Independent component analysis and its application to pattern recognition

被引：0

作者：

Chen, YW ^{[1
]}

机构：

[1] Univ Ryukyus, Fac Engn, Nishihara, Okinawa 9030213, Japan

来源：

KNOWLEDGE-BASED INTELLIGENT INFORMATION ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, PTS 1 AND 2 | 2001年 / 69卷

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this Paper, we present a new pattern recognition technique based on ICA (Independent Component Analysis). We use ICA to learn the basis functions of natural images and then the basis functions are used as pattern templates for feature detections. The succesful applications of the proposed method to edge detection and texture segmentation are demonstrated.

引用

页码：1243 / 1247

页数：5

共 11 条

[1]

[Anonymous], ADV NEURAL INFORM PR

[2] AN INFORMATION MAXIMIZATION APPROACH TO BLIND SEPARATION AND BLIND DECONVOLUTION [J].

BELL, AJ ;

SEJNOWSKI, TJ .

NEURAL COMPUTATION, 1995, 7 (06) :1129-1159

[3] The ''independent components'' of natural scenes are edge filters [J].

Bell, AJ ;

Sejnowski, TJ .

VISION RESEARCH, 1997, 37 (23) :3327-3338

[4]

CHEN YW, 2000, P 4 INT C KNOWL BAS, P566

[5]

Cheng YP, 2001, POWDERS AND GRAINS 2001, P69

[6]

LEE TW, UNPUB IEEE T IMAGE P

[7]

Lewicki MS, 1998, ADV NEUR IN, V10, P556

[8] Emergence of simple-cell receptive field properties by learning a sparse code for natural images [J].

Olshausen, BA ;

Field, DJ .

NATURE, 1996, 381 (6583) :607-609

[9]

RINEN AH, 1999, NEURAL COMPUTING SUR, V2, P94

[10]

ZENG XY, 2001, P 8 INT C NEUR INF P

← 1 2 →