ▁( Sh api ro ▁and ▁Stock man , ▁ 3 0 4 ) ▁ ▁The ▁simplest ▁way ▁of ▁finding ▁these ▁pe aks ▁is ▁by ▁applying ▁some ▁form ▁of ▁threshold , ▁but ▁other ▁techniques ▁may ▁yield ▁better ▁results ▁in ▁different ▁circumstances ▁– ▁determ ining ▁which ▁lines ▁are ▁found ▁as ▁well ▁as ▁how ▁many . ▁ ▁Since ▁the ▁lines ▁returned ▁do ▁not ▁contain ▁any ▁length ▁information , ▁it ▁is ▁often ▁necessary , ▁in ▁the ▁next ▁step , ▁to ▁find ▁which ▁parts ▁of ▁the ▁image ▁match ▁up ▁with ▁which ▁lines . ▁Moreover , ▁due ▁to ▁imper fection ▁errors ▁in ▁the ▁edge ▁detection ▁step , ▁there ▁will ▁usually ▁be ▁errors ▁in ▁the ▁accum ulator ▁space , ▁which ▁may ▁make ▁it ▁non - t rivial ▁to ▁find ▁the ▁appropriate ▁pe aks , ▁and ▁thus ▁the ▁appropriate ▁lines . ▁ ▁The ▁final ▁result ▁of ▁the ▁linear ▁H ough ▁transform ▁is ▁a ▁two - dimensional ▁array ▁( matrix ) ▁similar ▁to ▁the ▁accum ulator — one ▁dimension ▁of ▁this ▁matrix ▁is ▁the ▁quant ized ▁angle ▁ θ ▁and ▁the ▁other ▁dimension ▁is ▁the ▁quant ized ▁distance ▁r . ▁Each ▁element ▁of ▁the ▁matrix ▁has ▁a ▁value ▁equal ▁to ▁the ▁sum ▁of ▁the ▁points ▁or ▁pixels ▁that ▁are ▁position ed ▁on ▁the ▁line ▁represented ▁by ▁quant ized ▁parameters ▁( r , ▁ θ ).