Performance evaluation of image enhancement methods for object detection and recognition
文献类型：会议论文
| 作者 | Cai TF(蔡铁峰) ; Zhu F(朱枫); Hao YM(郝颖明); Fan XP(范晓鹏)
|
| 出版日期 | 2015 |
| 会议名称 | Conference on Applied Optics and Photonics (AOPC) - Image Processing and Analysis |
| 会议日期 | MAY 05-07, 2015 |
| 会议地点 | Beijing |
| 关键词 | contrast enhancement objects detection and recognition performance evaluation |
| 页码 | 1-5 |
| 中文摘要 | Human eyes cannot notice low contrast objects in the image. Image contrast enhancement methods can make the unnoticed objects noticed, and human can detect and recognize the objects. In order to guide the design of enhancement methods, performance of enhancement methods for objects detection and recognition(ODR) should be valued. The existing performance evaluation methods evaluate image enhancement methods by calculating the increment of contrast or image information entropy. However, it is essentially an image information transmission process that human detect and recognize objects in the image, and image contrast enhancement can be viewed as a form of image coding. According to human visual properties, the transmission process of ODR information are modeled in this paper, and a performance evaluation method was proposed from the information theory of Shannon. |
| 收录类别 | CPCI（ISTP） |
| 产权排序 | 1 |
| 会议主办者 | Chinese Soc Opt Engn, Chinese Soc Astronaut, Photoelectron Technol Comm, Sci & Technol Low Light Level Night Vis Lab, SPIE, Opt Soc, European Opt Soc, Opt Soc Korea |
| 会议录 | Proceedings of SPIE
 |
| 会议录出版者 | SPIE-INT SOC OPTICAL ENGINEERING |
| 会议录出版地 | BELLINGHAM, WA |
| 语种 | 英语 |
| ISSN号 | 0277-786X |
| ISBN号 | 978-1-62841-900-9 |
| WOS记录号 | WOS:000363280200107 |
| 源URL | [http://ir.sia.cn/handle/173321/17200]  |
| 专题 | 沈阳自动化研究所_光电信息技术研究室 |
| 推荐引用方式 GB/T 7714 | Cai TF,Zhu F,Hao YM,et al. Performance evaluation of image enhancement methods for object detection and recognition[C]. 见:Conference on Applied Optics and Photonics (AOPC) - Image Processing and Analysis. Beijing. MAY 05-07, 2015. |
入库方式： OAI收割
来源：沈阳自动化研究所
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