FREE TRAINING OBJECT DETECTION BASED ON MULTI-STAGE FUSION USING BELIEF FUNCTIONS

被引：0

作者：

Farhat, Mariem ^{[1
]}

Mhiri, Slim ^{[1
]}

Tagina, Moncef ^{[2
]}

机构：

[1] Natl Sch Comp Sci, GRIFT Res Grp, Cristal Lab, Manouba, Tunisia

[2] Natl Sch Comp Sci, Manouba, Tunisia

来源：

2016 INTERNATIONAL SYMPOSIUM ON SIGNAL, IMAGE, VIDEO AND COMMUNICATIONS (ISIVC) | 2016年

关键词：

Specific object detection; multi-stage fusion; Dempster theory;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Many of object detection methods are based on training phase. Theses methods are constrained to a known object. In this paper, we present a free training method for object detection that can deal with large viewpoint change. We exploit Dempster theory to combine between multiple descriptors in a multi stage method. To show the effectiveness of the technique, we apply it on multiple images from Coil100 database. Compared to existing methods, our method has proved to be more generic and more efficient.

引用

页码：153 / 158

页数：6

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