面向复杂场景自动目标检测和识别的变换域视觉注意模型研究

项目名称： 面向复杂场景自动目标检测和识别的变换域视觉注意模型研究

项目编号： No.61263048

项目类型： 地区科学基金项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 余映

作者单位： 云南大学

项目金额： 45万元

中文摘要： 面对遥感成像设备提供的大量数据，现有的数据处理技术已无法满足实际需求。而自动目标检测与识别作为从遥感图像中提取重要信息的一种有效途径，近年来格外受到重视。本项目以人的视觉注意机制为基础，探索基于变换域视觉注意模型的自动目标检测与识别的新理论和新方法。通过视觉注意方法对各种复杂场景遥感图像进行有效处理，能够快速地挑选出少数重要的候选区域进行目标识别，它将突破目前普遍采用的利用图像高层信息对全部图像区域进行逐像素加工的处理框架，从而实现工程上有效、快速的自动目标检测与识别。该项目的实现，将为解决遥感图像自动目标检测和识别的问题提供新的理论和方法。尤其是，在目前大量遥感数据缺乏有效的处理和分析工具的情况下，它可以提供一种较为通用的有效处理方法和手段。此外，在理论研究的基础上，将这些新方法与压缩感知系统相结合，通过视觉注意的引导来分配系统感知资源和计算资源，实现符合人类视觉感知特性的图像压缩感知。

中文关键词： 选择性视觉注意；视觉显著性；目标检测；目标识别；遥感图像

英文摘要： Existing technologies for data processing cannot meet practical requirements seriously, when dealing with numerous data provided by remote sensing imaging equipments. In recent years, much importance has been attached to automatic object detection and recognition that is considered as an effective way of extracting important information from remote sensing images. Inspired by the mechanism of human visual attention, this project will investigate new theories and new methods about transform domain-based visual attention model for automatic object detection and recognition. By using visual attention methods to process the remote sensing images of complex scenes effectively, we can rapidly select several important candidate regions for further object recognition. This method will break through the traditional framework that uses an image's high-level information to process the whole image region pixel by pixel. Thus it can perform automatic object detection and recognition that is effective and fast in engineering applications. If our project is accomplished, it can provide new theories and new methods for solving the problem of automatic object detection and recognition in remote sensing images. Especially, it can provide a universal method and measure in consideration of the fact that numerous remote sensing data

英文关键词： Selective Visual Attention；Visual Saliency；Object Detection；Object Recognition；Remote Sensing Image