基于视觉特性的目标检测算法研究

项目名称： 基于视觉特性的目标检测算法研究

项目编号： No.61501388

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 无线电电子学、电信技术

项目作者： 张艳邦

作者单位： 咸阳师范学院

项目金额： 19万元

中文摘要： 现有的基于视觉特性的显著性目标检测模型在背景简单、目标单一的环境下，具有较好的检测效果。但是现实中，场景的复杂性、目标的多样性以及先验知识有限等因素使得图像和视频中显著性目标的检测仍是一个挑战性的问题。本项目将选择和构造合适的图像底层特征，充分挖掘图像背景及前景的先验知识，根据显著图像素点的分布特点，提出以信息熵为基础的显著图衡量算法及特征融合算法，建立静态图像的显著性检测模型。考虑到视频中显著性特征，不仅受运动信息的影响，而且受相邻帧相关性的影响。本项目将扩展静态图像的检测模型，提出基于相邻帧显著性特征的协同显著性检测算法，采用马尔科夫模型融合所得协同显著性特征与视频的运动信息，改善在无任何先验信息的情况下，视频中显著性目标的检测效果。该项研究可为图像及视频中显著性目标的自动检测提供有效的技术途径和实现方法，具有重要的理论意义和应用前景。

中文关键词： 视觉信息处理；显著性目标；视觉注意；目标检测；协同显著性

英文摘要： The existing salient object detection models based on visual features have good detection results in the context of the simple background and single object. However, in reality, it remains a challenging problem for the complexity of scene, the diversity of objects, and the limitation of priori knowledge to detect salient objects in images and videos. We will select and construct appropriate image low-level features, and fully exploit prior knowledge of the image background and foreground. According to the distribution of saliency map pixels, an entropy-based measure algorithm and a fusion algorithm will be proposed to create a saliency detection model of static images. Since the salient features of the video are affected not only by the movement information, but also by the relativity of the adjacent frames, we will extend the detection model of static images and present a co-saliency detection algorithm based on salient features in adjacent frames. Next, the co-salient features derived from the above proposed algorithm and video motion information will be fused via Markov model features to improve the results of salient object detection in videos in the absence of any prior knowledge. The results of this project can provide effective technical approaches and implementation methods for automatic salient object detection in images and videos, which have important theoretical significance and application prospects.

英文关键词： visual information processing;salient object;visual attention;object detection;co-saliency