▁corresponding ▁to ▁real ▁lines ▁in ▁the ▁image . ▁ ▁K ernel - based ▁H ough ▁transform ▁( K HT ) ▁ ▁Fern and es ▁and ▁O live ira ▁ ▁suggested ▁an ▁improved ▁voting ▁scheme ▁for ▁the ▁H ough ▁transform ▁that ▁allows ▁a ▁software ▁implementation ▁to ▁achieve ▁real - time ▁performance ▁even ▁on ▁relatively ▁large ▁images ▁( e . g ., ▁ 1 2 8 0 × 9 6 0 ). ▁The ▁K ernel - based ▁H ough ▁transform ▁uses ▁the ▁same ▁ ▁parameter ization ▁proposed ▁by ▁D uda ▁and ▁Hart ▁but ▁oper ates ▁on ▁clusters ▁of ▁approximately ▁col linear ▁pixels . ▁For ▁each ▁cluster , ▁votes ▁are ▁cast ▁using ▁an ▁orient ed ▁elli pt ical - G aussian ▁kernel ▁that ▁models ▁the ▁uncertainty ▁associated ▁with ▁the ▁best - f itting ▁line ▁with ▁respect ▁to ▁the ▁corresponding ▁cluster . ▁The ▁approach ▁not ▁only ▁significantly ▁impro ves ▁the ▁performance ▁of ▁the ▁voting ▁scheme , ▁but ▁also ▁produces ▁a ▁much ▁cleaner ▁accum ulator ▁and ▁makes ▁the ▁transform ▁more ▁robust ▁to ▁the ▁detection ▁of ▁sp ur ious ▁lines . ▁▁ 3 - D ▁K ernel - based ▁H ough ▁transform ▁for ▁plane ▁detection ▁( 3 DK HT ) ▁Lim berger ▁and ▁O live ira ▁ ▁suggested ▁a ▁determin istic ▁technique ▁for ▁plane ▁detection ▁in ▁un organ ized ▁point ▁clouds ▁whose ▁cost ▁is ▁ ▁in ▁the ▁number ▁of ▁samples , ▁ach ieving ▁real - time ▁performance ▁for ▁relatively ▁large ▁datasets ▁( up ▁to ▁ ▁points ▁on ▁a ▁ 3 . 4   G Hz ▁CPU ). ▁It ▁is