A Connectionist Model for Corner Detection in Binary and Gray Images

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一种类神经网络模型Connectionist Model的角点检测方法,非常有意思,里面的方法融合多尺度及人工神经网络的思想,如果想大概了解下其内容,可以参考我的博客。
Ty Type 1 N() Type 2 Type 2 dcs for pixel i 9(;) Nodes for pixel j m g(x)={-m <-m 72 {1,l2,k1k2}∈{-1,02+1} O t→∞ ∑( V咛+辟√+楼 1+b h b /+√l+k wij=Uji=U17 U8=whT w2 ∑∑ ∑ 2∑∑m+2m∑ ∈N( 丿E/G de de wsC ∑9-1(a) ∈N( B -m<化< m2-m)<?<m da 方=∑ d N() (dca: dt)2(dcv: /dt dE/tt≤0t≥0E(t) dE/tt→0 t→○ der;/tt→0d/t→0t C dt 27 T比 ∈N() ?1 d duu ∑(an ∈N() (1+bt) B B ∑(n) B=(2k)/b <1 tn)s 1+itm=k a2 B≥log g? i +ot2 1+bt d t+log log u; (t=A(l B log(1+bt)+lo 6 40≤ og hi/ log 770(<1) (Oh;/)=0 心0 1+ g a B B> ()1=(4/B)=e四(41~B log(1/ B log +log uo log k B r/b 12 ()1+=A/B≤m log(1/) B 4(1-4)-B1g(4B)+l20slgm b∝ k log h m A- B log A≤B-BlgB+lg L0 1+bt= k 6.0=5.0 1+e 0.25 lax B=0.708 4 k m 0.05 0.071 b=0.5 A=kB Bk-B log( bk)<B-B log(B)+ B≤,ha(m/no) og log(uo/ 0) log(m/lo log k-h 1-log h g 0(;)cy;=9(vy;) 26 E=-∑∑m ∑ i∈N() 2点%+2 (a1+2*24+26)-(23+2*+ 3 strength1=(1+2米2+23)-(26+2米+28) △t ∑ 同;|sin;nz f∈N() k m C b 1o 6 JicAi eie dt yeiei-l0sc ∈N() k=3b=0.5 l0=0.25b=0.08t1=0.139m12=0.173 k=5b=110=0.26=0.05t1=0.0889=0.172 k=5b=1 u=0.256=0.08m(1=0.100t2=0.141 k=10b=1a0=0.1=0.0351=0.0252=0.045 k=10b 0=0.16=0.05u1=0.039a2=0.042 0.2ao=0.1e=0.05t1=0.02022=0.030 k=12b=110=0.099=0.05i?1=0.017tx2=0.020 k=12b=10=0.0066=0.03t1=0.0122=0.023 k=5b=0.20=0.56=0.25t1=0.0152=0.034 k=5b=10=0.56=0.25m1=0.0770=0.034 246-0.1i1-0.022 b=1u0 246=0.1t(1=0.022 02=0.088 2-0.088 A ,… k=5b=0.2t0=0.5e=0.25u1=0.0152=0.034 k=9b=1o=0.1b=0.041=0.021 0.093

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试读 9P A Connectionist Model for Corner Detection in Binary and Gray Images
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