DISTORTION INVARIANT OBJECT RECOGNITION IN THE DYNAMIC LINK ARCHITECTURE

被引：1053

作者：

LADES, M ^{[1
]}

VORBRUGGEN, JC ^{[1
]}

BUHMANN, J ^{[1
]}

LANGE, J ^{[1
]}

VANDERMALSBURG, C ^{[1
]}

WURTZ, RP ^{[1
]}

KONEN, W ^{[1
]}

机构：

[1] UNIV BONN,INST INFORMAT 2,W-5300 BONN,GERMANY

来源：

IEEE TRANSACTIONS ON COMPUTERS | 1993年 / 42卷 / 03期

关键词：

COMPUTER VISION; DISTORTION INVARIANCE; DYNAMIC LINK ARCHITECTURE; ELASTIC GRAPH MATCHING; OBJECT RECOGNITION; NEURAL NETWORK; WAVELET;

D O I：

10.1109/12.210173

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present an object recognition system based on the Dynamic Link Architecture, which is an extension to classical Artificial Neural Networks. The Dynamic Link Architecture exploits correlations in the fine-scale temporal structure of cellular signals in order to group neurons dynamically into higher-order entities. These entities represent a very rich structure and can code for high level objects. In order to demonstrate the capabilities of the Dynamic Link Architecture we implemented a program that can recognize human faces and other objects from video images. Memorized objects are represented by sparse graphs, whose vertices are labeled by a multi-resolution description in terms of a local power spectrum, and whose edges are labeled by geometrical distance vectors. Object recognition can be formulated as elastic graph matching, which is performed here by stochastic optimization of a matching cost function. Our implementation on a transputer network successfully achieves recognition of human faces and office objects from gray level camera images. The performance of the program is evaluated by a statistical analysis of recognition results from a portrait gallery comprising images of 87 persons.

引用

页码：300 / 311

页数：12

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