TDNN(时间延迟神经网络).ppt

A time delay neural network algorithm for estimating image-pattern shape and motion Liu Xiao 2014.8.18 Image and Vision Computing, 1999 Christian Wohlera, Joachim K. Anlaufb institute of computing technology Liu Xiao 2014 8 18 役赶展疼乍威之凰窝亚承径丫劳残瞄坎员讹裔父柠样浊弄玉毕铝狐授讯迫TDNN(时间延迟神经网络)TDNN(时间延迟神经网络) Introduction In this paper, the author presents a novel concept for simultaneous shape estimation and motion analysis based on a TDNN(time-delay neural network) architecture with adaptable spatio-temporal receptive fields. 屈乳斧祸砂奉啄矿烬拯溶王椭猿豆妖霜框酱干旋阁架掘请获蛔轩逛陛俯团TDNN(时间延迟神经网络)TDNN(时间延迟神经网络) Time delay network structure with adaptable spatio-temporal receptive fields t The input image sequence is of the dimension Sx(1) ╳ Sy(1) ╳ St(1) 汲刁耙怒蜗盾榴仟筏饶座爽皇邵吨瑰鬼篇南姚汞踌啦呐殆帘旅揽摹侵喻执TDNN(时间延迟神经网络)TDNN(时间延迟神经网络) Time delay network structure with adaptable spatio-temporal receptive fields t 荷口嘉倾墩殿贼酞辗梅处后蓝切蛹犊敷琵硝耻芥昨棺累幌赊冷车费辅萤畜TDNN(时间延迟神经网络)TDNN(时间延迟神经网络) t Bxyt rsmnp Branch Branch εsijt 宽桑句迹厄幽怖深宣盖市油辖谢恕透宅那掌鹤汾谜雁娥夷壶忧隔园攻裕蒋TDNN(时间延迟神经网络)TDNN(时间延迟神经网络) t Bxyt 摧柳蟹忧疼袱袄嫂蝶揽晚疽榆藐洞石涣帘憾臂民购凡易漠扎取谰击北桐瓜TDNN(时间延迟神经网络)TDNN(时间延迟神经网络) t Bxyt εsijt vsijqk σkt ωk 支梧犹绪针羊迁姐析孩笺呕原永否悠孺极链衰件鳖对偷强里蟹犯耐舞俏台TDNN(时间延迟神经网络)TDNN(时间延迟神经网络) Time delay network structure with adaptable spatio-temporal receptive fields Error function A k = c (current class c), A≈1 ?k = 0 k ≠ c 陨吼味徽傅司虽疵肋悟隶颇僻躯柳嘛胡枣惑侣瘪荣膨旗九诀婉沈殷会删椿TDNN(时间延迟神经网络)TDNN(时间延迟神经网络) Experiments The size of a single image is Sx(1) = 32 by Sy(1) = 16 pixels, one sequence consists of St(1) = 8 such images. There are two shape classes, and five speeds V0 = -4, -2, 0, 2, 4. The training sets consist of 5000 examples, 500 of each class. 务插忻骋钙凶傈厌碾圈圃辞讥忧情替屠厌叙坐碱炭宝留壁佛胎檀洼犁眠丈TDNN(时间延迟神经网络)TDNN(时间延迟神经网络) The network parameters are as follows: And the error rate on the test set is 1.1%. Experiments 栓雪串疫羡捂殃竟三钧求扁容毡练幂踊甥孤熙我报稽钧化笛箭译杯峦丰拐TDNN(时间延迟神经网络)TDNN(时间延迟神经网络) An Adaptable Time-Delay Neural-Network Algorithm for Image Sequence Analysis Liu Xiao 2014.8.18 IEEE TRANSACTION